USF Libraries
USF Digital Collections

Two essays on financial condition of firms

MISSING IMAGE

Material Information

Title:
Two essays on financial condition of firms
Physical Description:
Book
Language:
English
Creator:
Kudrimoti, Sanjay
Publisher:
University of South Florida
Place of Publication:
Tampa, Fla
Publication Date:

Subjects

Subjects / Keywords:
Balance sheet liquidity
Growth opportunities
Financial slack
Financial constraints
Performance decline
Dissertations, Academic -- Finance -- Doctoral -- USF   ( lcsh )
Genre:
non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: This dissertation includes two related chapters that analyze financial condition of firms. In the first chapter, I examine the relationship between the firms' level of cash holdings and governance. The findings show that higher levels of cash holdings are significantly related to strong governance. The results also show that firms with strong governance hold asymmetrically higher levels of cash than firms with weak governance when they have high growth opportunities. Furthermore, I also test the impact of financial constraint status of the firm on the level of cash holdings for both good and poorly governed firms separately. The results suggest that strong governance firms hold higher levels of cash to use as financial slack in order to avoid financial distress. In the second essay I examine if a firm's success in leaving distress is explained by firm characteristics and manager decisions. I proxy the managers' decisions by measuring changes in operating, investing, and financing choice variables. Timely decisions with regard to product refinement, proxied by increased investment in research and development and reduction in capital expenditures, increase the probability of successful turnaround. Further the results show that increased financing through additional sale of equity, acquisitions and sale of assets do not help a firm exit financial distress.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2008.
Bibliography:
Includes bibliographical references.
System Details:
Mode of access: World Wide Web.
System Details:
System requirements: World Wide Web browser and PDF reader.
Statement of Responsibility:
by Sanjay Kudrimoti.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 88 pages.
General Note:
Includes vita.

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
aleph - 002006951
oclc - 399609508
usfldc doi - E14-SFE0002734
usfldc handle - e14.2734
System ID:
SFS0027051:00001


This item is only available as the following downloads:


Full Text
xml version 1.0 encoding UTF-8 standalone no
record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd
leader nam Ka
controlfield tag 001 002006951
003 fts
005 20090615092919.0
006 m||||e|||d||||||||
007 cr mnu|||uuuuu
008 090615s2008 flu s 000 0 eng d
datafield ind1 8 ind2 024
subfield code a E14-SFE0002734
035
(OCoLC)399609508
040
FHM
c FHM
049
FHMM
090
HG173 (Online)
1 100
Kudrimoti, Sanjay.
0 245
Two essays on financial condition of firms
h [electronic resource] /
by Sanjay Kudrimoti.
260
[Tampa, Fla] :
b University of South Florida,
2008.
500
Title from PDF of title page.
Document formatted into pages; contains 88 pages.
Includes vita.
502
Dissertation (Ph.D.)--University of South Florida, 2008.
504
Includes bibliographical references.
516
Text (Electronic dissertation) in PDF format.
3 520
ABSTRACT: This dissertation includes two related chapters that analyze financial condition of firms. In the first chapter, I examine the relationship between the firms' level of cash holdings and governance. The findings show that higher levels of cash holdings are significantly related to strong governance. The results also show that firms with strong governance hold asymmetrically higher levels of cash than firms with weak governance when they have high growth opportunities. Furthermore, I also test the impact of financial constraint status of the firm on the level of cash holdings for both good and poorly governed firms separately. The results suggest that strong governance firms hold higher levels of cash to use as financial slack in order to avoid financial distress. In the second essay I examine if a firm's success in leaving distress is explained by firm characteristics and manager decisions. I proxy the managers' decisions by measuring changes in operating, investing, and financing choice variables. Timely decisions with regard to product refinement, proxied by increased investment in research and development and reduction in capital expenditures, increase the probability of successful turnaround. Further the results show that increased financing through additional sale of equity, acquisitions and sale of assets do not help a firm exit financial distress.
538
Mode of access: World Wide Web.
System requirements: World Wide Web browser and PDF reader.
590
Advisor: Ninon Sutton, Ph.D.
653
Balance sheet liquidity
Growth opportunities
Financial slack
Financial constraints
Performance decline
690
Dissertations, Academic
z USF
x Finance
Doctoral.
773
t USF Electronic Theses and Dissertations.
4 856
u http://digital.lib.usf.edu/?e14.2734



PAGE 1

Two Essays on Financial Condition of Firms by Sanjay Kudrimoti A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Finance College of Business Administration University of South Florida Major Professor: Ninon Sutton, Ph.D. Scott Besley, Ph.D. Christos Pantzalis, Ph.D. Jianping Qi, Ph.D. Date of Approval: September 30, 2008 Key Words: Balance Sheet Liquidity, Growth Opportunities, Financ ial Slack, Financial Constraints, Performanc e Decline, Turnaround Copyright 2008, Sanjay Kudrimoti

PAGE 2

i Table of Contents List of Tables iii Abstract v Essay 1 Is the Level of Cash Holdings In fluenced by Corporate Governance? 1 1.1 Introduction 1 1.2 Related Literature 7 1.2.1 Costly External Financing 7 1.2.2 Costs and Benefits of Liquid Asset Holdings 8 1.2.3 Investments a nd Financing Constraints 9 1.2.4 Corporate Governan ce Impacting Corporate Finance 11 1.3 Sample Construction 12 1.3.1 Measure of Governance Characteristics 12 1.3.2 Measure of Financial Constraints 14 1.3.3 Measure of Financial Distress 15 1.3.4 The Sample Selection Process 16 1.3.5 Variable Description 19 1.4 Empirical Results 20 1.4.1 Summary Statistics 20 1.4.2 Univariate Tests 22 1.4.3 Multivariate Tests 31 1.5 Conclusions 47 Essay 2 Do Management Decisions Matter when Fi rms are in Distress? 49 2.1 Introduction 49 2.2 Background: Firms Exiting Financial Distress 54 2.3 Related Literature 54 2.3.1 Financial Distress 55 2.3.2 Management Strategy 56 2.3.3 Turnarounds 57 2.4 The Sample and Variables 61 2.4.1 The Sample Selection Process 61 2.4.2 Control Variables 62 2.4.3 Test Variables 63 2.5 Measures of Financial Distress 64 2.6 Empirical Results 70 2.6.1 Sample Statistics and Univariate Tests 70 2.6.2 Multivariate Tests 74

PAGE 3

ii 2.7 Conclusions 82 References 83 About the Author End Page

PAGE 4

iii List of Tables Table 1 Variab les Definition and Construction 17 Table 2 Descriptive Statistics 21 Table 3 Sample sort: Cash Holdings by Governance over Time 23 Table 4 Correlation Matrix 24 Table 5 Panel A: Univariate Test (Cash Holdings vs. Governance Measures) 26 Panel B: Un ivariate Test (Cash Holdings vs. Test Variables) 27 Table 6 Panel A: Ca sh Holdings by Industry and Governance 29 Panel B: Cash Holdings by Industry and Growth Opportunities 30 Table 7 Multivariate Tests: Impact of Governance on Cash Holdings 33 Table 8 Panel A: Cash Hold ings and Governance Using G Index – Double Sort 35 Panel B: Cash Holdings and Governance Using E Index – Double Sort 36 Table 9 Multivariate Tests: Sample Differentiated by Growth Opportunities 38 Table 10 Multivariate Tests: Sample Differentiated by Financial Status and Governance 40 Table 11 Robustness Test s: Time and Industry Variation 42 Table 12 Robustness Tests: Using R& D/Total Assets as a Proxy Measure for the Growth Opportunities in place of Q 44 Table 13 Robustness Tests: Te sting with Lag Variables 45

PAGE 5

iv Table 14 Variable Construction 64 Table 15 Panel A: Non-Distressed vs. Distressed Firms – Summary Statistics 67 Panel B: Non-Distressed vs Distressed Firms –Industry Concentration 69 Table 16 Sample Statistics 71 Table 17 Correlation Matrix 73 Table 18 Multivariate Tests: Probit Analysis 76 Table 19 Multivariate Tests: Sample Differentia ted by Growth Opportunities 78 Table 20 Panel A: Comparison of 3year returns for the two Samples Over Time 80 Panel B: Comparison of 3-year Abnormal Returns for the two Samples Over Time 81

PAGE 6

v Two Essays on Financial Condition of Firms Sanjay Kudrimoti ABSTRACT This dissertation incl udes two related chapters that analyze financial condition of firms. In the first chapter, I examine the relationship between the firms’ level of cash holdings and governance. The findings show th at higher levels of cash holdings are significantly related to strong governance. The results also show that firms with strong governance hold asymmetrically higher levels of cash than firms with weak governance when they have high growth opportunities. Fu rthermore, I also test the impact of financial constraint status of the firm on the level of cash holdings for both good and poorly governed firms separately. The results suggest that strong governance firms hold higher levels of cash to use as financial slack in order to a void financial distress. In the second essay I examine if a firm’s success in leaving distress is explained by firm characteristics and manager decisions. I proxy the managers’ decisions by measuring changes in operating, investing, and financi ng choice variables. Ti mely decisions with regard to product refinement, proxied by increased investment in research and development and reduction in capital expenditu res, increase the probability of successful turnaround. Further the results show that incr eased financing through additional sale of equity, acquisitions and sale of assets do not help a firm exit financial distress.

PAGE 7

1 Essay 1 Is the Level of Cash Holdings Influenced by Corporate Governance? 1.1 Introduction Corporate liquidity enables firms to ma ke investments when opportunities arise without the need to access external capital markets (K eynes, 1936), thereby avoiding transactions costs and the cost s of information asymmetries of ten associated with equity issuances. On the contrary, the agency ar gument against padding up cash states that additional cash in the hands of managers will be misused (Jensen, 1986). Typically, the intuitive notion is agency costs are real and it would be more valuable to put cash into use making a positive return instead of keeping fo r manager discretion. Given this idea it is surprising to see firms increasing their cash hol dings as a percentage of total assets on average in recent times (Ditmar and Mahr t-Smith, 2007). I examine cash holdings to determine if it is driven by firm interest or manager interest. In other words, is the increase in cash holdings a ma nifestation of agency costs or manager identification of a new effective use of cash? To determine if increased cash holdings are pro-firm or anti-firm I turn to firm governance. All else equal, firms with better governance should act mo re in the interest of shareholders than firms with poor govern ance. In the same vein, managers who are less entrenched should act more in line with th e interests of shareholders as compared to firms with deeply entrench ed managers. Given the ability of governance to force

PAGE 8

2 managers to act in the best interests of shareholders, if firm governance is positively related to cash holdings then the recent increase in cash holdings may be a positive development. On the other hand, if firm governance is negatively related to cash holdings then the trend of increased cash holdings may be a reflection of agency problems. In this paper, I attempt to address the following questions. Do firms with strong or poor governance hold more cash, and if so, why does governance influence cash holdings? Managers who maximize shareholder wealth should set the firm’s cash holdings at a level where the marginal benefit of cash holdings equals the marginal cost (Opler et al., 1999). The potential benefits of holding mo re cash are found to be increased financial slack and lower risk with higher liquidity. Fi nancial slack and higher liquidity will allow firms to take advantage of more opport unities (Myers and Majluf, 1984) and avoid financial troubles. The costs of holding liquid assets are lower returns, higher taxation on the interest, and perhaps the most dangerous is an increase in agency costs (Jensen (1986); Stulz (1990)). Available funds provide ma nagers the ability to invest in projects providing pecuniary benefits, t hus increasing agency costs. Increased agency costs have been shown to affect firm performance adversely. Given the tradeoff between the benefits a nd costs of holding cash, I find evidence that the higher levels of cas h holdings are not, on average, associated with higher agency problems. The results illustrate increased cash holdings are positively related with governance, as firms with strong governance hold significantly more cash as a percentage of assets. I find this result to be robust to two different measures of firm governance, Gompers, et al (2003) Governance (G-i ndex) and the Bebchuck, et al. (2005) Entrenchment index (E-index). I also find this difference to be robust after using

PAGE 9

3 numerous controls to measure the differe nces between firms with strong and poor governance, including documented performance differences by controlling for firm cash flows. Firms with better govern ance are able to reduce the marg inal cost of carrying cash, thus increasing the value of cash. The results of this paper are consistent with the value of cash when in the hands of strong (well governed ) managers. Next, I turn my attention to why do firms with strong governance hold more cash? To explain the large difference in cash holdings between firms with strong versus poor governance, I hypothesize that firms with strong governance will increase their cash holdings when it makes sense to do so. Firms with strong governance will take advantage of the benefits of holding cash such as in creased financial slack during times of higher growth opportunities and will increase cash when free of financial constraint. The results are consistent with this idea as they show firms with str ong governance hold higher levels of cash when they have good investment opport unities. While the findings show that both well and poorly governed firms hold larger perc entages of cash when they have increased growth opportunities, firms with strong gove rnance hold asymmetrically higher amounts than firms with poor governance. The result s also demonstrate that firms with strong governance do not significantly increase thei r cash holdings when they do not have strong growth opportunities. Anot her potential reason for higher levels of cash holdings by strong governance firms is to build up slack in order to avoid financial pitfalls (Lamont, 1997), classified in this paper as fi nancial constraint or distress. I find strong governance firms invariably vary the level of cash holdings in times of financial constraint while poor governance firms appear to use up cash and hold low levels of cash holdings, whether they are financ ially constrained or not.

PAGE 10

4 This paper adds to the curre nt literature in two differe nt areas. The first area looks at the importance and value of cash to fi rms (Pinkowitz and W illiamson, 2007). I build on the idea of Opler, Pinkowitz, Stulz, and W illiamson (1999). They show that managers maximize shareholder wealth by setting the firm ’s cash holdings at a level such that the marginal benefit of cash holdings equals the ma rginal cost of those holdings. I show firm governance can mitigate agency issues and increase the value of cash holdings, thus offering an explanation for the observed tre nd of higher cash holdi ngs seen in recent times. I also add to the literature empha sizing the importance of corporate governance and agency issues in the workings of modern corporations. Prior research focuses on the impact of governance mechanisms on (i) firm performance (Hermalin and Weisbach, 1991, Bhagat and Black, 2002 and Gompers, Is hii and Metrick, 2003), (ii) acquisition activity (Byrd and Hickman, 1992) (iii) over investment of free-cash flow (Richardson, 2006), (iv) diversification di scount (Jiraporn, Kim and Davi dson, 2006) and (v) write-offs (Minnick, 2004). I add to this literature by investigating how the quality of corporate governance can influence a firm’s level of cas h holdings. The results of this paper are consistent with the work of Gompers, Ishii, and Metric (2003) and provide some potential insight as to how firms with strong governance can lead to increases in stock returns. I also add another side to the results of Dittmar and Mahrt-Smith (2007). Their study focuses on the value effects of govern ance on cash resources by analyzing how a change in cash holdings leads to a change in the market valuation of a firm. They find a positive and significant difference in the change in valuation of firm under the influence

PAGE 11

5 of a strong governance policy. Th ey also value the excess cash1 for poor and well governed firms and find that well governed firms have double the value of cash as compared to poorly governed firms. Based on these findings, they i llustrate governance as having a relatively minor impact on how firms accumulate cash, but a significant impact on how firms spend their money. Harford, et al. (2008) also study how agen cy problems affect cash holdings of firms. Their study primarily comments on the behavior and policy issues observed for poor governance firms with respect to the use of excess cash holdings. In analyzing the differences in cash holdings of strong and poorly governed firms, they focus on the investment behavior and pay out policies of their sample firm s. Their results show that firms with higher levels of excess ca sh and poor governance increase capital expenditures, increase acquisition activity a nd disburse excess cash to shareholders as share repurchases, thus exhibiting a less co mmitment behavior. Firms with excess cash and good governance disburse cash to shareholde rs by initiating or increasing dividends. While Harford et al. (2008) address how firms with poor governance end up with lower levels of cash holdings, my paper attempts to address why firms with different governance structures tend to hold different levels of cash holdings by analyzing the impact of growth opportunities of firms. Consistent with Harford et al. (2008), my initial results show that good governance firms w ith better growth o pportunities hold higher levels of cash holdings. I examine the role of growth opportunities further by separating the sample into groups of high, average a nd low growth firms (Growth opportunities 1 Dittmar and Mahrt-Smith define excess cash as cas h reserves held in excess of those needed for operations and investments.

PAGE 12

6 proxied by Q). I run my tests on these three sub samples and find that only well governed firms with high and average growth opportuni ties tend to hold higher levels of cash holdings. Good governance firms with low Q valu es do not significantly hold higher cash holdings. This analysis provides new eviden ce on the interaction of firm growth and governance in influencing cash holdings. Anothe r important distinction this paper has from the Harford et al. paper is about the expl anation of the role of financial constraints for good and poor governance firms with respect to the cash holdings. After showing that financially unconstrained firm s hold higher levels of cash holdings on average, I further explore the role of financial constraint s and governance on firm’s cash holdings. The results show that firm governance influe nces cash holdings only for financially unconstrained firms. Further tests reveal that financially unconstrained and well governed firms hold higher level of cash holdings as compared to well governed and financially constrained firms. Firms with poor govern ance hold lower cash holdings, whether they are financially constrained or not. That is, this study identifi es situations where strong governance firms hold more cash and poses ex planations for the significant difference between the average cash holding of firms with strong versus weak governance. The next section reviews the related litera ture and section III addresses the issues with sample construction. Section IV co mments on the observations made on summary statistics, and discusses the results of the em pirical tests and robustn ess tests and section V concludes.

PAGE 13

7 1.2 Related Literature Managers and shareholders view the cost s and benefits of liquid asset holdings differently. Managers have greater preference for cash, because it reduces firm risk and increases their discretion. Ople r et al. (1999) state that “ As long as there is any cost to holding cash, a firm that simply accumulates cash will at some point have an excessive amount of cash, and shareholders would be bette r off if the firm used that cash to pay additional dividends or to repurchase shares. ” Analysis of investment decisions of firms occupies a prominent place in research pr ograms in economics and corporate finance. Starting with Modigliani and Miller (1958) a vast amount of finance literature focuses on the pace and pattern of busine ss investment in fixed capital. This paper belongs to the subset of this literature that treats cash holdi ngs as investment in cash asset. Asymmetric information2 raises complications concerning the optimal choice of the financing method and the appropriate discount rate to use in present value calculations when evaluating investments. Investment expenditures in fixed capital and net working capital reduce dependence on external financing in presence of higher levels of cash holdings by firms. This analysis lends support for the argument that the level of cash holdings of a firm helps determine both the future growth and its ability to sustain downturns. 1.2.1 Costly External Financing An important insight, due to My ers and Majluf (1984), Myers (1984) and Greenwald, and Stiglitz and Weiss (1984), is th at raising equity externally will generally be problematic due to an adve rse-selection problem of the so rt first identified by Akerlof 2 Asymmetric information problems in capital markets: Greenwald, Stiglitz, and Weiss (1984), Myers and Majluf (1984), Myers (1984), et al.

