Operating or Financial Distress? How much Costly

American Journal of Scientific Research ISSN 1450-223X Issue 55 (2012), pp. 96-108 © EuroJournals Publishing, Inc. 2012 http://www.eurojournals.com/ajsr.htm

Operating or Financial Distress? How much Costly these are? Umar Farooq Department of Business Administration Punjab University Gujranwala Campus, Pakistan Mian Sajid Nazir Assistant Professor, Department of Management Sciences COMSATS Institute of Information Technology, Lahore, Pakistan E-mail: [email protected] Tel: +92 322 4569868 Muhammad Musarrat Nawaz Lecturer, Hailey College of Commerce University of the Punjab, Lahore, Pakistan Abstract Current study intended to explore the cost of financial distress for Pakistani manufacturing ongoing firms listed at KSE. In doing so, financial distress is divided into operating distress and financial but not operating distress. Sample consists of ongoing firms that were at least once in distressed counter for the period of analysis. Independent t-test, parametric and non-parametric correlation analysis and GLM regression analyses are used to conclude proposed models. It is found that firms borne opportunity losses before and after entering to both operating and financial distress. Moreover, results also show that operating distress affects more severely to firms’ value as compared to financial but not operating distress category. However, result for pre financial but not operating distress is found insignificant. On the other hand organizational efficiencies are also evidenced in post year to distress. In short current study provides opportunity to all stakeholders to assess firms’ performances before and after entering to both operating and financial distress.

Keywords: Cost of Financial Distress, Ongoing Distressed Firms, Opportunity Losses, Sales Growth

1. Introduction Firms consist of people and resources that work together and practice a number of activities to transfer some input into output for a common purpose i.e. profitability. In doing so, firms have to bear two types of burdens. One is operating burden that is related to the operational costs needed to execute firms’ operating activities while other is financial burden that can be defined as the obligations transpire as a result of leverage deployed by the firm from external sources to fulfill their investing needs. In order to earn profits firms have to generate enough proceeds from their operations that fulfill their both operational and financial needs with excess as the remaining residuals will be the business profit. However, in case where firms are unable to do so and did not produce enough proceeds to meet these expenses will be in a state of distress. Unable to meet operating costs demonstrate the lack of

Operating or Financial Distress? How much Costly these are?

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managerial efficiencies towards business operations or the economic shocks smacked by external environment and can be attributed to “Operating Distress”. While on the other hand the failure to meet financial burden can be labeled as “Financial Distress” and shows the firms’ inability to generate required returns from their investing activities to meet costs of external obligations. In short both operating and financial distress are the negative connotations that show the firms’ inability to fulfill their operating and financial obligations on time or to the full extent due to the temporary lack of liquidity and other difficulties confronted by firm (Davydenko, 2005; Gordon, 1971). Predicting such distress situations and the costs crop up as a result are the concerns of management, creditors, investors and other stakeholders and also lie at the heart of corporate financial management decisions. These negative notations are also used to illustrate the capital structure puzzle and sees as an important dynamic while constructing corporate capital structure strategies as demonstrated by trade off theory of capital structure (Myers and Jensen, 1986). Researchers view financial distress in term of insolvency, default or corporate restructuring synonymously (Andrade & Kaplan, 1998; Wruck, 1990). However, it is also suggested that financial distress is one of the most imperative reasons that leads to such default or insolvency rather than an exclusive cause that impacts solely in this respect (Purnanandam 2005; Turetsky, 2003). Despite such contradictions to define financial distress, researchers are agreed that the process of financial distress affects firm’s value negatively (Pindado and Rodrigues, 2005; Stulz, 1990). All these discussions confirm the significance of financial distress and the costs incurred as a result of such anguish situations. Previous studies tried to explore costs of financial distress in the context of developed countries while less intention is given to developing countries like Pakistan in this respect. Current study intends to investigate such costs of distress with different methodology and definition of financial distress in case of Pakistani ongoing manufacturing firms to fill the gap.