PAGE 14

8 (1970). Of course, an inability to access new equity would not compromise investment if firms could frictionlessly raise unlimited amou nts of debt financing. However, a variety of theories suggest that this is unlikely to be the case. Stiglitz and Weiss (1981, 1983) and others, show that the same adverse-sel ection problem can lead to credit rationing, whereby firms are simply unable to obtain all the debt financing they would like at the prevailing market interest ra te. Myers (1977), examines the impact of conflicts between firms’ claimholders on their i nvestment decisions leading to debt ‘overhang’ and hence underinvestment. Thus, cash reserves provide be nefits to equity holders by reducing the underinvestment problem. Managers wishing to avoid the costs associ ated with external financing in an imperfect information environment find it optimal to maintain sufficient internal financial flexibil ity to allow them to reduce the underinvestment problem. Further, since the equity holders suffer th e loss from underinvestment, they find it value increasing for managers to maintain the buffer stock of cash. 1.2.2 Costs and Benefits of Liquid Asset Holdings Chudson (1945) suggests that cash-to-assets ratios tend to vary systematically by industry, and tend to be higher among profitabl e companies. Vogel and Maddala (1967) show that cash balances dec lined over the time frame they ex amined, especially for larger firms. The more recent research papers have focused on the corporate actions resulting from high liquid asset holdings. Baskin (1987) highlights that firms use cash holdings for competitive purposes and Harford (1999) shows that cash-rich firms are more likely to make acquisitions. Opler et al (1999) analyze the benefits of liquid assets holdings under two different motivations. (i) Transaction cost motive – a firm saves transaction costs to raise funds and does not have to liquidate asse ts to make payments, and (ii) Precautionary

PAGE 15

9 motive – firm can use the liquid assets to fi nance its activities and investments if other sources of funding are not available or are excessively costly. R ecent literature has focused on the relation between cash holdings and its impact on the value of firm. Faulkender and Wang (2006) argue that the value of one additional dollar of cash reserves varies with its intende d use. They analyze three spec ific uses: (1) paying back to shareholders in the form of dividends, (2) capital spending an d (3) repaying debt or other obligations. Dittmar and Mahrt-Smith (2007) explore this issue further by asking “How does corporate governance impact the value of the firm and eventual use of cash reserves?” They document value destruct ion and performance declines of poorly governed, cash rich firms. Similarly Harfor d et al (2008) analyze how agency problems affect the propensity to stockpile cash in the US. They primarily find that poorly governed firms dissipate cash more quickly eith er by increasing investments, acquisition activity or exercising a payout policy of stock repurchases. My paper results concur with earlier literature and additionally I focus on the motivation issues behind the reasons for higher cash holdings by good governance firms. Th is paper adds to this line of literature, by extending the discussion to include argumen ts for explaining the circumstances as to when it is appropriate for good governance firms to hold higher level of cash holdings 1.2.3 Investments and Financing Constraints A common way of examining the impact of financial constraints in firms’ investment choices empirically was pioneered by Fazzari, Hubbard and Petersen (1988). Using a-priori criterion that re lates to the gap between the co sts of external financing and available internal funds, firms are categorized into classes of moreor less financially constrained. Hoshi, Kashyap, and Scharfstein (1991) estimate the investment-cash flow

PAGE 16

10 sensitivities of Japanese companies and fi nd that firms, which are associated with keiretsu3 groups, have significantly lower sensit ivities. Whited (1992) uses a financial constraint premise in that small firms with low liquid asset positions have limited access to debt markets, because they lack collat eral necessary to back up their borrowing. Her study finds the exogenous finance constraint to be particular ly binding for the constrained group of firms. Gilchrist and Himmelberg (1995) fi nd that firms with access to commercial paper and bond markets plan thei r investments independent of firm’s cash flows for the period. However, for firms with only limited access to capital markets (as indicated by lack of participat ion in public debt markets), investment in the firm tends to be ‘excessively’ sensitive to fluctuations in cash flow. Lamont (1997) shows that firms faced with a cash flow shock in their core business reduce investment in core and noncore segments. These and other studies found that the association between investment and cash flow is higher for firms that are e xpected to be more fi nancially constrained according to various a-priori criteria. The consensus regarding the positive re lation between the degree of financial constraints and investment-cash flow sensitiv ity was disturbed by the influential work of Kaplan and Zingales (1997). Th ey show that the theoretica l relation between the degree of financial constraints and the sensitivity of investment to ca sh flow does not have to be uniformly positive. Kaplan and Zingales s upport their argument empirically by applying subjective criteria to identify financially constrained and unconstrained firms, and demonstrating that firms that are less financ ially constrained exhibi t significantly higher investment-cash flow sensitivities than those th at appear more constrained. Their findings 3 Keiretsu institution coordinates the activities of member firms and finances much of their investment activity

PAGE 17

11 are supported by Cleary (1999), who uses large sa mples of U.S. and international firms, and reports results that are consistent with Kaplan and Zingales’ findings. 1.2.4 Corporate Governance Impac ting Corporate Finance Richardson (2006) examines whether firms’ governance structures are associated with over-investment of free cas h flow. Prior literature argues that agency conflicts arise when firms have free cash flow (e.g., Je nsen, 1986, 1993 and Stulz, 1990). Richardson finds little systematic evidence that governance structures ar e determined in response to the severity of these agency costs; however, he finds evidence that governance structures mitigate over-investment. Gompers, Ishii a nd Metrick (2003) retrace the shift in governance structure by analyzi ng the takeover market since the advent of junk bonds in 1980s. They argue that the rise of junk bond market enabled hostiletakeover offers for even the largest of public firms, in response to which many firms added takeover defenses and other restrictions of shareholde r rights. They also note that during the same time period, many states passed antitakeover la ws giving firms further defenses against hostile bids. They combine a large set of governance provisi ons into an index which proxies for the strength of shareholder righ ts, and then study the empirical relationship between this index and corporate performan ce. They find that firms with stronger shareholder rights had higher firm value, higher profits, higher sales growth, lower capital expenditures, and made fewer corporate acquisitions. Jiraporn, Kim and Davidson ( 2006) explore the agency theory as an explanation for the diversification discount. They empi rically examine the potential connections between corporate governance, shareholder right s, firm value, and the propensity for a firm to be diversified. The governance inde x developed by Gompers, Ishii, and Metrick

PAGE 18

12 (2003) is employed as the measure of streng th of shareholder rights. Their empirical studies reveal that firms in which sharehol der rights are more s uppressed by restrictive corporate governance suffer a deep er diversification discount. 1.3 Sample Construction Before addressing the core issues relate d to data, I first detail information regarding the construction of the index us ed for governance measures. Next I provide information regarding the construction of the KZ index used for distinguishing firms as financially constrained and unconstraine d, followed by the information regarding construction of Ohlson’s O score used to identify probability of distress for firms. 1.3.1 Measure of Governance Characteristics To measure the strength of shareholder ri ghts, the database I use employs the GIndex developed by Gompers, Ishii, and Me trick, (2003), henceforth GIM and the EIndex developed by Bebchuck, Cohen and Ferre ll (2005), henceforth BCF. They both use data from the Investor Responsibility Res earch Center (IRRC), which publishes detailed listings of corporate governance pr ovisions for individual firms in Corporate Takeover Defenses by Virginia Rosenbaum. The data on governance provisions are derived from various sources, such as corporate bylaws, ch arters, proxy statements, annual reports, as well as 10-K and 10-Q documents filed with SEC. The governance Index is constructed as follows: for every firm GIM add one point for every provision that restricts shareholder rights (i ncrease managerial power). While this index does not accurately reflect th e relative impacts of the various provisions, it has the advantage of being transparent and easily reproducible. The index does not require any judgments about the efficacy or wealth effects of any of these provisions;

PAGE 19

13 GIM consider only the impact on the balance of power. To clarify the logic behind the construction on the Governance Index, GIM use the following example; consider classified boards, a provision that staggers the terms and elections of directors and, thus, can be employed to slow down a hostile take over. If management uses this power judiciously, it could possibly l ead to an increase in overa ll shareholder wealth; if management, however, uses this power to mainta in private benefits of control, then this provision would diminish shareholder wealth. Ei ther way, it is apparent that classified boards enhance the power of managers a nd weaken the control rights of large shareholders. Hence, the Governance Inde x captures the balan ce of power between management and shareholder. Most provisions other than classified boards can be viewed with the same logic. Almost every provision enables management to resist different types of shareholder activism, such as calling special meetings, changing the firm’s charter or bylaws, suing the directors, or replacing them all at once. GIM note, however, that there are two exceptions, secret ballots (confidential voti ng) and cumulative voting. A secret ballot designates a third party to c ount proxy votes and, therefore, prevents management from observing how specific sharehol ders vote. Cumulative voting enables shareholders to concentrate their director’s votes so that a large minority shareholder can ensure some board representation. These two provisions are usually proposed by shareholders and opposed by management because they enhance shareholder rights and diminish the power of management. Thus, for each one, GIM add one point to the Governance Index when firms do not have it. For all other provisions, GIM add one point when firms do have it.

PAGE 20

14 In summary, the Governance Index is simply the sum of one point for the presence (or absence) of each provision. BCF (2005) argue that there is no a prio ri reason to expect that all the 24 IRRC provisions have equal relevance when meas uring firm’s governance. They study which IRRC provisions matter to the relationship be tween corporate governance and firm value. Their analysis leads them to identify six provi sions that are likely to play a substantial role in determining the governance of firms. Based on these six provisions they construct an index that they label the ‘entrenchment inde x”. Each firm in their database is given a score, from zero to six, with higher the sc ore indicating deeper entrenchment by the managers and hence proxied for poorer governance. 1.3.2 Measure for Financial Constraints In order to study the impact of financial status (constrain t / unconstraint), I divide the sample into sub samples that face greater financing constraints th an others as defined by the existing literature. The approach I use to distinguish the sample as financially constrained and unconstrained is based on th e results of Kaplan and Zingales (1997) study. Kaplan and Zingales (1997) classify firm s into discrete categories of financial constraint and then use an ordered logit re gression to relate their classifications to accounting variables. Lamont, Polk and S aa-Requejo (2001) used these regression coefficients to construct an index consisting of a linear combination of five accounting ratios, called the KZ index. The KZ index is hi gher for firms that are more constrained. The five variables, along with the signs of th eir coefficients in the KZ index, are: cash flow to total capital (negative), the market to book ratio (positive), debt to total capital (positive), dividends to total capital (nega tive), and cash holdings to capital (negative).

PAGE 21

15 Following Lamont, Polk and Saa-Raquejo (2001), I construct the five variable KZ index for each firm-year as the following linear combination: KZ Index (five variable) = -1.002*CashFlow + 0.283*Q + 3.130*Leverage – 39.368*Dividends – 1.315*CashHoldings. I classify the top tercile of all firms in the total sample ranked on the KZ index as financially constrained and classify the botto m tercile as financially unconstrained. This classification results in 6,153 fi rm-years as financially cons trained and 5,509 firm-years as financially unconstrained. 1.3.3 Measure for Financial Distress Ohlson (1980) uses maximum likelihood es timation of the so-called conditional logit model to predict corporate failure as evidenced by the event of bankruptcy. They identify four basic factors as being statistic ally significant in affecting the probability of failure within one year – (i) size; (ii) measur es of financial structur e; (iii) measures of performance and (iv) measures of curren t liquidity. In this paper I use Ohlson’s probabilistic prediction of ba nkruptcy measure very closely following Bhagat, Moyen and Suh (2005)’s use of the same measure to identify firms in performance declines. This measure is based on Ohlson’s predicted ba nkruptcy probabilities p, where P = 1/(1+e-Yit) Yit = -1.32 .407*(ln (TA)) + 6.03*TLTA – 1.43*WCTA + .757*CLCA – 2.37*NITA – 1.83*FUTL + .285*INTWO – 1.72OENEG .521*CHIN Wherein TA is total assets (COMPUSTAT #s in parentheses) (#6); TLTA is total liabilities to total a ssets (#181/#6); WCTA is the ratio of working capital to total assets [(#4 #5) / #6]; CLCA is the current liabilities to current assets ratio (#5/#4); NITA is net income to total assets ratio (#172/#6) and FUTL is fund from operations to total liabilities

PAGE 22

16 ratio (#110/#181). INTWO = 1 if net income (#172) is negative in prev ious two years or zero otherwise; ONEEG = 1 if to tal liabilities (#181) is great er than total assets (#6), 0 otherwise and CHIN is the ratio of the diffe rence in net income of current period with previous period over the absolute value of the difference (NIt – NIt-1)/ (|NIt| |NIt-1|). Following Bhagat et al (2005) this measur e is obtained from a variant of Ohlson’s bankruptcy probability model. Because the FU TL variable greatly restricts the sample size, pseudo-bankruptcy probabi lities p are calculated by igno ring the effect of FUTL in predicting bankruptcy probabil ities: p = 1/ (1 + e-Yit) Firms with declining performance and faci ng financial distress include firm-year observations with pseudo-bankruptcy proba bilities greater than or equal to 50%. 1.3.4 The Sample Selection Process I use the G-index developed by Gompers et al (2003) and Eindex developed by Bebchuck et al. (2005) as my measures of governance in order to distinguish between strong and poorly governed firms. Due to this fact the sample size for this paper is constrained by the firm-years for which the GIndex and the E-Index numbers that have been computed and provided for research purposes on the respective authors’ homepages. The accounting data for the sample comes from the Research Insight – COMPUSTAT database (numbers in parentheses are COMP USTAT data items). I include firms for all the years of the sample period (1990-2005) for which t -1 and t -2 COMPUSTAT data is available for the said parameters detailed in Table 1. The final sample size is 17,587 firmyears.

PAGE 23

17 Table 1. Variable Definition and Construction S.No Variable Notation Variable Description COMPUSTAT Notation 1. Size Natural logarithm of total assets Ln(#6) 2. Cash Flow Income before Extraordinary Items + Depreciation and Amortization #18 + #14 3. Q Market value of assets / Book Value of assets {(#199 #25) + #6 – (#60 + #74)} / #6 4. Leverage Total Liabilities / Total Assets #181 / #6 5. KZ -1.002*CashFlow+0.283*Q+3.130*L everage– 39.368*Dividends – 1.315*CashHoldings Item #8 is a lagged variable. CashFlow (#18 + #14) / #8 Q {(#6 + (#25 #24) – #60 – #74)} / #6 Leverage (#9 + #34) / (#9 + #34 + #216) Dividends (#21 + #19) / #8] CashHoldings #1 / #8 6. O-Score Yit = -1.32 .407*(ln(TA)) + 6.03*TLTA – 1.43*WCTA + .757*CLCA – 2.37*NITA – 1.83*FUTL + .285*INTWO – 1.72OENEG .521*CHIN Ln(TA) Ln(#6) TLTA #181/#6 WCTA (#4 #5) / #6 CLCA #5/#4 NITA FUTL INTWO OENEG CHIN #172/#6 #110/#181 1 if #172t & #172t-1 < 0; 0 otherwise. 1 if #181 > #6; 0 otherwise. (NIt – NIt-1)/(|NIt| |NIt-1|) 7. Cash Holdings {Cash + Mkt Sec}/ {T.A} [#1 / {#6}] 8. Net Assets Book value of Total assets Cash & Mkt Sec. #6 #1 9. P/O Ratio Common and preferred dividends / Net Income (#19 + #21) / #172 10. NWC {Current assets – Cash & Mkt Sec – Current Liabilities} {#4 #1 #5} 11 Acq Acquisitions #129 12. CAPEX Capital Expenditures #128 13. G-index The Gompers, Ishii and Metrick (2003) Governance Index 14. E-Index The Bebchuck, Cohen, and Ferrell (2005) Entrenchment Index 15. Strong Gov Firms for which the G number is less than or equal to 7 or E number is less than or equal to 2 16. Bad Gov Firms for which the G number is greater than or equal to 12 or E number is greater than or equal to 4 This table briefly describes the construction of the control and test variables used in this paper.

PAGE 24

18 For a firm to be included in the sample in a given year t it must meet the following criteria. (a) It has at least two years of COMP USTAT data prior to year t for all the variables listed in Table 1 and (b) it ha s to have a governance index (G) score as tabulated by Gompers, Ishii, and Metrick ( 2003) and entrenchment i ndex (E) as tabulated by Bebchuck, Cohen and Ferrell (2005). The G index score have numbers from 0 to 24, with the higher the score indicating poor gove rnance. In the constr uction of the E index only a subset of IRRC provisions are used a nd the index goes from 0 to 6, again with a higher index score indicating poor governance. I have made one cri tical assumption with respect to the governance index and the entren chment index numbers for the firms in my sample. Both databases provi de index numbers for discre te years such as 1990, 1993, et al. It is widely accepted in the governance literature that governance characteristics for firms do not change significantly, if at all, from year to year. Hence, in order to complete my panel data I make an assumption that G and E scores respectively for all firms in the y ears not tabulated by GI M (2003) and BCF(2005) have the same G score and E score respectivel y as that of the prev iously reported year until new data is available. For example, Amgen Inc., (TIC: AMGN) has a G score 9 in year 1993 and 10 in year 1995 as per th e GIM (2003) tabulations. Based on my assumptions, I tabulate for AMGN a G score of 9 for 1994 and a score of 10 for year 1996 and 1997. Following this assumption I fill up the G and E scores for the years 1991, 1992, 1994, 1996, 1997, 1999, 2001, and 2003. I then classify the firms with a G score4 4 Gompers, Ishii, and Metrick (2003) refer to the portfolio of firms with G <= 5 as a “democracy” portfolio and a portfolio of firms with G >= 14 as a “dictatorshi p” portfolio. Harford et al. (2008) sort the GIndex into quartiles and term the 1st quartile (strong sharehol der rights) as firms' with good governance and the 4th (weak shareholder rights) quartiles as firms with poor governance.

PAGE 25

19 of 7 and less as firms with strong governance and firms with G score of 12 and greater as poor governance firms. 1.3.5 Variable Description Table 1 lists all the variable s used in this paper. I us e the market-to-book ratio as measure of a firm’s growth opportunities, si nce the value of grow th options are not included in a firm’s book value, but should be reflected in its market value. I define the firm’s cash flow as income before extraord inary items plus depreciation and amortization charges. A firm’s decision with regard to its holdings of cash is modeled as a function of a number of sources and (competing) uses of funds (Almeida et al. 2004). Fazzari and Petersen (1993) argue that firms can offset the impact of cash flow shocks on fixed investment by adjusting working capital. Opler, Pinkowitz, Stulz and Williamson (1999) find that capital expenditures increase m onotonically with excess cash. Harford (1999) finds that cash-rich firms tend increase its acquisition activity. I include net working capital, which I define as current assets, minus cash and marketable securities, minus current liabilities. The control variable s net working capital, capital expenditure, acquisition, and cash flow are scaled by net asse ts, which is the book value of total assets net of cash. The Governance variables have b een borrowed from the Gompers, Ishii and Metrick (2003) Governance Index and Bebchuck, Cohen, and Ferrell (2005) Entrenchment Index. Table 1 list each of th ese variables and very briefly mentions its construction methodology.

PAGE 26

20 1.4 Empirical Results 1.4.1 Summary Statistics Table 2 provides descriptive st atistics for the different va riables used in the paper. The average cash holdings over the 15-year sa mple period used is about 0.12. In other words, firms hold just under 12 percentage point s of their total assets in cash. The next two variables examine the governance of firms. First, the G-index developed by Gompers, Ishii and Metrick (2003) is constr ucted as a proxy for th e balance of power between shareholders and managers. The aver age G-index score for the sample is just over nine. The E-index developed by Bebc huck, Cohen and Ferrell (2005) as another governance measure, examines the entrenchment level of managers. The average E-index score for the sample is around 2.27. The two variables, the KZ-index and the probability of distress, focus on measuring the level of financial constr aint for firms. The average KZ-index5 is about -3.9. The distress variables focus on firms taking fi nancial constraint one step further. The measure for firm level of distress is the proba bility of bankruptcy (distress) as measured by the Ohlson (1980). The average is 0.31 for this measure. The final seven variables are control vari ables used in the regressions including size, cash flow, leverage, Q, net working capital, capital expenditure, and acquisitions. Table 2 shows the averages of each of thes e variables. Table 2 also breaks down the sample based on strong and poor governan ce. Separating the types of governance illustrates the major difference in cash holdings between strong and poor governance, which is around 7.5 percentage points. 5 The KZ index is constructed by Lamont, Polk and Saa-Requejo (2001) using results from Kaplan and Zingales (1997) measures of firm level of financial constraints.

PAGE 27

21 Table 2. Descriptive Statistics Full Sample Strong Gov Poor Gov Difference Variable Obs. Mean Std. Dev. Mean Mean Good-Bad Cash Holdings 17587 0.1187 0.1646 0.1471 0.0728 0.0743*** G-index 17587 9.0545 2.7601 5.8420 13.0109 -7.169*** E-index 17587 2.2746 1.3447 1.0306 3.6213 2.5908*** KZ Index 17587 -3.899117.8584 -5.1551 -2.5422 2.6129*** Prob. of Distress 17587 0.3137 0.2863 0.2980 0.3112 0.0133*** Size 17587 7.1672 1.5020 6.8232 7.5417 0.7185*** Cashflow 17587 0.2227 0.6666 0.3009 0.0998 0.2011*** Leverage 17587 0.5677 0.3322 0.5371 0.5980 0.0608*** Q 17587 1.8357 1.4371 1.9911 1.6725 0.3186*** NWC 17587 0.0664 0.3042 0.0707 0.0868 -0.0161 CAPX 17587 0.0685 0.0597 0.0731 0.0622 0.0110*** ACQ 17587 0.0247 0.0636 0.0238 0.0264 -0.0027 This table provides summary statistics on ke y variables of the sa mple of 17,587 firm-year observations (1990-2005). Cash Ho ldings is computed as cash and marketable securities standardized by total assets. G-index and E-index are the governance and entrenchment index numbers as tabulated by Gompers et al (2003) and Bebc huck et al. (2005) respectively. KZ index, a proxy for financia l constraint, is measured following the methodology employed by Lamont et al (2001) Probability of distress is measured following Ohlson’s (1980) probabilistic pr ediction of bankruptcy methodology. Size is measured as natural log of total assets. Cashflow is measured as income before extraordinary items plus depreciation and am ortization. Leverage is ratio of total liabilities to tota l assets. Q is measured as ratio of market value to book value of assets. Net working capital is measured as current a ssets less of cash and ma rketable securities and current liabilities. Variables Cashflow, Net working capital, Capital expenditures and Acquisitions all are stan dardized by net assets (Total a ssets minus cash and marketable securities) following Opler et al. (1999). The c onstruction of all variables is detailed in Table 1. This table also illustrates differences be tween both strong and poor governance firms, especially in terms of performance as m easured by cash flow. A higher number for KZ index indicates financially c onstrained. Table 2 shows that on average poorly governed

PAGE 28

22 firms are more financially constrained. Poor ly governed firms on average have higher leverage and lower growth opportunities and hence lower capital expenditures as compared to strong governance firms. Further, it can be observed that both investment in net working capital and acquisitions are hi gher for poorly governed firms, although the differences for these two parameters are not statistically significant. 1.4.2 Univariate Tests Table 3 breaks down the cash holdings by year and by governance quality. Using the G-index, governance is sp lit up into good, average, and poor governance. Column ‘N’ notes the firm count under each category. Consiste nt with prior evidence such as Dittmar and Mahrt-Smith (2007)6, this table shows cash holdings are increasing over time. During the sample time period, cash holdings increase from about 8.6 percent of total assets to just over 20 percent of total assets. Firms with strong governance experience an increase in cash holdings of about 14 percentage poi nts over the time period, while firms with poor governance experience only about an 8 perc entage point increase. The difference in cash holdings between strong and bad governance firms increases over the sample period and peaks in 2002 and 2003. This evidence w ould not provide support for the classic agency argument predicting higher cash holdings for poor governance firms. 6 The sample considered in their paper reflects variation in cash holdings as percentage of total assets from 5% in year 1990 to 13% in 2003.

PAGE 29

23 Table 3. Sample Sort: Cash Holdings by Governance Over Time (Using G-Index) Year Good Gov Avg. Gov Poor Gov Total Diff. (Good Gov Poor Gov) CH N CH N CH N CH N 1990 0.1031 339 0.0834498 0.0633 159 0.0861 997 0.0398 1991 0.1026 343 0.0848505 0.0639 181 0.0865 1030 0.0387 1992 0.1051 326 0.0838484 0.0625 168 0.0866 979 0.0426 1993 0.1190 318 0.0961551 0.0768 224 0.0985 1094 0.0421 1994 0.1186 306 0.0852532 0.0646 218 0.0905 1057 0.0540 1995 0.1147 301 0.0842551 0.0600 253 0.0869 1106 0.0548 1996 0.1149 289 0.0885531 0.0639 242 0.0899 1063 0.0510 1997 0.1215 271 0.0852494 0.0626 218 0.0900 983 0.0588 1998 0.1490 349 0.0950603 0.0636 259 0.1091 1210 0.0854 1999 0.1489 316 0.0946576 0.0591 265 0.1076 1157 0.0898 2000 0.1416 401 0.1103567 0.0615 302 0.1110 1271 0.0801 2001 0.1587 412 0.1246554 0.0732 326 0.1260 1291 0.0855 2002 0.2225 382 0.1771534 0.0865 285 0.1748 1202 0.1360 2003 0.2298 365 0.1809684 0.0979 293 0.1801 1342 0.1319 2004 0.1957 350 0.1894757 0.1114 290 0.1778 1396 0.0843 2005 0.2397 103 0.2064229 0.1417 77 0.2017 409 0.0980 Total 0.1471 5173 0.119592620.0728 37620.1187 17587 0.0743 This table breaks down the cash holdings by year over the samp le period 1990-2005 as shown under the column Total. Further, the ca sh holdings are also tabulated separately for good, average and poor governed firms by each year. The governance measure considered here is the Gompers et al (2003) G-Index measure. Firms with G-score of 7 and below are termed as Good governance firms. Firms with G-score 12 and higher are categorized as poorly governed firms. Finall y, all the firm with a G-score between 8 and 11 are termed as firms with average governance. Table 4 provides a correlation matrix for all variables to be used in the regressions. I find that the correlation coeffici ent between most of the variables is fairly low. We do see as expected a high correla tion between the G-index and the E-index.

PAGE 30

24 Table 4. Correlation Matrix CH Gindex Eindex Size KZ Leverage Q CAPX ACQ Cashflow NWC CH 1.0000 Gindex -0.0721 1.0000 (0.0000) eindex -0.0047 0.7934 1.0000 (0.5187) (0.0000) Size -0.4370 0.2653 0.0316 1.0000 (0.0000 (0.0000) (0.0000) KZ -0.1813 0.0022 0.0253 0.1110 1.0000 (0.0000 (0.7750) (0.0009) (0.0000) Leverage -0.3495 0.0674 0.0178 0.2834 0.1780 1.0000 (0.0000) (0.0000) (0.0154) (0.0000) (0.0000) Q 0.5184 -0.0926 -0.0692 -0.3030 -0.0615 -0.2408 1.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) CAPX 0.0730 -0.0088 -0.0487 -0.0768 0.1012 -0.0174 0.1099 1.0000 (0.0000) (0.2298) (0.0000) (0.000 0) (0.0000) (0.0175) (0.0000) ACQ -0.1315 0.0319 0.0716 -0.0983 -0 .3210 0.0532 -0.0568 -0.2050 1.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Cashflow 0.8794 -0.0326 -0.0367 -0.3465 -0 .1050 -0.2329 0.3105 0.0498 -0.1125 1.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.000 0) (0.0000) (0.0000) (0.0000) (0.0000) NWC -0.3704 -0.0556 -0.0407 -0.0141 0.0200 -0.2539 -0.1361 -0.0919 0.0117 0.4389 1.0000 (0.0000) (0.0000) (0.0000) (0.0546) (0.0083) (0.0000) (0.0000) (0.0000) (0.1138) (0.0000) This table shows the correlation ma trix between the independent va riables used in this paper. G-index and E-index are the gover nance and entrenchment index numbers as tabulated by Gompers et al (2003) and Be bchuck et al. (2005) respecti vely. Size is measured a s natural log of total assets. KZ index, a proxy for financial constrai nt, is measured following the methodology employed by Lamo nt et al (2001). Construction of variab les: Cashflow, Q, Leverage, CAPX ACQ and NWC are detailed in Ta ble 1. P-values in parentheses .

PAGE 31

25 This is expected as these two measures ar e created from the sa me IRRC provisions, where the E-index is a subset of the G-i ndex focused on entrenchment provisions. These two variables are not used in the same regr ession in the paper. The two are used only independently to show the results are robust to either measurement of governance. In order to make certain that multicollinearity is not an issue between independent variables, Variance inflation factor (VIF) test was performed. VIF measures the impact of collinearity among the independent variables’ in a regression mode l on the precision of estimation. It expresses the degree to which collinearity among the predictors degrades the precision of an estimate. Typically a VI F value greater than 10 is of concern. VIF tests resulted with a maximum VIF measure 2.64 for leverage and the over-all mean VIF was 1.42. Table 5 runs a univariate sort on the sa mple. Panel A lists the cash holdings over the sample period for all differing levels of governance. There is an obvious trend for both G-index and E-index where cash holdings decrease as the G-index and E-index increase. Increases in the Gindex and E-index signify a worsening in firm governance. Consistent with the findings of Table 3, fi rms with better governa nce hold more cash. Panel B of Table 5 sorts cash hol dings by additional variables. Also shown in the table is a t-test of the difference between the high a nd low group of each different sort. The first sort looks at G-index, which is a recap of table 3 and tests the difference between strong and bad governance. The difference is almost 7.5 percentage points a nd is statistically significant at the 1% level. E-index has a similar result to G-index. Splitting the sample based on size shows smaller firms hold a higher percentage of cash than larger firms. Small firms hold almost 11 percentage points more cash as

PAGE 32

26 compared to assets as large firms do. The next factor examined is the financial constraint level of firms, as measured by the KZ-index. Table 5A: Univariate Test (Cash Holdings vs. Governance Measures) Panel A: Cash Holdings G-Index Mean E-index Mean 2 Best Governance 0.2326 0 Best Governance 0.1435 3 0.16151 0.1554 4 0.14842 0.1329 5 0.14663 Average Governance 0.1113 6 0.14664 0.0808 7 0.15685 0.0582 8 0.14936 Worst Governance 0.0997 9 Average Governance 0.1283 10 0.1163 11 0.0941 12 0.0868 13 0.0640 14 0.0642 15 0.0628 16 0.0549 17 0.1076 18 0.0743 19 Worst Governance 0.0530 Panel A shows cash holdings based on differe nt levels of governance. G-Index is the Gompers et al (2003) score and E-Index is the Bebchuck et al (2005) score. In both cases the lower the index number, the better the gove rnance of firms. The means reported here are the mean values of cash holdings for fi rms with respective G and E index numbers. Cash holdings are computed as cash and marketable securities standardized by total assets.

PAGE 33

27 Table 5B: Univariate Test (Cash Holdings vs. Test Variables) Panel B: Cash Holdings Variable Description Mean Difference (1-3) G-Index 1 Strong Governance 0.1471 G-Index 2 Average Governance 0.1195 G-Index 3 Poor Governance 0.0728 0.0743*** E-Index 1 Strong Governance 0.1511 E-Index 2 Average Governance 0.1220 E-Index 3 Poor Governance 0.0773 0.0738*** Size 1 Small 0.1818 Size 2 Medium 0.1095 Size 3 Large 0.0742 0.1076*** KZ-Index 1 Not Constrained 0.2176 KZ-Index 2 Average Constrained 0.0979 KZ-Index 3 Constrained 0.0530 0.1646*** Distress Dummy: 0 Not Distress 0.1301 Distress Dummy: 1 Distress 0.0948 0.0353*** Q 1 Low Q 0.0790 Q 2 Average Q 0.0933 Q 3 High Q 0.2042 -0.1252*** Panel B shows cash holdings based on differe nt levels of governance measures, Size, Financial Constraint and Distress status a nd Growth Opportunities. G-Index and the EIndex broadly grouped as good, average and poor governance firms respectively. The next sets of variable s are grouped by size. Size as meas ured by total assets (adjusted for CPI index for 2004 dollars) are sorted and grou ped as size 1 (bottom tercile), size 3 (top tercile) with size 2 constituting the middle tercile. KZ index scores, computed in accordance with Lamont et al (2001) me thodology, are also grouped by terciles, the smallest of the three labeled as financially unconstrained firms. Probability of distress is measured following Ohlson’s (1980) probabili stic prediction of bankruptcy methodology. Distress dummy is 1 when Ohlson’s probab ility of bankruptcy is greater than 0.5. Q variable is computed as the ratio of market value of firm to the book value of firm and proxy growth opportunities. Q values with less than 1 are firms with low growth opportunities, Q values between 1 and 2 as firms with average growth opportunities and Q values greater than 2 are firms with gr eatest growth opportunitie s. Cash holdings are computed as cash and marketable securities st andardized by total assets. The last column shows difference of means t-test. *** indicates significant at 1% confidence level

PAGE 34

28 Financially constrained firms hold over 16 percentage points less cash than nonfinancially constrained firms. Financially constrained firms find it difficult to access financial markets (Whited, 1992; Gilchrist and Himmelberg, 1995) and thus are forced to put all of their assets to use and are unable to hold excess levels of cash. Firms considered in financial distress have a similar issue with shortage of cash. Investment opportunities as measured by Q also impact the level of cash holdings7. Firms with a higher Q or high growth opportunities hold around 12. 5 percentage points more cash as compared to assets than firms with low growth opportunities. This is not surprising as firms with more future growth opportunities will hold more cash on ha nd to quickly and more efficiently take advantage of opportunities as they become available. Table 6 examines the industry concentration of the firms in the sample. We examine different industries to see if the di fference in cash holdings between firms with strong and poor governance is concentrated in certain industries. I report in Panel A as any industry with at least 250 firm-year observations. Table 6 is sorted by the tota l concentration of the sample by industry. Reported in the table are cash holdings for each type of govern ance as well as the ratio of cash holdings of strong governance firms to poor governan ce firms. I find certain industries have greater differences, but only three industries of the reported 20 indus tries have ratios less than one. That is, only 15 percent of the i ndustries with si gnificant concen trations do not follow the trend of strong governance firms holding more cash. 7 The Q variable is used as a measure of the firm’s future growth opportunities in this paper. However, Q is also sometimes employed as a performance measure. Thus, to avoid problems with interpretation of Q, we also use an alternative measure of growth opportunities in a later section.

PAGE 35

29 Table 6A: Cash Holdings by Industry and Governance Full Sample Good Gov Bad Gov Good/Bad SIC Code Industry Con CH Con CH Con CH Ratio 49 Electric, Gas and Sanitary Services 9.10% 0.0217 7.01% 0.0178 7.68% 0.0198 0.8993 28 Chemicals and Allied Products 8.10% 0.1707 7.63% 0.2108 9.88% 0.0993 2.1222 73 Business Services 7.51% 0.2678 9.93% 0.2828 3.26% 0.1160 2.4387 36 Electrical and Electronic Equipment (Ex. Computers) 7.36% 0.2153 9.08% 0.2531 4.97% 0.1431 1.7686 35 Industry and Commercial Machinery and Computers 7.14% 0.1756 7.54% 0.2203 8.03% 0.0768 2.8669 38 Measuring and Analyzing Instruments 4.86% 0.1583 3.37% 0.1873 5.97% 0.1174 1.5955 37 Transportation Equipment 3.16% 0.0761 1.97% 0.0812 3.39% 0.0795 1.0211 20 Food and Kindred Products 3.15% 0.0570 3.83% 0.0749 2.61% 0.0416 1.7977 48 Communication 2.94% 0.0736 3. 81% 0.0952 3.39% 0.0156 6.1050 13 Oil & Gas Extraction 2.78% 0.0525 2.13% 0.0747 2.04% 0.0349 2.1424 33 Primary Metal Industries 2.59% 0. 0493 2.37% 0.0483 4.13% 0.0436 1.1074 50 Wholesale Trade Durable Goods 2.54% 0.0582 2.46% 0.0467 2.88% 0.0542 0.8623 27 Printing, Publishing & Allied Industries 2.48% 0.0813 1.88% 0.1150 3.37% 0.0359 3.2000 26 Paper and Allied Products 2.08% 0.0262 1.14% 0.0268 2.74% 0.0225 1.1907 34 Fabricated Metal Products 2.05% 0.0536 1.25% 0.0825 2.88% 0.0440 1.8737 60 Depository Institutions 2.03% 0.1158 2.04% 0.1407 1.82% 0.0868 1.6206 59 Miscellaneous Retail 1.77% 0.0951 3.09% 0.0954 0.62% 0.0487 1.9601 51 Wholesale Trade – Non Durable Goods 1.52% 0.0594 0.60% 0.1223 2.85% 0.0328 3.7299 56 Apparel and Accessory Stores 1.49% 0.1745 1.93% 0.1537 2.31% 0.1371 1.1212 58 Eating and Drinking Places 1.42% 0.0499 1.39% 0.0533 1.57% 0.0564 0.9465 This table presents cash holdings (standardized by total assets) for firms differentiated by industry. Only firms with at least 250 firm -year observations per industry are reported here. The column Con represents concentrati on of firms by industry for the total sample. Next, I report cash holdings for good govern ance firms and poor governance firms again segregated by industry. The governance measure used here to is the Gomper, Ishii, and Metrick (2003) G-Index. Similar results were obtained when Bebchuck, Cohen and Ferrell (2005) E-Index was use d. The last column reports th e ratio of cash holdings for good over poor governance firms.

PAGE 36

30 Table 6B. Cash Holdings by Industry and Growth Opportunities Good/Bad SIC Code Industry Con Mean-Q Ratio 50 Wholesale Trade Durable goods 2.54% 1.393 0.862 49 Electric, Gas and Sanitary Services 9.10% 1.107 0.899 58 Eating and Drinking Places 1.42% 1.990 0.947 37 Transportation Equipment 3.16% 1.531 1.021 33 Primary Metal Industries 2.59% 1.290 1.107 56 Apparel and Accessory Stores 1.49% 2.043 1.121 26 Paper and Allied Products 2.08% 1.465 1.191 38 Measuring and Analyzing Instruments 4.86% 2.219 1.596 60 Depository Institutions 2.03% 1.219 1.621 36 Electrical and Electronic Equipment (Ex. Computers) 7.36% 2.129 1.769 20 Food and Kindred Products 3.15% 2.058 1.798 34 Fabricated Metal Products 2.05% 1.512 1.874 59 Miscellaneous Retail 1.77% 2.015 1.960 28 Chemicals and Allied Products 8.10% 2.567 2.122 13 Oil & Gas Extraction 2.78% 1.505 2.142 73 Business Services 7.51% 2.514 2.439 35 Industry and Comm ercial Machinery and Computers 7.14% 1.890 2.867 27 Printing, Publishing & Allied Industries 2.48% 1.829 3.200 51 Wholesale Trade – Non Durable goods 1.52% 1.511 3.730 48 Communication 2.94% 1.760 6.105 This table reflects cash holdings (standardized by total assets) for firms differentiated by industry. Only firms with at least 250 firm -year observations per industry are reported here. The column ‘Con’ represents concen tration of firms by industry for the total sample. Next, I compute the cash holdings for strong governance firms and poor governance firms again segregated by industry. The last column repor ts the ratio of cash holdings for strong over poor governance firms. The governance measure used here to is the Gomper, Ishii, and Metric k (2003) G-Index. Similar results were obtained when Bebchuck, Cohen and Ferrell (2005) E-Index was used. The results are sorted in increasing order of the ratio of cash holdi ngs held by strong governance firms over poor governance firms. The corresponding averag e Q values are reported here for the respective industry.