2. Corporate Financial Distress Two counter parties i.e. debtors and creditors are involved in the process of financial distress. Scope of creditors is not restricted to external capital provider only but also includes other stakeholders like suppliers or employees. Wruck (1990) has defined current liabilities as deferred payments to creditors and employees, damages from legal actions, current portion of long term finance and interest payments to external capital providers. Researchers have defined financial distress into different ways. One group of researchers viewed financial distress as the state of failure to pay financial obligations when they became due (Altman, 1984; Andrade & Kaplan, 1998; Wruck, 1990). They suggested that financial distress is a state that differentiates firms from their healthy and infirmity positions where corrective measures are needed to overcome those troubled situations. Andrade & Kaplan (1998) divided financial distress into two forms: failure to pay financial obligations on secluded time and restructuring of capital structure to avoid default or bankruptcy. Conversely, another view suggests that financial distress is distinct from default or bankruptcy and demonstrates the situations between firms’ healthy and illness positions (Gordon, 1971; Turetsky, 2003). Purnanandam (2005) developed a theoretical model in which he explains financial distress as a state between solvency and insolvency. He argues that financial distress is a state where firms do not have enough proceeds to meet their debt obligations while at the maturity of such debt obligations firms enter into insolvency state. However, financial distress is prerequisite of default or bankruptcy as Gilbert et al. (1990) suggest that financial distress is one of the potential determinants of bankruptcy rather than an exclusive source of default. Similarly, Turetsky & MacEwen (2001) also described financial distress as combined but separate multiple processes and divided these continuous subsequent series of processes into different stages that contain some particular unfavorable financial characteristics. In adverse situations firms move to next stage until reached to end point i.e. insolvency and in case of recovery came back to previous stage until reached to healthy position. They argued that process starts with dividend reductions, negative profits and then problem of free cash flow leads to default or restructuring. In short these studies differentiate financial distress with bankruptcy or default

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Umar Farooq, Mian Sajid Nazir and Muhammad Musarrat Nawaz

and consider it as separate process that can lead to bankruptcy but is not synonymously related to default. This paper also assumes financial distress as continuous process and suggests that firms who are not announced bankrupt or default can still face distressed situations and bear losses due to such anguish situations 2.1. Costs of Financial Distress Financial distress is seen as a costly process that can affect firms’ performances and its capital structure negatively. Researchers have divided such losses into direct bankruptcy cost such as legal or administrative costs that incur once at the time of default and indirect hidden losses like opportunity or productivity losses. 2.1.1. Direct Costs of Financial Distress Direct bankruptcy costs realize once at the time of liquidation or default and that is why called direct costs of bankruptcy. These are fixed payments to third party such as professional lawyers, accountants, attorneys, trustees or administrators etc who execute the process of bankruptcy or reorganization. Though such costs are smaller than indirect cost of financial distress but still documents 3% to 25% losses of firms’ value in previous literature. Warner (1977) argued that direct costs of default are smaller in nature and reported only 1% insignificant losses of firms’ value just prior to bankruptcy. However, Altman (1984) criticized on his narrow approach in selecting sample and definition of bankruptcy cost. Altman (1984) examines the costs of bankruptcy borne by 11 railroads announced default under bankruptcy act section 77 for the period of 1933 to 1955 and found 1.8 million average direct bankruptcy costs that were 3.5 percent of their market value on average. Moreover, Stanley & Girth (1971) and Ang et al. (1982) also studied direct costs of bankruptcy and found average losses of 24.9% and 7.5% of firms’ value respectively. Gilson et al. (1990) studied for a sample of 169 firms during the period of 1978 to 1987 who restructure themselves through out-of-court method and evidenced direct losses of 65% of their book value of assets. This implies that direct cost of financial distress significantly affects firms’ value negatively despite its low magnitude. 2.1.2. Indirect Cost of Financial Distress Besides direct costs of bankruptcy that occur at the time of liquidation, firms also bear some indirect hidden losses before or at the time of default. These costs can be in the form of opportunity loss or firms’ underperformance as compared to expected results. Opportunity losses can be attributes to decreased customers’ loyalty as the risk of default increases. Moreover, during distress cost of capital increases due to high debt financing while creditors also impose restrictive terms and conditions that also contribute negatively to firms’ operations and results to productivity losses. Researchers are agreed to the notation that indirect costs of financial distress are higher and difficult to evaluate due to its complex nature than direct bankruptcy costs (Andrade & Kaplan, 1998; Gilson et al., 1990). Altman (1984) first tries to study and measure indirect costs of financial distress with different methodology. He assessed the magnitude of bankruptcy costs and compares the present value of cost of bankruptcy against current tax benefits. His research provides basic information to measure indirect costs and also proves that costs of distress are enough significant that it should consider in decision making. Ofek (1993) studied firms’ responses towards the process of financial distress and found that financial distress compel firms to adopt operational restructuring and downsizing in order to survive especially in the case of high leveraged firms. These results are also consistent with John et al. (1992) who also demonstrate assets restructuring and managerial and employees downsizing in responses to financial distress. Opler & Titman (1994) also tried to explore losses in the context of financial distress. They divided such losses into three broad categories. First they linked these costs to customer driven losses and suggest that increased default risks affect customers’ loyalty and ultimately decrease sales revenue. They argued that operating problems during distress period decrease the customers’ confidence that respected firm will not delivered their products on time with required quality. Moreover, Babenko (2003) also found that such negative effects on customers’ confidence become