PAGE 37

31 In Panel B of Table 6, I further examine this issue by sorting the industries by the ratio of cash holdings of strong and poor governance and adding in average Q of the industry. The results of the table are consiste nt with the idea as an industry’s Q increase so does the cash holdings ratio. Th is table is sorted by the ratio, as tabulated in the last column. 1.4.3 Multivariate Tests Table 7 illustrates the results from a regression with cash holdings as the dependent variable. The regression examines the effects of governance on cash holdings, while controlling for several other factors. The base model is as follows: Cash_Holdingsi t = a0 + a1Gi,t + a2KZi,t + a3Sizei,t + a4CashFlowi,t + a5Leveragei,t a6Market-to-booki,t + a7NWCi,t +a8Capital Expendituresi,t + a9Aquisitionsi,t + Year Dummies + Industry Dummies + ei,t The results of the fixed effects regr ession confirm the main findings of the previous univariate tests in a multivariate se tting. The coefficient for G-index in column (1) of Table 7 is negative and statistically significant meaning firms with better governance hold a significantly hi gher percentage of assets in cash. The coefficient for KZ-index is also negative and significant. Since increases in KZ mean higher financial constraint, the negative relati on means firms with lower levels of constraint hold more cash. This result lends support to the rational expectation that firm s hold more cash when they are able to (low financia l constraints), and they hold le ss cash when sources of funds are tight (high financial constr aints). Size is also negative and significant as smaller firms hold a higher percentage of cash per asset. Cash flow is posit ive and significant as better firm performance leads to higher total cash hol dings. This variable also helps to control

PAGE 38

32 for the documented difference in perfor mance between strong and poor governance (Harford, et al. 2008). Leverage is negative and significant, which suggests that the high fixed payments associated with high leverage lowers cash holdings. Consistent with the univariate findings, the coefficient for Q is pos itive and significant as firms with better opportunities hold a higher percentage of cas h. The positive relationship between capital expenditures and cash holdings indicates that firms in greater need for capital additions hold a greater percentage of cash. The acqui sition variable shows a negative and significant relationship with cash holdings, in dicating that firms engaging in acquisition activity hold a lower pe rcentage of cash. This finding is in line with Harford (1999), who shows that cash rich firms are more likely to actively pursue activ e acquisition strategy and in the process lower their cash holdings. The R-squared of this regression is high at 0.8526. Column (2) of table 7 adds net working cap ital to the regression. This variable is negative and significant and is in agreement w ith prior work. Dittmar et al. (2003) show similar results and comment that net work ing capital and cash holdings appear to be substitutes. Column (3) of Table 7 uses a dummy variable for strong governance instead of using the raw g-index number This is done to see if we get the same result as was conducted in Column (1) when we use a dum my variable instead of the continuous gindex variable since the results could be influenced by a few firms with really strong or poor governance. The result from the dummy va riable used to measure strong governance is the same as when using the raw g-index in column (1) and supports the results from the previous tables. The other variables in th is regression do not ch ange significantly. Columns (4) and (5) run similar tests but use the e-index instead of the g-index used in

PAGE 39

33 the other columns. The results using e-inde x are the same as those found when using gindex. As e-index decrease or as managers become less entrenched they hold a higher percentage of cash. Table 7. Multivariate Tests: Impact of Governance on Cash Holdings Dependant Variable: Cash Holding (Cash / TA) (1) (2) (3) (4) (5) G-Index -0.0046 -0.0044 (0.00) (0.00) Strong Governance Dummy 0.0313 0.0303 (0.00) (0.00) Poor Governance Dummy -0.0021 -0.0007 (0.22) (0.70) E-index -0.0125 (0.00) KZ-Index -0.0005 -0.0005 -0.0006 -0.0005 -0.0005 (0.00) (0.00) (0.00) (0.00) (0.00) Size -0.0046 -0.0044 -0.0066 -0.0066 -0.0054 (0.00) (0.00) (0.00) (0.00) (0.00) Cashflow 0.1831 0.1733 0.1863 0.1746 0.1826 (0.00) (0.00) (0.00) (0.00) (0.00) Leverage -0.0637 -0.0972 -0.0595 -0.0994 -0.0607 (0.00) (0.00) (0.00) (0.00) (0.00) Q 0.0211 0.0194 0.0208 0.0189 0.0199 (0.00) (0.00) (0.00) (0.00) (0.00) NWC -0.0576 -0.0701 (0.00) (0.01) CAPEX 0.1141 0.1207 0.1112 0.1156 0.1341 (0.00) (0.00) (0.00) (0.00) (0.00) ACQ -0.1641 -0.1546 -0.1704 -0.1586 -0.1614 (0.00) (0.00) (0.00) (0.00) (0.00) Constant Yes Yes Yes Yes Yes Number of Obs. 17587 17587 17587 17587 17587 R-squared Overall 0.8526 0.8525 0.7656 0.7418 0.8486 This table shows the regression of the depe ndant variable: Cash Holdings (Cash / TA) on governance measures G-index in model (1) an d (2). Model (3) and (4) use strong and poor governance dummy by G-Index and E-Inde x respectively. Size Leverage, Cash flow, Q, Net working capital, capital expenditu res, Acquisitions, the financial constraint measure KZ index are being used as control variables in this regression. The construction of these variables is detailed in Table 1. 2digit SIC codes are used as industry dummy. Finally the year dummies were also considered in order to control for fluctuations on account of passage of time. Model (5) uses E-index in place of G-index. P-values are shown in parentheses.

PAGE 40

34 Both measures of governance lead to the same result, with strong governance positively related to cash holdings. These finding s suggest that agency problems are not the driving force behind the higher cash holdings in recent years. After finding this result, the big question that arises is why do firms with better governance hold a higher percentage of their assets in cas h. We address this question next. Table 8 examines the question of govern ance and cash holdings with a two-way sort of governance and other key variables used in the regression. The results with size and governance illustrate small firms with st rong governance hold the highest cash as a percentage of assets, while large poor governan ce firms hold the least cash. The next sort examines governance with Q and finds the high est level of cash holdings for firms with strong governance and high Q or high growth opportunities. This makes sense as firms with greater opportunities may want to hold highe r levels of cash to take better advantage of future opportunities. The next sort examines governance and the KZ-index. Firms with strong governance and low financial cons traint hold the highest perc entage of cash, suggesting that these firms may hold more cash when they are financially able to, perhaps building up slack for weaker times. The differen ce between firms with strong and weak governance is negligible when firms in both the groups are financially constrained. These findings suggest that firms with strong govern ance build up cash when available, but poor governance firms do not build up as much cas h even when they are not financially constrained. Finally, I observe higher cash holdings for firms that are not in distress as compared to firms in distress. Panel B uses e-index as a measure for governance with a similar result.

PAGE 41

35 Table 8A. Cash Holdings and Governance Using G Index – Double Sort Panel A: G-Index is used to calcu late good, average, and poor governance Good Average Poor Difference (Good Poor) Large Size 0.0944 0.0761 0.0544 0.0400** Medium Size 0.1194 0.1176 0.0734 0.0460** Small Size 0.2079 0.1789 0.1130 0.0949*** Good Average Poor Difference Low Q 0.0900 0.0796 0.0530 0.0369** Avg Q 0.1182 0.0962 0.0573 0.0610** High Q 0.2353 0.2056 0.1310 0.1043*** Good Average Poor Difference KZ1 (Con) 0.0604 0.0520 0.0410 0.0195** KZ2 (Avg) 0.1285 0.0978 0.0617 0.0669** KZ3 (Not Con) 0.2696 0.2250 0.1147 0.1549*** Good Average Poor Difference Distressed 0.1146 0.0953 0.0602 0.0544** Not Distress 0.1629 0.1318 0.0770 0.0859** Panel A of this table reflects the univariate test of the key variab le – the cash holdings against the governance measur es G-Index broadly grouped as good, average and poor governance firms respectively. G-Index is the Go mpers et al (2003) score used as a proxy for governance measure of firms in this paper. The next sets of variables are grouped by size. Total assets adjusted for CPI index for 2004 dollars are sorted and the smallest tercile grouped as small firms and so on. KZ index scores, computed in accordance with Lamont et al (2001) methodology, are also grou ped by terciles, the sm allest of the three labeled as financially unconstr ained firms. Probability of di stress is measured following Ohlson’s (1980) probabilistic prediction of bankruptcy methodology. Distress dummy is 1 when Ohlson’s probability of bankruptcy is greater than 0.5. Q variable is computed as the ratio of market value of firm to the book value of firm and proxy growth opportunities. Q values with less than 1 ar e firms with low growth opportunities, Q values between 1 and 2 as firms with averag e growth opportunities and Q values greater than 2 are firms with greatest growth opportuni ties. Cash holdings are computed as cash and marketable securities standardized by to tal assets. The last column shows difference of means t-test. *** indicates significant at 1% confidence level

PAGE 42

36 Table 8B. Cash Holdings and Governance Using E Index – Double Sort Panel B: E-Index is used to calcu late good, average, and poor governance Good Average Poor Difference Large Size 0.0974 0.0732 0.0490 0.0485*** Medium Size 0.1299 0.1132 0.0718 0.0581*** Small Size 0.2134 0.1783 0.1261 0.0873*** Good Average Poor Difference Low Q 0.0851 0.0831 0.0587 0.0264** Avg Q 0.1128 0.0962 0.0648 0.0480** High Q 0.2408 0.1957 0.1375 0.1033*** Good Average Poor Difference KZ1 (Con) 0.0605 0.0539 0.0399 0.0207** KZ2 (Avg) 0.1227 0.0981 0.0669 0.0558*** KZ3 (not Con) 0.2548 0.2195 0.1379 0.1168*** Good Average Poor Difference Distressed 0.1143 0.0996 0.0572 0.0571*** Not Distressed 0.1615 0.1290 0.0840 0.0775*** Panel B of this table reflects the univariate test of the key variab le – the cash holdings against the governance meas ure E-Index broadly grouped as good, average and poor governance firms respectively. E-Index is the Be bchuck et al (2005) score, being used as a proxy for governance measure of firms in th is paper. The next se ts of variables are grouped by size. Total assets adjusted for CP I index for 2004 dollars are sorted and the smallest tercile grouped as small firms and so on. KZ index scores, computed in accordance with Lamont et al (2001) me thodology, are also grouped by terciles, the smallest of the three labeled as financially unconstrained firms. Probability of distress is measured following Ohlson’s (1980) probabili stic prediction of bankruptcy methodology. Distress dummy is 1 when Ohlson’s probab ility of bankruptcy is greater than 0.5. Q variable is computed as the ratio of market value of firm to the book value of firm and proxy growth opportunities. Q values with less than 1 are firms with low growth opportunities, Q values between 1 and 2 as firms with average growth opportunities and Q values greater than 2 are firms with gr eatest growth opportunitie s. Cash holdings are computed as cash and marketable securities st andardized by total assets. The last column shows difference of means t-test. *** indicates significant at 1% confidence level

PAGE 43

37 It appears that firms with strong governance hold higher levels of cash as growth opportunities increase. My proposal is str ong governance firms will increase holdings when advised to do so. One of the times when firms could be benefited from higher cash holdings is when they have strong growth oppor tunities. Table 9 take s a deeper look into this notion. Column (1) of table 9 examines the sample of firms with high levels of Q as measured with a Q above 2. Column (2) and column (3) examine the sample of firms with average Q (Q values between 1 and 2) an d low Q (Q values less than 1) respectively. The examination of each sample illustrates the magnitude of g-index decreases when moving from high Q to low Q. In column (3), which examines only low Q firms, the gindex is insignificant. That is, governance is not significantly rela ted to cash holdings when examining firms with limited growth opportunities. This story is consistent with the idea of firms with strong governance holding more cash when appropriate or when it is in shareholders’ best interest. Column (4) examines the whole sample and uses a dummy for both strong governance and high Q. It also includes an interaction va riable of high Q and strong gov ernance. All three variables are positive and significant. Strong governance, high Q, and the combination of the two all increase cash holdings. The strong govern ance dummy variable is significant but lower in magnitude as compared to Table (7) where the interaction te rm is not used. This would appear to be consistent with the id ea of one viable reason for strong governance firms to hold more cash. They hold higher leve l of cash when these firms have stronger growth opportunities and do so more than othe r firms with weaker governance. This in depth analysis of the role of growth opportunities to firm cas h holdings adds to the results shown in Harford et al. (2008) paper.

PAGE 44

38 Table 9. Multivariate Tests: Sample Differentiated by Growth Opportunities Dependant Variable: Cash Holding (Cash / TA) Sample High Q Avg Q Low Q Full Sample (1) (2) (3) (4) G-Index -0.0216 -0.0067 -0.0002 (0.00) (0.00) (0.46) Strong Governance Dummy 0.0365 (0.00) Poor Governance Dummy -0.0034 (0.05) KZ-Index -0.0044 -0.0001 -0.0011 -0.0006 (0.00) (0.00) (0.00) (0.00) Size -0.0194 -0.0102 -0.0237 -0.0040 (0.00) (0.00) (0.00) (0.00) Cashflow 0.2185 0.1413 0.1351 0.1714 (0.00) (0.00) (0.00) (0.00) Leverage -0.1324 -0.1307 -0.1065 -0.1270 (0.00) (0.00) (0.00) (0.00) Q 0.0170 0.0731 0.0457 (0.00) (0.00) (0.22) High Q Dummy 0.0385 (0.00) Low Q Dummy -0.0211 (0.00) Strong Governance Dummy Q 0.0008 (0.10) NWC -0.0811 -0.1128 -0.0735 -0.0734 (0.00) (0.00) (0.00) (0.00) CAPEX 0.1905 0.1401 0.3742 0.1366 (0.00) (0.01) (0.00) (0.00) ACQ -0.5599 -0.0986 -0.0208 -0.1678 (0.00) (0.00) (0.10) (0.00) Constant Yes Yes Yes Yes Number of Obs. 4558 10266 2763 17587 R-squared Overall 0.7921 0.7598 0.2349 0.8300 This table shows the regression of the de pendant Variable: Cash Holding (Cash/TA) on governance measure G-index, constraint measur e KZ index, and other control variables – Size (log of total assets), cash flow, Q, Le verage, NWC, CAPEX a nd ACQ. 2-digit SIC codes are used as industry dummy. Finally the year dummies were also considered in order to control for fluctuations on account of passage of time. The sample of independent variables is rest ricted in sample size by groupi ng firms in brackets of High Q, Average Q and Low Q. The last column re flects the results of using full sample. High Q, Low Q dummy variables and an inter action variable of strong governance dummy with Q is included in this model.

PAGE 45

39 In this paper I agree with Harford et al results that firms with better governance and better growth opportunities hold higher levels of cash hol dings, but I further extend my study to show that only well-governed firms with better grow th opportunities hold significantly higher levels of cash holdings. Well governed firms with low growth opportunities do not significantly differ in their cash holdings when compared to poorlygoverned firms with low growth opportunities. Another reason for holding additional cash is financial slack. It may help firms to hold additional cash as it may help them avoi d financial constraints and even financial distress. To test this notion I examine if firm governance is negatively related to financial constraint status of firms. Table 10 examines this issue. Column (1) examines the sample of firms considered financially unconstra ined by the KZ-index measure. For firms considered financially unconstrained, governan ce as measured by the g-index is negative and significant. That is, fina ncially unconstrained firms w ith better governance hold more cash than unconstrained firms with poor governan ce. In contrast, as shown in column (2), governance does not affect th e level of cash holdings when firms are financially constrained. This finding sugge sts that strong governance fi rms hold cash when available (i.e., when financially unconstrained). Colu mns (3) and (4) examine firms with strong governance and poor governance. Cash holdings for firms with strong governance are significantly influenced by the KZ-index a nd level of Q, while firms with poor governance are unaffected by the KZ-index and influenced by Q at a much smaller margin. The results of this table are consis tent with the idea of firms with strong governance holding more cash with increasing growth opportunities (as measured by Q) and decreasing financial cons traint (as measured by the KZ -index). The other interesting

PAGE 46

40 insight in this table is that poorly governed firms choose to hold lower levels of cash holdings and do not plan for contingencies of constraint or growth opportunities’ conditions. Table 10. Multivariate Tests: Sample Differen tiated by Financial Status and Governance Dependant Variable: Cash Holding (Cash / TA) Sample NonConstrained Constrained Good Gov Poor Gov (1) (2) (3) (4) G-Index -0.0118 -0.0010 (0.00) (0.19) KZ-Index -0.0078 0.0000 (0.00) (0.84) Size -0.0104 -0.0001 -0.0061 -0.0047 (0.00) (0.15) (0.00) (0.00) Cashflow 0.1446 0.5811 0.1793 0.4641 (0.00) (0.00) (0.00) (0.00) Leverage -0.0768 -0.0016 -0.1078 -0.1252 (0.00) (0.00) (0.00) (0.00) Q 0.0115 0.0059 0.0183 0.0015 (0.00) (0.00) (0.00) (0.00) NWC -0.0445 0.0407 -0.0992 0.0117 (0.00) (0.00) (0.00) (0.00) CAPEX 0.2742 0.0402 0.0942 -0.0644 (0.00) (0.00) (0.00) (0.00) ACQ -0.2221 -0.0473 -0.0769 -0.0018 (0.00) (0.00) (0.00) (0.28) Constant Yes Yes Yes Yes Number of Obs. 5509 6153 5451 3457 R-squared Overall 0.8450 0.9481 0.7416 0.6738 This table shows the regression of the de pendant Variable: Cash Holding (Cash/TA) on governance measure G-index, constraint measur e KZ index, and other control variables – Size (log of total assets), cash flow, Q, Leverage, NWC, CAPEX and ACQ. The construction of these variables is detailed in Table 1. 2-digit SIC codes are used as industry dummy. Finally the year dummies were also considered in order to control for fluctuations on account of passa ge of time. The sample of independent variables is restricted in sample size by grouping firms in brackets of Constrai nt, Non-Constraint, Good governance and Poor governance firms using KZ Governance dummy respectively.

PAGE 47

41 Harford at al. (2008) paper do not study th e role of financial constraints when analyzing the firm cash holdings in presence of firm governance. The results that the higher cash holdings for financia lly unconstrained firms as ag ainst financially constraint firms is true only when firms are well gove rned, is significant addition to existing literature. The other variables under column (4 ) of this table show that poorly governed firms lower the level of cash holdings when faced with choices of additional capital expenditures, acquisitions and in paying off some of the debt from its balance sheet. Table 11 tests the robustness of the results from the early tables, looking to see if the results are influenced by differing time period s of the sample or by industry affects. I examine time affects and industry affects as the results from tables 3 and 5 indicate variation in time and in industries. Column (1) of table 11 examines the time period from 1990—1997. This regression illustrates governan ce was a significant during the time period but to a lesser degree than shown in table 7. Column (2) of table 11 examines the period from 1998-2005. This regression demonstr ates a similar relation with governance but the magnitude is over double in size. Th e results from the time period analysis indicate governance has become more significa nt in terms of determ ining cash holding of firms. In column (3) of table 11, I conduct regr essions using fixed effects for industry (3). I find the use of fixed effects for industry does not significantly change the results reported from table 7. The results of this table indicate time period and industry does not change the relation between governance and cash holdings.

PAGE 48

42 Table 11. Robustness Tests – Time and Industry Variation Dep. Var: CH 1990-1997 1998-2005 Fixed Industry Macro_Adj Ind_Adj_ch (1) (2) (3) (4) (5) G-Index -0.0015 -0.0032 -0.0075 -0.0019 -0.0039 (0.00) (0.00) (0.00) 0.000 (0.000) KZ-Index -0.0004 -0.0006 -0.0005 -0.0001 -0.0001 (0.00) (0.00) (0.00) 0.000 (0.079) Size -0.0053 -0.0031 -0.0035 -0.0013 -0.0106 (0.00) (0.00) (0.00) 0.000 (0.000) Cashflow 0.2884 0.1631 0.1658 0.2693 0.1031 (0.00) (0.00) (0.00) 0.000 (0.000) Leverage -0.0269 -0.0187 -0.1139 -0.0431 -0.0058 (0.00) (0.00) (0.00) 0.000 (0.000) Q 0.0136 0.0164 0.0204 0.0057 0.0059 (0.00) (0.00) (0.00) 0.000 (0.000) NWC -0.0188 -0.0233 -0.0335 -0.0598 -0.0018 (0.00) (0.00) (0.00) 0.000 (0.000) CAPEX 0.1078 0.0896 0.2504 -0.0439 0.1044 (0.00) (0.00) (0.00) 0.000 (0.000) ACQ -0.0367 -0.0930 -0.1447 -0.0795 -0.0017 (0.04) (0.00) (0.00) 0.000 (0.000) GDP_Gr_Dummy -0.0057 0.000 Year Dummies Yes Yes Yes No No Industry Dummies Yes Yes No Yes No Constant Yes Ye s Yes Yes Yes Number of Obs. 8309 9278 17587 17587 17587 R-squared Overall 0.9633 0.7582 0.8448 0.4523 0.3031 This table shows the regression of the depe ndant variable Cash Holdings on governance measure G-index, constraint measure KZ index, and other control variables – Size, Cash Flow, Q, Leverage, NWC, CA PEX and ACQ. The construction of these variables is detailed in Table 1. 2digit SIC codes are used as industr y dummy in models (1), (2) and (3). The year dummies were considered in or der to control for fluc tuations on account of passage of time in models (1) and (2). Model (1) considers firm-years 1990-1997 and model (2) reflects results when 1998-2005 firm-years were considered. Model (3) controls for fixed industry effects. Model (4 ) controls for time variation by using GDP growth factor. Model (5) reflects regressi on results for firm-years by controlling for industry with mean adjusted values for cash holdings in place of 2-digit SIC dummy.

PAGE 49

43 In model (4) I replace the year dummies with a variable that reflects the macroeconomic condition over the sample period. I consider the GDP growth for every year in the sample period and construct a du mmy variable where in the value 1 captures above average growth in GDP and 0 value implies below average growth. Over the sample period the average GDP comes out to 2.96%. Based on the construct of the dummy variable explained above I observe th e GDP growth higher th an the average for years 1992, 1994, 1996-2000, 2004, and 2005 and vice-versa for the rest of the years. All the results of model (4) regr ession are consistent with ear lier findings. The GDP growth dummy variable is negative and significant m eaning that firms decrease cash holdings in years when US economy does better than averag e. In model (5) the primary regression is run with industry adjusted CH as the depende nt variable. The results of this regression agree with the model (3) results. Next, I a ddress the issue if any, with using Q as my proxy for growth opportunities. Si nce Tobin’s q has been used as a proxy in studies of the relationship between insider ownership a nd market-based performance, I construct another proxy variable for growth opportunities in place of Q and test for robustness of my primary tests. I use the ratio of R&D to total assets as my proxy for growth opportunities. The results are provided in Table 12. All the results of my earlier regression stand even with the new pr oxy variable for growth opportunities. The final robustness test for this study is carried out in two separate regressions with two different dependent variables but by including lags of key independent variables. The lag independent variables test ed here over the two separate regressions include the governance measure Gt-1, the cash holding variable CHt-1, the interaction between the lag cash holding variable and the good governance lag va riable(for Panel A

PAGE 50

44 shown in Table 13) and the lag of constraint measure KZt-1 as shown in Panel B of Table 13. In the first regression the dependent variab le considered is the financial constraint measure, the KZ index. Table 12. Robustness Tests: Using R&D / To tal Assets as a Proxy Measure for the Growth opportunities in place of Q Dependant Variable: Cash Holding (Cash / TA) (1) (2) (3) (4) (5) G-Index -0.0002 -0.0004 (0.05) (0.02) Strong Governance Dummy -0.0067 -0.0017 (0.00) (0.433) Poor Governance Dummy -0.0014 -0.0118 (0.66) (0.00) E-index -0.0163 (0.00) KZ-Index -0.0007 -0.0004 -0.0006 -0.0004 -0.0007 (0.00) (0.00) (0.00) (0.00) (0.00) Size -0.0059 -0.0038 -0.0055 -0.0043 -0.0071 (0.00) (0.00) (0.00) (0.00) (0.00) Cash flow 0.1553 0.1337 0.1542 0.1341 0.1584 (0.00) (0.00) (0.00) (0.00) (0.00) Leverage -0.0999 -0.2367 -0.0991 -0.2391 -0.0947 (0.00) (0.00) (0.00) (0.00) (0.00) R&D/TA 0.6333 0. 5066 0.6469 0.5253 0.5631 (0.00) (0.00) (0.00) (0.00) (0.00) NWC -0.1317 -0.1346 (0.00) (0.03) CAPEX 0.1281 0.1348 0.1328 0.1255 0.1298 (0.00) (0.00) (0.00) (0.00) (0.00) ACQ 0.0461 0.0021 0.0448 0.0006 0.0590 (0.00) (0.00) (0.00) (0.95) (0.00) Constant Yes Yes Yes Yes Yes Number of Obs. 8551 8551 8551 8551 8551 R-squared Overall 0.8771 0.8680 0.8778 0.8703 0.8630 This table shows the regression of the depe ndant variable: Cash Holdings (Cash / TA) on governance measures G-index in model (1) an d (2). Model (3) and (4) use strong and poor governance dummy by G-Index. Size, Leve rage, Cash flow, Net working capital, capital expenditures, Acquisiti ons, the financial c onstraint measure KZ index are being used as control variables in this regression. R&D/Total Assets is used as a proxy measure for growth opportunities in place of Q – used in all the earlier tests. The construction of these variables is detailed in Table 1. 2-di git SIC codes are used as industry dummy. Finally the year dummies were also considered in order to control for fluctuations on account of passage of time. Model (5) uses E-index in place of G-index. P-values are shown in parentheses.

PAGE 51

45 Table 13. Robustness Tests: Testing with Lag Variables Panel APanel B Dependant Variable :KZ : Cash holdings G t -1 0.880-0.0012 (0.000)(0.001) CHt-1 -22.6620.4465 (0.000)(0.000) CHt-1*Good_Gt-1 -7.190 (0.006) KZt-1 -0.00004 (0.003) Size -2.333-0.0012 (0.000)(0.000) Leverage 6.451-0.024 (0.002)(0.000) Cash flow -16.9060.1134 (0.000)(0.010) Q -0.0470.0353 (0.855)(0.000) NWC -3.769-0.054 (0.000)(0.000) CAPEX 7.0770.0961 (0.248)(0.000) ACQ -1.417-0.1486 (0.705)(0.000) Constant yesyes Number of Obs. 1468914074 R-Squared Overall 0.0800.8149 Panel A of this table shows the regression of the dependent variable KZ index on 1period lag variables: G-index, Cash holdings, and KZ index a nd the interaction variable of KZ index and cash holdings in lag form. Size, Leverage, Q, Cash flow, Net working capital, capital expenditures, and Acquisitions, ar e being used as control variables in this regression. P-values are shown in parentheses. Panel B of this table shows the regression of the dependant variable: Cash Holdings (Cash / TA) on 1-period lag variables: cas h holdings, governance measure G-index, and the financial constraint meas ure KZ index. Size, Leverage, Q, Cash flow, Net working capital, capital expenditures, and Acquisitions, ar e being used as control variables in this regression. This regression closely emulates Harford, et al. (2008) test. P-values are shown in parentheses.

PAGE 52

46 The results in Panel A of table 13 indicat e that with increase in G index (higher number implying poor governance), the KZ index increases (higher KZ index number implies constrained firm). In other words, we ll governed firms in the current time period tend to be less financially constrained in the next period. The lag of cash holding variable has a large and signifi cant negative coefficient. This result implies that increase in cash holdings in the curre nt time period significantly de creases the likelihood of firm being financially constrained in the next time period. This re sult is in agreement with the earlier results shown, and that financially unconstrained firm s tend to have higher levels of cash holdings. The next test variable is the interac tion of the lags of cash holdings and good governance variables. The coefficient is nega tive and significant at 6% level. This significant negative coefficient for the interaction term suggests that well-governed firms with higher cash holdings are less likely to ge t into financial trouble in the subsequent period. These results in combination with Ta ble 10 results adds to the significant understanding of the role of financial cons traints and governance pl ays in defining the nature of firm cash holdings. Next, as expected – larger firms, firms with increased cash flow and firms with increased net working cap ital expenditures (negative and significant coefficient) are less financiall y constrained while firms with more leverage (positive and significant coefficient) are mo re financially constrained. In the panel B of Table 13 the regression shown is similar to the Harford et al (2008) regression. The dependent variable here is the cash holdings and the key test variables including itself (cash holdings) are in lag form. Th e results of this paper are consistent with the results shown in the Harf ord, et al paper. A ne gative and significant

PAGE 53

47 coefficient variable for the lag G Index implies that poorly governed firms in the prior time period tend to hold lower level of cash holdings in the following time period. Firms that hold higher level of cash holdings and t hose that are less fina ncially constrained in the lag period and firms with positive grow th opportunities tend to hold higher level of cash holdings in the following period. These resu lts agree with the prior findings of this paper showing that well governed firms, less fi nancially constrained firms, and firms that have better growth opportunities tend to hold higher level of cash holdings. 1. 5 Conclusions In the presence of capital market impe rfections deriving from asymmetric information between managers and capital pr oviders, liquidity can take on a strategic role. The first interesting result that I find in this paper is firm s with strong governance tend to hold higher level of cash holdings as compared to poorly governed firms. It appears strong governance firms are able to mitigate agency issues associated with holding cash and lower the marginal costs of holding cash, thus increasing the value of holding cash. The reason for strong governan ce firms holding more cash is consistent with two benefits of holding cash. One reas on for firms with strong governance to hold more cash is when the firm has strong gr owth opportunities. Holding additional cash provides the firms better opportunities to take advantage of invest ment opportunities as they arise. Firms with strong governance are also sele ctive in increasing their cash holdings as firms with limited growth opportunities do not hold higher levels of cash. Another reason for firms with strong governance to hold mo re cash is to provide financial slack to avoid distress during downturns. Firms with strong governance hold more cash when

PAGE 54

48 financial constraints are low a nd cash is available. In contra st, poorly governed firms are indifferent to the financial condition of the firm with respect to its level of cash holdings and hold lower levels of cash in either scenario.

PAGE 55

49 Essay 2 Do Management Decisions Matter When Firms Are In Distress? 2.1 Introduction One aspect of economic theory argues th at competitive markets in transition to long-run equilibrium eliminate inefficient firms through the process of bankruptcy and liquidation. James (1995), Hotchki ss (1995), and Kahl (2002) argue that reorganization of firms after bankruptcy filings is pointless, as these firms are simply delaying an inevitable corporate death. Furthermore, White (1989) ar gues that managers voluntarily choose to keep the firm going instead of liquidating as a self-interested preser vation of their own jobs. This evidence seems to paint a dim pictur e of a firm’s ability to eventually work itself out of bankruptcy. In the twelve month period ended December 31, 2007, there were 28,322 businesses that filed for bankrupt cy, according to the U.S. federal court data8. As alarming as those numbers are, they are small in comparison to the number of firms that are in distress each year. Previous researchers have attributed manager ineffectiveness, poor timing, and lack of contingency planning (Hambrick and Schecter, 1983; Hofer, 1980; Schendel, Patton and Riggs, 1976) as the l eading indicators in the dec line of corporate performance leading to financial distress. Several author s have pointed to di fferent areas of the 8 Of these 28,322 businesses, 78 were publicly trading firms. The five largest bankruptcy filings from this list are real estate / mortgage-related financial compan ies. Further, the largest filing of 2007 (New Century Financial – Pre-petition assets: $26 billion) made it in to the 10 largest bankruptcies of all time. Source: BankruptcyData.com, a division of New Generation Research, Inc.,

PAGE 56

50 business that may increase firm performance while under distress9. Financial distress is typically a precursor to bankruptcy as many fi nancially distressed firms appear on future years’ list of bankrupt firms. Given the grim picture of bankruptcy, is financial distress any different? Are managerial decisions during distress effect ive in improving the firm's prospects? Anecdotal evidence shows that many well-known, financially strong firms have at one time been in severe financial distress10. Thus, even with the aforementioned dark view of a firm’s ability to re-establis h itself, many managers manage to pull their firms out of financial distress each year. In this paper, I look to examine if a fi rm’s success in leaving distress is explained by firm characteristics and manager decisions I primarily focus on two questions: how do some firms find their way out of distress while others do not, and what impact do managers’ decisions have on distress? To answers these questions, I examine the characteristics and manager financing and i nvesting decisions that lead firms out of distress and back to financial stability. To define manager investing and financ ing decisions affecting firms’ distress status, I start with the bankruptcy model as defined by Beaver (1966). “The firm is viewed as a reservoir of li quid assets, which is supplied by inflows and drained by out flows. The reservoir serves as a cushion or buffer against variations in the flows. The solvency of the firm can be defined in terms of the probability that the reservoir will be exhausted at which point the firm will be unable to pay its obligations as they mature (i.e. 9 Some of the many are (Bibeault 1982), operational restructuring (Kang and Shivdasani, 1997), asset restructuring on the lines of management buyouts (MBOs) (Kaplan, 1989), asset divestment (Kang and Shivdasani, 1997), asset investment (Bhagat, Moyen and Suh, 2005), acquisitions (Grinyer, Mayes and McKiernan, 1988), and financial restructuring (Slatter, 1984; John, Lang, and Netter 1992) 10 Xerox Corporation and Eastman Kodak are couple of notable examples. A more detailed discussion is given later.

PAGE 57

51 failure)”. Both the current ‘t ype’ and ‘timing’ of infl ows and outflows are largely influenced by the management’s prior time-peri od decisions. This being the case, then the current decisions of managers will influence the nature of future cash flows. I analyze managers’ decisions ex ante to study what type of decisions help distressed firms to come out of financial distress. The importance of manager actions unde r distress is not a brand new idea. Support for diverse management actions when in distress is found in Ofek (1993). Ofek explores the various responses of a firm in distress regarding both its operational and financial conditions, and analyz es why some distressed fi rms choose certain responses over others. Thus, they look at different ma nagement choices but not the success of the choice. Bhagat, Moyen, and Suh (2005) analyze both healthy and distressed firms. They focus on the investment policy for these two groups, and find a significant number of financially distressed firms have negative cash flow sensitivity. These findings suggest that managers of firms in distress invest more than they did in the prior year. However, their study does not examine how these actions influence whether or not distressed firms are able to get out of financial distress. Ka ne and Richardson (2002) show that firms that are more likely to get out of distress opt to reduce the size of their property, plant, and equipment. I extend this study further by an alyzing other changes managers make to increase the probability of getting out of the state of financial distress. To accomplish this goal, I first compile a sample of distressed firms. From the sample of distressed firms, I differentiate the financial status of a firm into two different state variables (State 1: Not distressed; and State 2: Distressed) in the future. Following Bhagat, Moyen and Suh (2005), I use Ohlson’s (1980) probabilistic prediction of

PAGE 58

52 bankruptcy measure to distinguish between th e above two mentioned states. Through this analysis, I can identify if firm characteristi cs and management decisions play a role in firms’ exiting financial distress, or if, similar to the results for firms reorganizing after bankruptcy, the managers have no re al impact on distress status. The results show firm characteristics are important in determining the future financial state of the firm. Size, leverage, a nd income at the time a firm enters financial distress are significant in determining if firms are able to exit distre ss. More specifically, larger firms with less leverage and higher income at the time they enter distress have a better chance of exiting distre ss within a three year peri od. The results on managers’ decisions show managers who increase th eir investment in product refinement by significantly increasing the rese arch and development expenditures help firms to get out of distress. I further explore the role of increase in resear ch and development investment in helping firms successfully tu rnaround. I find that the increa se in R&D helps firms with average and low growth opportunities. For fi rms with high growth opportunities simple increase in R&D investment does not help firm s to exit distress. I infer from this result that distress for high growth firms has less to do with its own product as compared to other factors discussed in lit erature that cause distress. Firms with average and low growth opportunities are usually found in well established industries and they have a higher leverage to communicate the changes to the product thus positively impacting future cash flows and hence exit distress. Wo rking capital investment is not significantly related to the future financia l state of the firm. In support of Kane and Richardson (2002), the findings also show that firms lowering th eir level of capital expenditures are more likely to exit distress. This evidence is consis tent with the argument that cost cutting and

PAGE 59

53 trimming firm operations to pr ovide additional funds for re search and development can contribute significantly towards a successful turnaround. The result s also show the inability of changes in financing choices to move a firm out of distress. Firms selling common or preferred stock to raise money, as also shown in Bhagat et al. (2005), does not increase the likelihood of firms’ exiting distress. So, why does it matter which firms make it out of distress? In addition to the intuitive answer, the importance of leaving fi nancial distress can be explained in more than one way. As far as the indirect costs of bankruptcy are con cerned, the costs keep increasing as a firm sinks deeper into dist ress and results in the loss of reputation and potential drop in sales due to poor financ ial performance. I fo cus on shareholders’ primary concern, stock returns. As expect ed, the returns for the sample of firms remaining in distress are signi ficantly lower than those successfully exiting distress during the three year period. Firms leaving dist ress have three-year holding period returns around 37 percent points higher than their counterpa rts unable to make it out of distress. The remainder of the paper is structured as follows. Section II highlights issues of the macroeconomic conditions from the recen t past in regards to firms experiencing performance decline and their attempts at turnaround. Secti on III details the relevant literature leading up to this study. Section IV presents a discussion of the Ohlson’s measure used for distinguishing the distresse d versus non distressed samples. Section V offers information on data collection and further identifies the control and the test variables used in the study. S ection VI presents a discussi on of results and Section VII concludes.

PAGE 60

54 2.2 Background: Firms Exiti ng Financial Distress Many firms find their way out distress by making key corporate decisions. Schefenacker, which makes mirrors for carmakers such as BMW and Mercedes, when faced with serious financial distress in late 2006, emerged from a tortuous restructuring, moved their headquarters, downsized its de bt by 47%, and its founder gave up threequarters of his shares to creditors. Do wnsizing of workforce (General Motors); restructuring of capital struct ure (Meridian); asset sale (Ford); and change in top management (Citigroup, Merrill Lynch) are a few recent examples of how managements of different firms have reacted to performance declines. The related literature has so far focused on three forms of exit strategies for distressed firm s – resolution through bankruptcy filing, voluntary liquidation, and me rger and acquisition. These exit strategies invariably result in losses for the stockholders of the fi rm. White (1983) emphasizes how equity holders always favor cont inuance since their interest is eliminated if liquidation is chosen. Hence, a sizable number of distre ssed firms choose alternative strategies to combat distress and continue to keep the owner’s control over the firm. This paper analyzes the strategies employed by firm ma nagement that have preserved and helped grow shareholder’s wealth afte r the financial distress phase. 2.3 Literature Review The supporting literature for this paper can be categorized into three distinct themes. The first is the financial distress literature which has focused largely on explaining11 and measuring the costs of distress12. The next significant research area tied 11 Direct Costs: From the capital st ructure perspective significant costs on account of financial distress for stockholders in the form of legal and administra tive costs of restructuring the firm’s debt.

PAGE 61

55 to this paper addresses topics such as turn around and recovery in th e face of declining performance. Turnaround is closely associated with management strategy, as researchers explored various mechanisms employed by managers attempting turnarounds. 2.3.1 Financial Distress Altman (1983) introduces corporate distre ss by including with it the legal process of corporate bankruptcy reor ganization and liquidation and describes it as “a sobering economic reality reflecting the uniqueness of the American way of corporate death.” In contrast, Wruck (1990) states categorically that “financial distress – is not synonymous with corporate death.” She finds that firms in financial distress face a variety of situations having very different effect s on their values and claimholders. This diversity in conjunction with conflicts of interest am ong claimholders, leads to an information problem that makes valuing a distressed fi rm difficult. Interestingly, Wruck (1990) identifies the ‘upsides’ associated with financ ial distress. She states that financial distress is often accompanied by comprehensive organizational changes in management, governance and structure. This organizati onal restructuring can create value by improving the use of resources. Financial dist ress frees resources to move to highervalued uses by forcing managers and director s to reduce capacity a nd to rethink operating policies and strategy decisions. This kind of or ganizational change is unlikely to occur in an all-equity firm, because without leverage poor performance does not lead to financial distress. It is financial distress that gives creditors a legal right to demand restructuring. Indirect costs: The opportunity loss suffered when co rporate resources are diverted to debt restructuring process from more productive uses (reviewed by Myers (1984) and Masulis (1988)) Managerial financial distress costs: Gilson (1989) shows that 52% of all sampled firms experience a seniorlevel management change during the period of financial distress 12 Warner (1977) measures the direct costs as result of bankruptcy; Ang et al (1982) attempt to measure the administrative costs as a result of Bankruptcy.

PAGE 62

56 Kaplan (1989) analyzes financially distre ssed firms that subsequently complete management buyouts over the period from 1985 through 1989. They find a higher incidence of default on their debt ex-post the buyout decision. De nis and Denis (1995) analyze the causes and resolutions of financ ially distressed firms by examining a sample of 29 leveraged recapitalizati ons completed between 1985 and 1988. Interestingly they do not find a higher rate of asset sales among th e distressed firms and, when asset sales do occur, the market participants treat this news as a negative signal. 2.3.2 Management Strategy Bracker (1980) reviews the historical de velopment of the strategic management concept and discusses the many definitions of strategy offered by various researchers13, as related to the business world. He states that “The major importance of strategic management is that it gives organizations a framework for developing abilities for anticipating and coping with change.” Schendel and Patton (1978) work out a simultaneous equation model of corporate stra tegy. They refer the strategic concept to multiple levels: the corporate level, the bus iness level, and the functional area level. Hofer (1980) discusses two types of cor porate turnaround strategies: strategic and operating. His discussion leads to a conclu sion that strategic turnarounds most often involve a significant shift in the nature of the business. Managers adopting operating turnaround strategy refocus their energies on the core business by choosing to emphasize 13 Few examples: (i) Strategy is a series of actions by a firm that are decided on according to the particular situation – Von Neumann & Morgenstern, 1947; (ii) Strategies are directional action decisions which are required competitively to achieve the company’s purpose. – Cannon, 1968; (iii) Strategies are forwardlooking plans that anticipate change and in itiate action to take advantage of opportunities that are integrated into the concepts or mission of the company – Newman & Logan, 1971; (iv) Strategy is concerned with long-range objectives and ways of pursuing them that affect the system as a whole. – Ackoff, 1974; (v) Strategy is a meditating force between the organization and its environment : consistent patterns in streams of organizational decisions to deal with the environment – Mintzberg – 1979.

PAGE 63

57 one of the four following areas: increasing re venues, decreasing costs, decreasing assets, or a combination effort. 2.3.3 Turnarounds Schendel et al. (1975) studi ed 54 firms each with four consecutive years of earnings decline and then subsequently four consecutive years of earnings improvement. They use information from business periodical s regarding these firms to study the causes of decline and actions taken for successful turnaround. They subjectively rate the causes for decline and actions taken for turnaround a nd classify each as either strategic or operating in nature. Hofer (1980) applied simila r logic to his research. His analysis of 12 poorly performing firms showed firms th at became distressed on account of poor strategic decisions successively had “strategic” turnarounds. T hose firms’ whose cause of distress resulted from poor ope rating decisions made “opera ting” turnarounds. Bibeault (1982) surveyed 81 chief executives who had f aced turnaround situations. His discussions with the professionals attempting turnarounds ad d invaluable insights in to issues such as leadership aspects as well as organizational and human issues. In his view, the primary objectives for the financially distressed firm are survival and achievement of a positive cash flow. Hambrick and Schecter (1983) argued against the dichotomy of classifying turnaround actions as “strategic” and “ope rational” since the distinction between classifications have blurred. The sample fo r their study is drawn from PIMS database14. The target sample was the available data ove r four years on all matu re industrial-product 14 Profit Impact of Market Strategies (PIMS) is a larg e scale statistical study of environmental, strategic, and performance variables of individual business units.

PAGE 64

58 businesses15 in the PIMS database. A total of 260 businesses met their required criterion for low performance including those that subsequently made performance improvements. Their cluster analysis indicat ed three primary successful tu rnaround actions: asset/cost surgery, selective product/market pruning, a nd a piecemeal strategy. Robbins and Pearce (1992) address the turnaround process in term s of retrenchment and recovery. In the retrenchment phase, they hypot hesize firms seek to stab ilize declining performance through reductions in costs and fixed asse ts. In the recovery phase, systematic investments are made to stimulate fina ncial improvement. Their research design constrained their sample to firms that faced reasonably similar operating and competitive conditions. Their sample consisted of fi rms belonging to a single specific industry (Textile). They concluded that successful tu rnarounds were often a result of efficiency moves rather than of product-market ch anges or of market share increases. Hoshi et al. (1990) find the financially di stressed group of firms invest more and sell more than non-group firms in the years following the onset of financial distress; Asquith et al. (1994) analyze firms that issued junk bonds in the 1970s and 80s and subsequently experienced financial trouble. Consistent with Ofek (1993), their study shows that distressed firms undertake restruct uring primarily by selling assets. Sharpe (1994) shows a statistically a nd economically significant re lationship between a firm’s financial leverage and the cyclicality of its labor force. He shows that firms that experience relatively high opportunity costs of capital during cyclical downturns are prone to reduce employment so as to conserve their working capital at such times. John et 15 A mature business is defined by PIMS as one in an industry whose real growth is less than 10 percent annually, in which most potential buyers understand the product, and whose set of competitors is well known.

PAGE 65

59 al. (1992) study a sample of large firms (firms with assets exceed ing $1 billion) with a performance decline in the sample period (at least one year of negative earnings (19801987), followed by at least 3 years of positive earnings). They find strong evidence of changes in operations and investment to th ese performance shocks. These changes they find are result of voluntary actions by the firm managers and not in r eaction to a threat of change in corporate control. Their st udy suggested that the firms retrenched16 quickly, and on average concentrated their focus. In the year following ne gative earnings, average employment fell by about 5% and the averag e number of business segments declined. The arguments presented by the above two pape rs and others suggest that firms with declining performance often c hoose to sell assets as they go through the restructuring process. Shleifer and Vishny (1992) di scuss the nature of asset il liquidity especially in the context of distress firms. They argue that when firms have trouble meeting debt payments and sell assets, the highest valu ation potential buyers of these assets are likely to be other firms in the same industry. But with the possibility of a contagion effect, these firms themselves are likely to have trouble mee ting their debt payments. The other probable group of buyers of these assets, industry out siders, would face agency costs of hiring specialists to run these asse ts and may fear overpaying for lack of proper knowledge of the assets characterist ics. Hence when industry buyers cannot buy the assets and industry outsiders face significant cost s of acquiring and managing the assets, assets in liquidation fetch prices below value in best use, whic h is the value when managed by specialist. 16 Defined as reduction in firm assets and/or costs.

PAGE 66

60 In addition to asset sales and layoffs, capit al expenditure reductions also play a role in restructuring of a distressed firm. In examining financially distressed companies that previously issued high yield junk bonds, Asquith et al. (1994) show that eighty-three percent of firms reduce capital expenditures from the year before the onset of distress to the year after. Andrade and Kaplan (1998) examine the investment behavior of financially distressed firms that remain in good economic health. They find that firms in financial distress but in good economic hea lth decrease their capital investment expenditures, sell assets at depressed pri ces, but do not undertake riskier investment projects. Kane and Richardson (2002)’s sample of financial distress firms include firms that have high likelihood of impending failu re but have not yet filed for bankruptcy protection. They consider such firms to no l onger be ‘going concerns’. Their focus is primarily on two potentially mitigating actio ns – growth or contraction of plant investment and the likelihood that either ac tion will lead to emergence from financial distress, thereby reducing the risk of corporate failure. They conclude in favor of contraction stating that disinve stment increases the likelihood of the firm getting out of distress. Cleary et al. (2004) develop a model of a U-shaped relation between investment and internal funds. As is standard, the fi rm invests less when it faces a decrease in internal funds. For low levels of internal f unds, however, the firm must invest more to generate enough revenues to m eet its contractual obligations Investments therefore form a U-shape over all internal fund levels. Moye n (2004) also graphs a U-shaped relation between investment and cash flows for uncons trained firms. In bad conditions, firms

PAGE 67

61 invest more to generate more revenues next period, thereby decrea sing the probability of defaulting and paying default costs. Bhagat et al. (2005) document negative cash flow sensitivity for distressed firms with operating losses and a positiv e sensitivity for all other firm s. They also show that the negative cash flow sensitivity is generated by distressed firm s with operating losses that invest more than the previous years. These firms invest more when their cash flows are decreasing. They claim that this additional i nvestment is made on account of funds raised by equity claimants and infer this as eviden ce of a gamble for resurrection. They also provide evidence consistent w ith an asset substitution problem only for the subset of financially distressed firms with operating losses that invest more than the previous year. 2.4 The Sample and Variables 2.4.1 The Sample Selection Process The initial sample consists of all companies drawn from COMPUSTAT during the period 1989 to 2001 that had financial data available for six con tiguous fiscal years. Three consecutive years of data is required to compute the measure of distress. The following three consecutive year restriction is needed to test the fi nancial condition three years hence. The returns data is extrac ted from the CRSP database. Using Ohlson’s methodology, probabilistic predictions of bankruptcy are computed for each firm year in the total sample. Following Bhagat, et al. ( 2005) firms with 50% or greater probability are counted as financially distre ssed firms. The total sample size of all distressed firms is 18,434 firm-years. The rest of the sample for wh ich the probability of distress is less than 50% is treated as non-distressed firms for th e respective years for which the probabilities are computed. The total sample size of a ll non distressed firms is 30,948 firm-years.

PAGE 68

62 Schendel et al., (1975) provide a concrete definition of upturn as four consecutive years of increasing profits. Among Hofer’s (1980) successful turnarounds, the average elapsed time from trough to peak was three y ears. Bibeault(1982) noted that the time required for a turnaround is a f unction of the size of the orga nization: “Altogether, we are talking about anywhere between one and th ree years, with a $20 million company taking one year and a company the size of Memore x taking three years”. For this study, I am considering turnaround as a firm in distress in year t and out of di stress t+3 years hence. Thus, for each year the firms are in distre ss, the measure of distress (O-score) is recomputed for all such firms three years hen ce. These two steps lead to the formation of my sample of firms that ‘remain in distress ’ and firms that had ‘successful turnaround’. This database now reflects each firm’s financial position as healthy or in distress after the three year measurement period, identifying if the distress firm made a recovery three years after being classified as distressed. A total of 3,050 firm-years or 16.5% of the total distressed sample successfully completed a tu rnaround three years after entering distress. The control and test variables were also obtained from COMPUSTAT. 2.4.2 Control Variables The empirical literature cite d earlier with regard to turnaround strategies suggests that suitability and effectiveness of turn around strategies are dependent on certain intrinsic factors. These factor s are relevant to a firm’s fi nancial condition and impact the direction and intensity of dec line or recovery. They are gene rally not altered significantly in magnitude by manager’s actions in a short duration. Size is computed as log of total assets (Item #6); Leverage is computed as to tal liabilities divided by total assets (Item #181/#6); Operating Income standardized by total assets (item #13/#6); growth

PAGE 69

63 opportunities for the firm proxied by Tobin’s Q – and computed as market value of assets divided by book value of assets ({(#199 #25) + #6 – (#60 + #74)} / #6) and cash holdings (#1). 2.4.3 Test Variables The objective of this paper is to obs erve and identify those distinguishing managerial actions that c ontribute significantly towards a successful tu rnaround of a distressed firm. Based on the assumption that significant changes in managerial decisions will be reflected in the financial statements I compute the changes observed in certain specific variables and analyze th eir impact on the firm’s health three years hence. A time period of three years is allowed to pass by to study the impact of those decisions – a justifiably conservative approach for the ma nager’s actions to work through the firm’s operations. These specific factor s have been addressed in earlier papers but either have yielded contrary results or have not been explained in regard s to recovery from distress. For firms in distress, changes are observed a nd recorded for these following factors: (i) Change in acquisitions (item #129) ((Grinyer, Mayes and McKiernan, 1988), (ii) Change in capital expenditure (item #128) (Kang a nd Shivdasani, 1997, Bhagat, Moyen and Suh, 2005), (iii) change in working capital (ite m #4-#5) (Kang and Sh ivdasani, 1997), (iv) change in equity (item #216) (Slatter, 1984; John, Lang, and Netter 1992), (v) change in research and development (ite m # 46) (Guerard et al., 1987)17 and (vi) change in net income (item #172) (Schendel et al., 1975). Tabl e 14 discusses the variables to be used in this paper. 17 Guerard et al, (1987) model R&D expenditures as a function of previous years’ R&D expenses and hence use the raw changes in R&D expenditures when analyzing the corporate financial policy.

PAGE 70

64 Table 14. Variable Construction S.No Variable Notation Variable Description Compustat Notation 1. Size Natural logarithm of total assets Ln(#6 t ) 2. Cash Cash Holdings #1 t 3. Tobin’s Q Market value of assets / Book Value of assets {(#199 t #25 t) + #6 t – (#60 t + #74 t )} / #6 t 4. Leverage Total Debt / Total Assets #181 t / #6 t 5. Operating Income. Operating Income/Total Assets #13 t /#6 t 6. O-Score Yit = -1.32 .407*(ln(TA)) + 6.03*TLTA – 1.43*WCTA + .757*CLCA – 2.37*NITA – 1.83*FUTL + .285*INTWO – 1.72OENEG .521*CHIN Ln(TA) Ln(#6 t ) TLTA #181 t /#6 t WCTA (#4 t #5 t ) / #6 t CLCA #5 t /#4 t NITA FUTL INTWO OENEG CHIN #172 t /#6 t #110 t /#181 t 1 if #172t & #172t-1 < 0; 0 otherwise. 1 if #181 t > #6 t; 0 otherwise. (NIt – NIt-1)/(|NIt| |NI t -1|) 7. Chg in R&D R&Dt+1 – R&Dt #46t+1 #46t 8. Chg in CAPX Capital Expenditurest+1 – Capital Expenditurest #128t+1 #128t 9. Chg in Acq. Aquisitionst+1 – Acquisitionst #129t+1 #129t 10 Chg in WC Working Capitalt+1 – Working Capital t (#5-#4)t+1–(#5-#4) t 11. Chg in Equity Shareholders’ Equityt+1 – Shareholders Equityt #216t+1 #216t 12. Chg in NI Net Incomet+1 – Net Incomet #172t+1 #172 t 13. Returns Annual returns CRSP Database This table details the construction of the di stress variable (based on the probabilistic predictions of bankruptcy as derived from Oh lson’s score), control variables, and test variables. 2.5 Measures of Financial Distress The goal is to use a method that reflects decline in performanc e leading to varying degree of financial distress. Pr ior research has considered negative net income as a sign of financial trouble with most of them having considered more than one year of negative net income to classify a firm as financiall y distressed. Some other methods applied to

PAGE 71

65 capture the financial distress status of firms are as follows: Fazzari et al. (1988) consider firms with negative real sale s growth as financially di stressed firms. Wruck (1990) defines financial distress as a situation wher e cash flow is insufficient to cover current obligations. Hoshi et al. (1990) assume a firm is approachin g distress when the ratio of operating income to interest expense (int erest coverage) falls below one. Asquith, Gertner, and Scharfstein (1994) define financial distress in the most fundamental way, i.e., liquidation value of a firm’s asset is less than the face value of the firm’s liabilities. Ofek (1993) constructs a distressed firm sample by including firms that experience a year of average or above average performance (b ase year) followed by a year of very poor performance (distress year), defined as annua l stock returns in the bottom decile of the market. Opler and Titman (1994) identify i ndustries that have experienced economic distress and differentiate firms in those industr ies based on their levera ge ratios. A 3-digit SIC industry is defined as bei ng economically distressed when its median sales growth is negative and when it experiences median stoc k returns below -30%. Ciccone (2001) uses proxy for financial distress with a bottom line focus. He considers a firm in financial distress if it has losses, (i.e. earningst < 0), and earnings dec line (i.e. actual annual earningst < actual annual earningst-1). In addition to the above measures a more comprehensive way to classify firms on the continuum is to compute the probabil ity of a firm becoming bankrupt. The two methodologies most commonly applied in the li terature are accounting-based measures of distress risk – (i) Z-score (Altman, 1968) a nd (ii) O-score (Ohlson, 1980). Altman (1968) investigates a set of financial ratios in bankruptcy prediction context using a multiple discriminant statistical methodology. Ohlson (1980) uses maximum likelihood estimation

PAGE 72

66 of the so-called conditional lo git model to predict corporat e failure as evidenced by the event of bankruptcy. They identify four basic factors as being statis tically significant in affecting the probability of failure within one year – (i) size; (ii) measures of financial structure; (iii) measures of performance; and (iv) measures of current liquidity. In this paper, I use Ohlson’s probabilis tic prediction of bankruptcy measure very closely imitating Bhagat, Moyen and Suh’s ( 2005) use of the same measure to identify firms in performance decline. This measur e is based on Ohlson’s predicted bankruptcy probabilities p, where P = 1 / (1 + e-Yit) Yit = -1.32 .407*(ln (TA)) + 6.03* TLTA – 1.43*WCTA + .757*CLCA – 2.37*NITA – 1.83*FUTL + .285*INTWO – 1.72OENEG .521*CHIN Where TA is total assets (COMPUSTAT #s in parentheses) (#6); TLTA is total liabilities to total a ssets (#181/#6); WCTA is the ratio of working capital to total assets [(#4 #5) / #6]; CLCA is the current liabilities to current assets ratio (#5/#4); NITA is net income to total assets ratio (#172/#6) and FUTL is fund from operations to total liabilities ratio (#110/#181). INTWO = 1 if net income (#172) is negative in prev ious two years or zero otherwise; ONEEG = 1 if to tal liabilities (#181) is great er than total assets (#6), 0 otherwise and CHIN is the rati o of the difference in net inco me of the current period with the previous period over the absolute value of the difference. (NIt – NIt-1)/ (|NIt| |NIt-1|).

PAGE 73

67 Table 15A. Non-Distressed vs. Dist ressed Firms Summary Statistics Non-Distressed Firms Distressed Firms Variable Obs. Mean Obs. Mean Difference Total Assets 30,948 5.4176 18,434 4.3947 1.0229*** Leverage 30,948 0.3911 18,434 0.6976 -0.3065*** Operating Income / TA 30,948 0.1239 18,434 -0.0429 0.1668*** Q 30,948 2.1071 18,434 2.3176 -0.2104 Cash 30,948 186.0336 18,434 51.9170 134.12*** O-Score 30,948 -1.9044 18,434 1.3788 -3.2832*** Ohlson’s probabilistic prediction of bankruptcy measure is used to distinguish between healthy or distressed status. The measur e predicts bankruptcy probabilities p, where P = 1 / (1 + e-Yit) Yit = -1.32 .407*(ln(TA)) + 6.03*TLTA – 1.43*WCTA + .757*CLCA – 2.37*NITA – 1.83*FUTL + .285*INTWO – 1.72OENEG .521*CHIN Wherein TA is total assets (COMPUSTAT #s in parent heses) (#6); TLTA is total liabilities to total as sets (#181/#6); WCTA is the ratio of working capital to total assets [(#4 #5) / #6]; CLCA is the curr ent liabilities to current assets ratio (#5/#4); NITA is net income to total assets ratio (#172/#6) and FUTL is fund from operations to total liabilities ratio (#110/#181). INTWO = 1 if net income (#172) is negative in prev ious two years or zero otherwise; ONEEG = 1 if to tal liabilities (#181) is greater than total assets (#6), 0 otherwise and CHIN is the ratio of the differe nce in net income of current period with previous period over the absolute value of the difference. (NIt – NIt-1)/(|NIt| |NIt-1|). Size is computed as log of total assets (Item #6); Leverage is computed as total liabilities divided by total as sets (Item #181/#6); Operating In come standardized by total assets (item #13/#6); Growth opportunities for the firm pr oxied by Tobin’s Q – and computed as market value of assets divide d by book value of assets ({(#199 #25) + #6 – (#60 + #74)} / #6), Cash holdings (#1). Following Bhagat, et al. (2005), this m easure is obtained from a variant of Ohlson’s bankruptcy probability model. Because the FUTL variable greatly restricts the sample size, pseudo-bankruptcy probabilities, p, are calculated by i gnoring the effect of FUTL in predicting bankr uptcy probabilities: p = 1/ (1 + e-Yit) Firms with declining performance and faci ng financial distress include firm-year observations with pseudo-bankr uptcy probabilities greater than or equal to 50%. Panel A

PAGE 74

68 of Table 15 compares the distressed sample used in this paper to a sample of nondistressed firm. This table justifies the choi ce of the distress measure (O-score) used in this paper in determining and labeling firms’ financial condition as distressed or not distressed. I look at some important firm va riables and see if the O-score appears to predict firms with distressed characteristics. By comparing the sample of firms not considered in distress with the firms that are considered in distress, we see the distressed sample appears to be accurately predicted by O-score. I further examine control variable to identify differences between a typical firm in distress as compared to a firm not in distress. Total asset size is a key variable in determining the distress level of firms, as shown by Gilchrist and Himmelberg (1995). The size of the firm is a proxy for both the flexibility and internal slack available to the declining firm. The firms in distress are expected to be of smaller si ze and this is observed using a measure of size, which is the natural log of total assets. Leverage is also a key determinant in distress level (Kaplan and Stein, 1993). It is expected that firms with higher leverage, on average, to be in distress more often. Consistent with this pred iction the firms in distress have doubled the amount of leverage as compared to n on-distressed firms in the sample. Operating income is also an important determinant in figuring out firm distress level (Ciccone, 2001). Firms with lower and especially negative operating income on average will find themselves in distress. C onsistent with the idea, firms identified as distressed have a negative operating income on average, while the non-distressed firms in the sample have positive operating income. A firm’s Q, as a proxy for investment opportunities, does not appear to affect the fi rm’s distress level (Opler and Titman, 1993).

PAGE 75

69 Table 15B. Non-Distressed vs. Distre ssed Firms – Industry Concentration Distressed Firms Non-Distressed Firms SIC Code % of sample SIC Code % of sample Business Services 73 10.89% 73 9.15% Chemicals and Allied Products 28 7.27% 28 9.38% Electric, Gas and Sanitary Services 49 6.85% 49 3.64% Electronic And Other Electrical Equipment And Components, Except Computer Equipment 36 6.72% 36 9.92% Industrial And Commer cial Machinery And Computer Equipment 35 6.34% 35 7.75% Measuring, Analyzing, And Controlling Instruments; Photographic, Medical And Optical Goods; Watches And Clocks 38 5.92% 38 8.04% Oil And Gas Extraction 13 4.94% 13 3.53% Communications 48 4.36% 48 2.43% Wholesale Trade-durable Goods 50 2.80% 50 2.68% Eating And Drinking Places* 58 2.59% Food And Kindred Products* 20 3.01% The industry concentration for the top ten industries of distressed and non-distressed firms, respectively, are measured and repor ted here. A total of 18,434 firm-year sample size of distress firms and 30,948 firm-year non-dist ressed firm sample si ze is considered for the sample period of 1989-2004. Industry cla ssification and description information is gathered from U.S. Department of Labor Occupational Safety & Health administration website manual. http://www.osha.gov/pls/imis/sic_manual.html Only top ten industries for distressed and non-distressed firms shown here. Specifically, distressed firms have higher investment opportunities as defined by Q but not by a significant amount Distressed firms hold a fraction of the cash as firms not in distress. John (1993) argues that firm s hold more cash as avoidance to financial distress. Panel B of Table 15 examines the i ndustry concentration of firms not in distress versus the industry concentration of firms that are in distress. The panel demonstrates that some industries have a higher probability of having firms in distress. The industry SIC

PAGE 76

70 code with the highest concentration for the distressed sample is SIC code 73 (Business Services). This SIC code makes up almost 11 pe rcent of the total distressed sample, while making up approximately 9 percent of the tota l non-distressed sample. In contrast, SIC code 36 (Electronics and other Electrical Equipment Components) makes up 6.72 percent of the distressed sample but almost 10 percen t of the non-distressed sample. These results demonstrate the variation among different indus tries and the different levels of risk among industries (Hou and Robinson, 2006). Certai n industries have a higher probability of firms entering distress than others. This highlights the need fo r industry controls. 2.6 Empirical Results 2.6.1 Sample Statistics and Univariate Tests Table 16 examines the sample statistics for the whole sample of firms in distress for the given year, the firms that made it out of distress in a three year period and the sample of firms that did not make it out of distress in three years. The sample of firms that made it out of distress repr esents about 15% of the entire sample of firms in distress, while the remainder are firms that did not exit distress in the three year period. I compare the difference between the variables for the two types of firms to examine if these variables appear to estimate the successful exit from distress. The results indicate firms making it out of distress in the three year period have significant differences on average than firms remaining in distress. Firms getting out of distress in a three year time period are larger in size as measured by total assets, have less leverage, and have higher cash holdings.

PAGE 77

71 Table 16. Sample Statistics Variable Full Sample Out of Dist ress Still in Distress Difference Observations 18,434 3,055 15,379 -12,324 Size 4.3947 4.5577 4.3624 0.1953*** Leverage 0.6976 0.6204 0.7129 -0.0924*** Oper. Income / TA -0.0429 -0.0152 -0.0484 0.0332** Q 2.3176 2.1014 2.3604 -0.2590 Cash 51.9170 77.6547 46.8130 30.841*** Chg in R&D 0.8920 3.4126 0.2509 3.1617** Chg in CAPX 0.5504 -5.3226 2.0408 -7.3634*** Chg in Acq. -3.0196 -2.8534 -3.0618 0.2085 Chg in WC 8.7819 12.0342 7.9548 4.0795*** Chg in Equity -1.3049 0.0682 -1.6542 1.7223 Chg in NI 18.6210 39.4163 13.3320 26.084*** Ohlson’s probabilistic prediction of bankruptcy measure is used to distinguish between healthy or distressed firm status. The meas ure predicts bankruptcy probabilities p, where P = 1 / (1 + e-Yit) Yit = -1.32 .407*(ln(TA)) + 6.03*TLTA – 1.43*WCTA + .757*CLCA – 2.37*NITA – 1.83*FUTL + .285*INTWO – 1.72OENEG .521*CHIN Wherein TA is total assets (COMPUSTAT #s in parent heses) (#6); TLTA is total liabilities to total as sets (#181/#6); WCTA is the ratio of working capital to total assets [(#4 #5) / #6]; CLCA is the curr ent liabilities to current assets ratio (#5/#4); NITA is net income to total assets ratio (#172/#6) and FUTL is fund from operations to total liabilities ratio (#110/#181). INTWO = 1 if net income (#172) is negative in prev ious two years or zero otherwise; ONEEG = 1 if to tal liabilities (#181) is greater than total assets (#6), 0 otherwise and CHIN is the ratio of the differe nce in net income of current period with previous period over the absolute value of the difference. (NIt – NIt-1)/(|NIt| |NIt-1|). Size is computed as log of total assets (Item #6); Leverage is computed as total liabilities divided by total assets (Item #181/#6); Operat ing Income standardi zed by total assets (item #13/#6); Growth opportunities for the fi rm proxied by Tobin’s Q – and computed as market value of assets divided by book value of assets ({(#199 #25) + #6 – (#60 + #74)} / #6), Cash holdings (#1). The change variables are all computed as a first difference year (t+1) – (t). The year t is the year in which firm is identified as being in distress. The change variables are computed for Change in acquisitions (item #129); Change in capital expenditure (item #128); change in working capital (item #4-#5); change in equity (item #216); change in research and development (item # 46); and change in net income (item #172)

PAGE 78

72 The next set of variables focus on change s made in the first year after a firm enters distress. The average change in R&D is significantly higher for firms getting themselves out of distress during the three year period. Thus, this finding suggests that distressed firms that increase their investme nt in R&D have a greater incidence of turnaround. Changes in other types of investme nts do not share this positive relation with successfully exiting distress. For example, changes in capital expenditures (CAPEX) are negative, as firms leaving the distress group have lower capital e xpenditures on average. Acquisitions do not appear to affect firms’ ab ility to get out of di stress, as change in acquisition level is not signi ficantly different for the two samples. Working capital measures the firm’s net position in liquid a ssets. Change in working capital is much higher for firms getting out of distress (Fazzari and Petersen 1993). Selling equity does not appear to be different among the two groups, as they do not have a significant difference in changes in equity. The change in yearly net income is much greater for firms getting out of distress. Table 17 provides a correlation matrix fo r all variables to be used in the regressions. This table examines the correlati on between the independe nt variables. I find that the correlation coefficient between most of the variables is fairly low. One coefficient that does provide some concern is the relation of le verage and operating income standardized by assets This variable has a correl ation coefficient of 0.4225. This high correlation may cause issues in the regr essions, so I run the main regression with and without both variables at the same time. Variance Inflation Factor (VIF) test were also run to analyze further if any multicoll inearity existed among independent variables.

PAGE 79

73 Table 17. Correlation Matrix Size Leverage Income/TA Q Chg in R&D Chg in CAPX Chg in Acq. Chg in WC Chg in Equity Chg in NI Size 1 Leverage 0.0015 1 (0.8343) Income/TA 0.2095 -0.4225 1 (0.0000) (0.0000) Q -0.0954 0.1056 -0.1657 1 (0.0000) (0.0000) (0.0000 Chg in R&D 0.0269 0.0031 0.0077 0.0006 1 (0.0008) (0.7001) (0.3386) (0.9440 Chg in CAPX 0.0100 0.0013 0.0042 0.0005 0.2959 1 (0.2151) (0.8769) (0.6047) (0.9470 (0.0000) Chg in Acq. -0.0491 -0.0024 -0.0007 0.0032 0.0154 0.0156 1 (0.0000) (0.7632) (0.9319) (0.6931 (0.0560) (0.0538) Chg in WC 0.0348 0.0047 0.0047 0.0009 0.1017 -0.1982 -0.0589 1 (0.0000) (0.5593) (0.5563) (0.9081 (0.0000) (0.0000) (0.0000) Chg in Equity -0.0151 -0.0074 0.0031 -0.0017 -0.0379 0.0537 0.1801 -0.0589 1 (0.0608) (0.3573) (0.6994) (0.8341 (0.0000) (0.0000) (0.0000) (.0139) Chg in NI 0.0997 0.0133 0.0006 -0.0024 0.1137 -0.1750 0.0175 0.2041 -0.0620 1 (0.0000) (0.0989) (0.9417) (0.7696) (0.0000) (0.0000) (0.0000) (0.0356) (0.0000) This table provides data on the correlations between certain variable measures The dataset is comprised of 30,948 firm year observations covering the period 1989 through 2 004. Size is computed as log of total asse ts (Item #6); Leverage is computed as total liabilities divided by total a ssets (Item #181/#6); Operating Income standardiz ed by total assets (item #13/#6); Growth opportu nities for the firm proxied by Tobin’s Q – and computed as market va lue of assets divided by book valu e of assets ({(#199 #25) + #6 – (#60 + #74)} / #6), Cash holdings (#1). The change variables are al l computed as a first difference year (t+1) – (t). The year t is the year in which firm is identified as being in distress. The change vari ables are computed for Change in acquisitions (item #129) capital expenditure (item #128); working capital (item #4-#5); equity (ite m #216); research and development (item # 46); and net income (item #172)

PAGE 80

74 The largest VIF measure18 was observed for the operating income variable (1.47) and the over-all mean VIF was 1.17. 2.6.2 Multivariate Tests Table 18 shows a probit model measuring whether the firm has remained in distress, equaling zero, or if the firm has ex ited distress, equaling one. I use this probit model to measure the likelihood of the indepe ndent variables to explain which firms are more likely to exit distress. I find that many different ex ante variables measuring the condition of the firm and changes the firm ma kes over their first year of distress are significant in predicting the su ccess of firms exiting distre ss. In the first column, I measure the condition of the firm with severa l controls and examine changes in assets and changes in financing. The regression coe fficient for size, as measured by the natural log of total assets, is positive and statistically significant. Larger firms are more likely to get out of distress as compared to smaller fi rms. Leverage is nega tive and statistically significant in the regression with a coeffici ent of -0.6167. Firms with higher amounts of leverage are much less likely to come out of distress. High fixed payments make it extremely hard for firms to overcome distress. The coefficient for income over assets is negative and statistically significant and the coefficient for Q is insignificant in the regression. The next set of variables measure changes that occur in the first year of distress to see if these changes increase or decrease the probability of the firm exiting distress. The change variables proxy the managers’ ‘turnaroun d’ decisions in reac tion to the financial distress. The first three variables measure if the firm’s use of resources in the first year of 18 Variance inflation factor (VIF) measures the impact of collinearity among the independent variables’ in a regression model on the precision of estimation. Typi cally a VIF value greater than 10 is of concern.

PAGE 81

75 distress impacts the distress status of the fi rm. Consistent with the univariate evidence, change in R&D is positive and significant. Firms that increase their R&D spending increase the likelihood of getting out of distress. Support for this result can be traced back to the arguments presented by Hofer (1980). Ho fer categorizes turnar ound strategies into two broad types: ‘operating’ and ‘strateg ic’. ‘Operating’ turnaround strategies place emphasis on increasing revenues, decreasing costs, decreasing assets or a combination of these. ‘Strategic’ turnarounds involve either changing strate gy for competing in the same line of business or calls for entering a ne w business. Hofer states that employing turnaround strategies to ‘save’ the existing business involves emphasi s on functional area by increasing investments in marketing, production, and/or engineering. The observed increase in R&D spending by fi rms leading to succe ssful turnaround can be attributed to ‘strategic’ turnaround technique employed by the management. The change in CAPEX coefficient is negative and si gnificant, indicating that an increase in spending on capital expenditures decreases the likelihood of exiting di stress. This result is consistent with the main results of Kane and Richardson (2002). Th e coefficient for change in acquisitions is insignificant, as the level of acquisitions by firms under distress does not impact distress status of the firm. The next two variables measure if financi ng activities of the firm matter to the distress status of the firm. The coefficient of the change in working capital variable is insignificant. The coefficient for change in the amount of common or preferred stock is also insignificant. These variables display the irrelevance of a few of the financing activities of firms.

PAGE 82

76 Table 18. Multivariate Tests: Probit Analysis (1) (2) Total Assets 0.0968 0.0920 (0.00) (0.00) Leverage -0.6167 -0.6172 (0.00) (0.00) Income / TA -0.0937 (0.02) Q -0.0025 0.003 (0.51) (0.93) Chg in R&D 0.0003 0.0003 (0.05) (0.05) Chg in CAPX -0.0002 -0.0002 (0.01) (0.01) Chg in Acq. 0.0000 0.0000 (0.97) (0.99) Chg in WC 0.0000 0.0000 (0.89) (0.87) Chg in Equity 0.0000 0.0000 (0.98) (0.97) Chg in NI 0.0001 0.0001 (0.03) (0.02) Year Dummies Yes Yes Industry Dummies Yes Yes Constant -0.4607 -0.4437 (0.00) (0.00) Number of Obs. 14649 14649 Pseudo R-squared 0.055 0.054 The probit model measures whether the fi rm has remained in distress, equaling zero or if the firm has exited distress, equaling one A total of 18,434 distressed firm-year sample size is considered for the sample period of 1989-2004. Of these, 18,434 distressed firmyear sample size 3,040 firm-years exit financ ial distress condition over three year time period. Size is computed as log of total asse ts (Item #6); Leverage is computed as total liabilities divided by total as sets (Item #181/#6); Operating In come standardized by total assets (item #13/#6); Growth opportunities for the firm pr oxied by Tobin’s Q – and computed as market value of assets divide d by book value of assets ({(#199 #25) + #6 – (#60 + #74)} / #6), Cash holdings (#1). The cha nge variables are all computed as a first difference year (t+1) – (t). The year t is the y ear in which firm is identified as being in distress. The change variables are comput ed for Change in acquisitions (item #129); Change in capital expenditure (item #128); change in working capital (item #4-#5); change in equity (item #216) ; change in resear ch and development (item # 46); and change in net income (item #172).

PAGE 83

77 Change in net income looks to capture if year one performance has an impact on the firm distress status. This variable is posi tive and statistically signi ficant as a good first year in terms of better income will increase the likelihood of firms getting out of distress. Both year and industry dummies we re used in this regression. Given a potential issue with multicollinearity I drop operating income to total assets in column (2) of the regression to ensure that the earlier results are accurate. Dropping this variable does not change any of the major results in column (1). After illustrating the predictive ability of distress I then focus my attention on why that is important. The previous result showing increased i nvestment in R&D is associated with greater likelihood of turnaround seems plausi ble for higher growth firms for which investments in technology can add value to th e firm. It is of in terest to investigate whether the R&D finding is driven by hi gh growth firms, or whether increased investment in R&D assists in turnaround fo r non high growth firms as well. Thus, to further understand the role of increased R& D expenditures in aiding distressed firms' recovery the probit model was run after divi ding the sample into three distinct groups. Using the Q values, firms were differentiate d into high, average and low growth firms. Firms with Q values greater than 2 were categ orized as high growth firms. Firms with Q value between 1 and 2 were categorized as av erage growth firms and finally firms with Q value less than 1 were place in low growth category. 3,739 firm-years (25%), 7,749 firmyears (51%), and 3695 firm-year s (24%) respectively was the each sub-group sample size. The results of this probit regression ar e shown in Table 19. The findings are similar

PAGE 84

78 to that of the total sample regression of table 18. For all three groups, I observe that probability of turnover increases with size a nd decreases with increase in leverage. Table 19. Multivariate Tests: Sample Differentiated by Growth Opportunities Hi_Growth Avg_Growth Lo_Growth Total Assets 0.0523 0.1288 0.1344 (0.001) (0.000) (0.000) Leverage 0.2013 1.2018 1.4735 (0.000) (0.000) (0.000) Income / TA 0.0345 0.1640 0.1258 (0.008) (0.069) (0.100) Q 0.0028 0.0697 0.0773 (0.493) (0.299) (0.683) Chg in R&D 0.00001 0.00025 0.00020 (0.294) (0.077) (0.039) Chg in CAPX 0.0011 0.0001 -0.0007 (0.065) (0.005) (0.061) Chg in Acq. 0.00007 0.00002 0.00008 (0.948) (0.766) (0.850) Chg in WC 0.00004 0.00002 0.00002 (0.807) (0.756) (0.761) Chg in Equity 0.00008 0.00009 -0.00002 (0.281) (0.366) (0.472) Chg in NI 0.00022 0.00003 0.00003 (0.012) (0.057) (0.030) Year Dummies Yes Yes Yes Industry Dummies Yes Yes Yes Constant 0.688 0.4437 -0.3740 (0.003) (0.017) (0.096) Number of Obs. 3739 7749 3695 Pseudo R-squared 0.055 0.0770 0.0734 The probit model measures whether the fi rm has remained in distress, equaling zero or if the firm has exited distress, equaling one The sample size is 18,434 distressed firm-year for the period of 1989-2004. Of these 3,040 firm-years exit financial distress condition over three year time period. The sample here is separated into three groups by their growth ranking. Firm-years with Q greater than 2 are grouped under “Hi-Growth”, between 1 and 2 Q value firms are considered here as average growth and firms with Q less than 1 are the Low Growth firms. Size Ln(Item #6); Leverage (Item #181/#6); Operating Income standardized by total assets (item #13/ #6); Growth opportunities for the firm proxied by Tobin’s Q – and computed as market value of assets divided by book value of assets ({(#199 #25) + #6 – (#60 + #7 4)} / #6), Cash holdings (#1). The change variables are all computed as a first difference year (t+1) – (t). The year t is the year in which firm is identified as being in distre ss. The change variables are computed for Change in acquisitions (item #129); Change in capital expenditure (item #128); change in working capital (item #4-#5); change in equity (item #216) ; change in research and development (item # 46); and cha nge in net income (item #172).

PAGE 85

79 Increase in operating income increases th e probability of successful turnover and hence I observe positive and significant coefficient for all three sub-groups. The important test variable, the ch ange in R&D expenditures, shows a positive coefficient for all three groups but is signifi cant only for average and low growth firms at 8% and 4% level, respectively. These findings suggest that firms with low to average growth opportunities who increase their investments in R&D may be able to recover from distress by focusing on product innovations or by improving the efficiency of their operations by investments in technology (e .g., through automating aspects of the production process). The bene fit of increasing R&D for hi gh growth firms is not as distinct, perhaps because the degree of R&D spending in mo re rapidly growing firms is already relatively high. While it is expected that fi rms that are able to exit distress will perform better than firms that remain in a distress state, it is still of interest to measure the value of exiting distress from a shareholder wealth pers pective. Are the retu rns for firms out of distress significantly higher than those for firms that linger in a distress state, or is the difference in performance only marginal? Tabl e 20 (Panel A) measures the three year return for the group of firms able to get out of distress and the firms still in distress for each individual year. The results of this table demonstrate the importance of exiting distress. For the entire sample, firms that ca n make it out of distre ss perform significantly better in terms of three-year returns as co mpared to the firms unable to emerge from distress. The difference in thre e-year returns between the out of distress versus the sample remaining in distress is around 37 percentage po ints. I also examine the three-year return for each year individually. I find the same resu lt holds over each individual year over the

PAGE 86

80 whole sample period. I do notice a variation of the return differe nce over time but each year is significantly greater for sample of fi rms that are able to make it out of distress. Each year is significant at th e 1% level with the exception of 1996, which is significant at the 10% level. Table 20A. Comparison of 3-Year Return s for the two Samples and Over Time Time Period Out of Distress Still in Distress Difference Full Sample 1.0787 0.7049 0.3738*** 1989 1.1809 0.4948 0.6861*** 1990 1.5521 1.1512 0.4009*** 1991 0.9219 0.6272 0.2947*** 1992 0.8561 0.5541 0.302*** 1993 0.9388 0.4425 0.4963*** 1994 1.0273 0.6923 0.3358*** 1995 1.0470 0.3851 0.6619*** 1996 0.7796 0.6381 0.1415* 1997 1.0935 0.4496 0.6439*** 1998 1.1967 0.7292 0.4675*** 1999 0.8825 0.1967 0.6858*** 2000 1.4593 1.0235 0.4358*** 2001 1.2478 0.9472 0.3006*** The returns are acquired from CRSP databa se. A total of 18,434 distressed firm-year sample size is considered for the sample period of 1989-2004. Of the 18,434 firm-year 3,040 firm-years exited out of distress. The returns shown here are three-year returns. The ‘out of distress’ column tabulates three-year returns for the total sample for firms that were in distress at certain point in time and three years hence had a successful turnaround. The ‘still in distress’ column lists three-year returns of firms that were in distress at certain point in tim e and continued to be in fina ncial distress post three years. Further panel B of Table 20 uses abnorm al returns computed over three year period using market adjusted re turns. It is observed that the interpretation of panel A continues to hold even when abnormal return s are used in place of three-year buy and hold returns. The results for the three-year returns provide two findings for the paper.

PAGE 87

81 Table 20B. Comparison of 3-year Abnormal Re turns for the two Samples and Over Time Time Period Out of Distress Still in Distress Difference Full Sample 0.4124 0.1703 0.2421*** 1989 0.6809 0.1403 0.5406*** 1990 0.3060 0.1989 0.1070 1991 0.2640 0.1572 0.1069 1992 0.1301 0.0096 0.1205* 1993 0.3801 0.0800 0.3001*** 1994 0.3286 0.0793 0.2492*** 1995 0.5599 0.0754 0.4845*** 1996 0.1341 0.1313 0.0028 1997 0.2967 0.1860 0.1106* 1998 0.4052 0.0480 0.3573*** 1999 0.7537 0.2189 0.5348*** 2000 0.6187 0.3995 0.2193* 2001 0.7764 0.5764 0.2000** The returns are acquired from CRSP databa se. A total of 18,434 distressed firm-year sample size is considered for the sample period of 1989-2004. Of the 18,434 firm-year observations 3,040 firm-years exited out of distress. The returns shown here are threeyear abnormal returns. The ‘out of distress’ column tabulates three-year abnormal returns for the total sample for firms that were in dist ress at certain point in time and three years hence had a successful turnaround. The ‘still in distress’ column lists three-year abnormal returns of firms that were in distress at certain point in tim e and continued to be in financial distre ss three years hence. First, the market seems to agree with th e O-score identification that these firms are indeed doing better than firms still in distress. So, three-year returns and the corresponding abnormal returns are much higher for firms exiting distress. Also, in table 20 I define predictable ex-ante firm characteris tics that can help us explain whether or not a firm increases their likelihood of get ting out of distress. These predictable characteristics could help us identify firms, which are more likely to exit distress and have strong three-year returns. This could possibly increase the ri sk-return tradeoff for investors looking to find investme nt on riskier firms that are curre ntly in distress.

PAGE 88

82 2.7 Conclusions In this paper, I find firm turnaround is pr edictable with ex ante variables. Both firm characteristics and investment decision affect a firm’s likelihood to exit financial distress. Size, leverage, and income level at th e time of distress all impact firm distress level. The primary result of the paper sugge sts that firms can take timely actions in response to distress. Investments in product development through research and development spending increase the probability of a firm making a successful turnaround. Further, I find that this increased R&D invest ment is even more important for firms with average and low growth opportunities. Other results, such as the relationship between capital expenditure reductions and recovery from distress, ar e consistent with previous studies. The finding suggest that firms may be able to increase the likelihood of exiting distress by reducing capital expenditures and making investments in research and development which can lead to increased production efficiency or product innovations. Financial decisions do not have a signifi cant effect on successful firm turnaround, suggesting that financing stra tegies during distress are not important in determining the success of a turnaround.

PAGE 89

83 References Akerlof, G. A. (1970). "Market for Lem ons Quality Uncertainty and Market Mechanism." Quarterly Journa l of Economics 84(3): 488-500. Almeida, H., M. Campello, et al. (2004). "The cash flow sensitivity of cash." Journal of Finance 59(4): 1777-1804. Altman E., I. (1968). “Financial ratios, di scriminant analysis and the prediction of corporate bankruptcy.” Journal of Finance 23, 589-609. Altman, E. I. (1983). "Exploring the Road to Bankruptcy.” Journal of Business Strategy 4(2): 6. Andrade, G. and S. N. Kaplan (1998). “How costly is financial ( not economic) distress? Evidence from highly leveraged transactions th at became distressed.” Journal of Finance 53(5): 90. Asquith, P., R. Gertner, et al (1994). “Anatomy of Financial Distress: An examination of junk-bond issuers.” Quarterly Jour nal of Economics 109(3): 34. Baskin, J., (1987). "Corporate liquidity in games of monopoly power." Review of Economics and Statistics 69: 312-319. Bebchuck, L.A., A. Cohen., A. Ferrell (2005). "What Matters in Cor porate Governance?" Working Paper Series. Beaver, W. H., (1966). “Financial Ratios as Pr edictors of Failure.” Journal of Accounting Research 4(3): 71-111. Bhagat, S. and B. Black (1999). "The uncer tain relationship between board composition and firm performance." Business Lawyer 54(3): 921-+. Bhagat, S. and B. Black (2002). "The NonCorrelation Between Board Independence and Long-Term Firm Performance," Jour nal of Corporation Law, 27(2). Bhagat, S., N. Moyen and I. Suh (2005). “Inve stment and internal funds if distressed firms.” Journal of Corporate Finance 11: 449-472. Bibeault, D.G. (1982). “Corporate Turnaround: Ho w managers turn lose rs into winners, McGraw-Hill, New York.

PAGE 90

84 Byrd, J. W. and K. A. Hickman. (1992). "D o Outside Directors Monitors Managers?: Evidence from Tender Offer Bids" Journal of Financial Economics 32(2): 195-221. Chudson, W., (1945) "The pattern of Corporat e Financial Structure." National Bureau of Economic Research, New York. Ciccone. S. J. (2001). “Analyst Forecast Pr operties, Financial Distress and Business risk.” Working paper Series. Cleary, S. (1999). "The Relationship between Fi rm Investment and Fi nancial Status." The Journal of Finance 54(2): 673-692. Dittmar, A. and J. Mahrt-Smith (2007). "Corporate Governance and the Value of Cash Holdings." Journal of Fina ncial Economics 83(3): 599-634. Dittmar, A., J. Mahrt-Smith et al. (2003). "International corporate governance and corporate cash holdings." Journal of Financ ial and Quantitative Analysis 38(1): 111-133. Fazzari, S. M., R. G. Hubbard, et al. ( 1988). "Financing Constraints and Corporate Investment." Brookings Papers on Economic Activity (1): 141-206. Fazzari, S. M. and B. C. Petersen (1993). "Working Capital and Fixed Investment: New Evidence on Financing Constraints" The RAND Journal of Econom ics, 24(3): 328-342. Froot, K. A., D. S. Scharfst ein, et al. (1993). “Risk manage ment: Coordinating corporate investment and financing policie s.” Journal of Finance 48(5): 30. Gilchrist, S. and C. P. Himmelberg (1995) "Evidence on the role of cash flow for investment." Journal of Mone tary Economics 36(3): 541-572. Gilson, S. C., (1989). “Management Turnover and Financial Distress.” Journal of Financial Economics, 25(2): 241-262. Gompers, P., J. Ishii, et al (2003). "Corporate governance a nd equity prices." Quarterly Journal of Economics 118(1): 107-155. Greenwald, B., J. E. Stiglitz, et al. (1984). "Informational Imperfections in the Capital Market and Macroeconomic Fluctuations." The American Economic Review 74(2): 194199. Grinyer, P. H., D. G Mayes, P. McKier nan. (1988) “Sharpbenders: The Secrets of Unleashing Corporate Potential. ” Basil Blackwell, Oxford.

PAGE 91

85 Guerard Jr, J. B., A. S. Bean, S. Andrew s (1987). “R&D Management and Corporate Financial Policy. ” Manageme nt Science, 33(11): 1419:1427. Hambrick, D.C. and S.M. Sch ecter (1983). “Turnaround Strategi es for Mature IndustrialProduct Business Units.” Academy of Mana gement Journal, 23(2): 231-248. Harford, J. (1999). "Corporate cash reserves a nd acquisitions." Journal of Finance 54(6): 1969-1997. Harford, J., S. A. Mansi et al., (2008). "Corpor ate Governance and firm cash Holdings in the US." Journal of Financial Economics 87: 535-555. Hermalin, B. E. and M. S. Weisbach ( 1991). "The Effects of Board Composition and Direct Incentives on Firm Performan ce." Financial Management 20(4): 101-112. Hofer, C. W., (1980). “Turnaround Strategies .” Journal of Business Strategy. 1(1): 1931. Hoshi, T., A. Kashyap, et al. (1990). “The Role of Banks in Reducing the Costs of Financial Distress in Japan.” Jour nal of Financial Economics 27(1): 22. Hoshi, T., A. Kashyap, et al. (1991). "Corpor ate Structure, Liquidity, and Investment: Evidence from Japanese Industrial Groups." Th e Quarterly Journal of Economics 106(1): 33-60. Hotchkiss, E. S., (1995). “Post bankruptcy Pe rformance and Management Turnover.” The Journal of Finance. 50(1): 3-21. Hou, K., D. T. Robinson. (2006). “Industry C oncentration and Average Stock Returns.” The Journal of Finance 61(4): 1927-1956. James, C., (1995) "When Do Banks Take Equity in Debt Restructurin gs ? The Review of Financial Studies 8(4): 1209-1234. Jensen, M. C. (1986). "Agency Costs of Free Cash Flow, Corporate-Finance, and Takeovers." American Ec onomic Review 76(2): 323-329. Jensen, M. C. (1993). "The Modern Industr ial-Revolution, Exit, and the Failure of Internal Control-Systems." J ournal of Finance 48(3): 831-880. Jiraporn. P., Y. S. Kim, et al. (2006). "C orporate Governance, Shareholder Rights and Firm Diversification: An Empirical Analys is." Journal of Banking and Finance. 30(3): 947-963.

PAGE 92

86 John, T. A., (1993). “Accounting Measures of Corporate Liquidity, Leverage, and Costs of Financial Distress.” Fina ncial Management 22(3): 91-100. John, K., L. H. P. Lang, et al. (1992). “The Voluntary Restructuring of Large Firms in Response to Performance Decline. ” Journal of Finance 47(3): 27. Kahl, M., (2002). “Economic Distress, Fina ncial Distress, and Dynamic Liquidation.” The Journal of Finance 57(1) :135-168. Kane, G. D and F. M. Richardson (2002). “T he Relationship between changes in Fixed Plant Investment and the Likelihood of Emer gence from Corporate Financial Distress.” Review of Finance and Accounting, 18 : 259-272. Kang, J-K., A. Shivdasani. (1997). “Corporate restructuring during performance declines in Japan.” Journal of Financ ial Economics 46(1) : 29-65. Kaplan, S., (1989). “The Effects of Manage ment Buyouts on Operating Performance and Value.” Journal of Financial Economics 24: 217-254. Kaplan, S. N. and L. Zingales (1997). "Do Investment-Cash Flow Sensitivities Provide Useful Measures of Financi ng Constraints?" The Quarterly Journal of Economics 112(1): 169-215. Keynes, J. M. (1936). "The General Theory of Employment Intere st and Money." The Quarterly Journal of Economics 51(1): 147-167. Lamont, O. (1997). "Cash flow and investmen t: Evidence from internal capital markets." The Journal of Finance 52: 83-109. Lamont, O., C. Polk, et al. (2001). "Finan cial Constraints and Stock Returns." The Review of Financial Studies 14(2): 529-554. Lang, L. H. P., R. M. Stulz, et al. (1991). "A Test of the Free Cash Flow Hypothesis: The Case of Bidder Returns." Journal of Financial Economics 29(2): 21. Minnick, K. (2004). "Write-offs and Corpor ate Governance." Working Paper Series. Modigliani, F. and M. H. Mill er (1958). "The Cost of Cap ital, Corporation Finance and the Theory of Investment." The Am erican Economic Re view 48(3): 261-297. Myers, S. C. (1977). "Interactions of Cor porate Financing and Investment Decisions Implications for Capital-Budgeting Re ply." Journal of Finance 32(1): 218-220. Myers, S. C. (1984). "Capital Structur e Puzzle." NBER Working Paper Series.

PAGE 93

87 Myers, S. C. and N. S. Majluf (1984). "Cor porate Financing and I nvestment Decisions when firms have information that investors do not have." Journal of Financial Economics 13: 187-221. Ohlson, J. A. (1980). "Financial Ratios and the Probabilistic Prediction of Bankruptcy." Journal of Accounting Research 18(1): 109-131. Ofek, E. (1993). "Capital Structure and Firm Response to Poor Performance: An Empirical Analysis." Journal of Financial Economics 34(1): 28. Opler, T., L. Pinkowitz, et al. (1999). "The de terminants and implications of corporate cash holdings." Journal of Fi nancial Economics 52(1): 3-46. Opler, T. C. and S. Titman (1993). “The Determinants of Leveraged Buyout Activity: Free Cash Flow vs. Financial Distress Co sts.” The Journal of Finance 48(5): 1985-1999. Opler, T. C. and S. Titman (1994). "Financi al Distress and Corporate Performance." The Journal of Finance 49(3): 1015-1040. Pinkowitz, L. and R. Williamson (2001). "Ba nk power and cash holdings: Evidence from Japan." Review of Financ ial Studies 14(4): 1059-1082. Pinkowitz, L. and R. Williamson (2007). "Wha t is the Market Value of a Dollar of Corporate Cash." Journal of Applie d Corporate Finance 19(3): 74-81. Richardson, S. (2006). "Over-investment of free cash flow." Review of Accounting Studies." 11(2-3): 159-189. Robbins, D. K. and J. A. Pearce II (1992). “Turnaround: Retrenchment and Recovery.” Strategic Management Journal 13: 287-309. Schendel, D.E., R. Patton, and J. Riggs (1976) “Corporate turnaround strategies.” Purdue University. Schendel, D. E., and G. R. Patton (1978). “A Simultaneous Equation Model of Corporate Strategy.” Management Science 24(15): 1611-1621. Sharpe, S. A. (1994). "Financial market impe rfections, firm leverage, and the cyclicality of employment." American Economic Review 84(4): 15. Shleifer, A. and R. W. Vishny (1992). "Liqui dation Values and Debt Capacity: A Market Equilibrium Approach." Journal of Finance 47(4): 24. Stiglitz, J. E. and A. Weiss (1981). "Cre dit Rationing in Markets with Imperfect Information." American Economic Review 71(3): 393-410.

PAGE 94

88 Stiglitz, J. E. and A. Weiss (1983). "Incentive Effect s of Terminations Applications to the Credit and Labor-Markets." Amer ican Economic Review 73(5): 912-927. Stulz, R. M. (July 1990). "Man agerial discretion and optimal financing policies." Journal of Financial Economics 26(1): Pages 3-27. Vogel, R. C. and G. S. Maddala (1967) "Cross-Section Estimat es of Liquid Asset Demand by Manufacturing Corporations." Journal of Fina nce 22(4): 557-575. Warner (1977) White, M. (1983). “Bankruptcy Costs and the New Bankruptcy Code.” Journal of Finance, 38(2): 477-488. Whited, T. M. (1992). "Debt, Liquidity C onstraints, and Corporate-Investment Evidence from Panel Data." Journal of Finance 47(4): 1425-1460. Wruck, K. H. (1990). "Financial Distre ss, Reorganization, and Organizational Efficiency." Journal of Fi nancial Economics 27(2): 26.

PAGE 95

About the Author Sanjay Kudrimoti graduated with a Ma sters Degree in Chemistry in 1992 and worked in the manufacturing se ctor as a Chemist, in India. He immigrated to United States in 1998. He enrolled in Portland State University, Oregon to earn his MBA (Finance). He joined Morgan Stanley thereaft er before entering the Doctoral Program at University of South Florida, Tampa. His rese arch focus is in areas relating to Financial Distress, Corporate Governance, Cash Ho ldings and Secondary Equity Offerings. Mr. Kudrimoti is presently employed with Salem State College in the State of Massachusetts. In addition to his teaching re sponsibilities he is ma de a commitment to get his research published in refereed journals. Outside of academia, Mr. Kudrimoti has always been active an active volunteer in hi s community. Mr. Kudrimoti is married with two kids. He presently resides in Marb lehead, a small town on the coast of Massachusetts.