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higher as firms move toward default position. Second, they argued that healthier competitors also attack through aggressive price strategies in order to capture market share of distressed firms and third are the management driven losses where firms tend to decrease their key workforce especially in the case of highly leveraged firms as Jensen (1989) explains that firms with high debts respond to poor performances and distressed situations more quickly. However, Opler & Titman (1994) did not found significant results for management driven costs and also failed to measure relative portion of these three costs of distress. Hoshi et al. (1990) studied costs of financial distress in term of relationships with creditors and found that firms bear losses in distressed situations especially for the firms having weak bargaining power to their creditors and are unable to renegotiate. He suggests that asymmetric information and free riding problems are potential determinants of cost of financial distress and argued that these components makes it difficult for the firms to renegotiate with their creditors in distressed situations. On the other hand literature also reveals that firms’ external obligations can also compel them to sale their fixed assets at under-pricing during the distressed situations (Shleifer and Vishny, 1992). Pulvino (1998) also provides similar results and demonstrates that due to cash flow problems, firms sold their assets in order to meet their debt obligations on time. He uses sample of 28 US airlines in his research and 8 out of these 28 airlines use chapter 7 or chapter 11 bankruptcy rules subsequently. He found that airline who subsequent bankrupted sold their airplanes at low prices as compared to non-bankrupt airlines. After Altman (1984) Opler & Titman (1994) it was Chen and Merville (1999) who extended their work and tried to explore costs of financial distress for ongoing firms. It was first successful attempt to study costs of financial distress while assuming financial distress as a cyclic process as their sample includes only those firms who were not at default counter for studied period. They argued that temporal state of financial distress also affect firms’ value. They divided indirect costs of financial distress into opportunity losses due to loss of customers, loss of key suppliers, loss of valuable workforce and foregone investment opportunities. Their results reveal that firms having distinct patterns of increased probability of financial distress bear 10.3% of average losses of their firm’s value while document highest loss as 76% in this respect. Pindado & Rodrigues (2005) studied Ex-ante costs of indirect financial distress for multiple economies. They develop model to investigate costs of financial distress to provide international evidences in this respect. Accurate debt based prediction model used to measure probability of financial distress differentiate their research from previous works. Moreover, controlling like hood of financial distress allow them to investigate trade off between costs and benefits of debts as Jensen (1989) argued that leverage can also be beneficial for distressed firms. Their sample consists of 186 firms from Germany, 1704 firms from US and 491 firms from UK. They use change in industry adjusted sales growth as opportunity loss to measure indirect cost of financial distress. For all countries they found positive relation of probability of financial distress with opportunity losses measured as industry adjusted sales growth. One of recent studies proposed by George & Hwang (2009) investigates costs of financial distress in term of operating profits and stock returns. Their results reveal that highly leveraged firms document more decrease in operating profits as compared to low leveraged firms. Yen & Li (2008) studied stock returns as costs of financial distress and found that firms loss substantial amount of stock prices within post 20 transaction days of the announcement from the day of distress. Furthermore, their results also document largest cost of financial distress for delisted firm and found lower costs of financial distress that maintain normal trading. All these discussions reveal that financial distress is a costly process and firms bear losses before and after entering to financial distress. Current study also aims to explore costs of distress in case of Pakistani ongoing distressed manufacturing firms and proposed that firms bear opportunity losses prior and after entering to distress statuses.

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2.2. Positive Aspects of Financial Distress Though firms have to bear losses due to financial distress whether they become default or not, however still financial distress plays an important role to improve organizational efficiencies. Makridakis (1991) argued that financial distress taught firms how to adopt changes according to environment and how to control and eliminate negative consequences of financial distress. Moreover, Kahl (2002) also studied potential benefits of financial distress in term of increased efficiencies and argued that benefits of these increased efficiencies overweight costs of financial distress. However, if firms didn’t learn from such distressed situations, creditors can sue for liquidation. Ofek (1993) suggested that state of financial distress compel firms to change their operational strategies in order to gain more efficiency while Diamond (1993) and Gilson (1989) argued on effective management control as a result of distressed state. Moreover, financial distress and default situations allow us to select firms that should liquidate or continue (Smith & Stromberg, 2004). Decision is made on the basis of benefits of going concern against liquidation. So, financial distress also helps to identify unprofitable firms that should liquidate. In conclusion financial distress is just not a negative connotation only but also provides a chance to identify economically feasible firms that should reorganize for better performances and firms that could not provide further benefits and should liquidate.

3. Methodology Current study assumes financial distress as a cyclic process and proposed that firms who are not announced default or bankrupt also face distressed situations. Moreover, distress is divided into “operating distress (OD)” where firms do not have enough proceeds to meet their operating costs and “financially but not operating distress (FDnOD)” where firms show positive EBIT but are unable to pay their financial obligations on time or to full extent. It is proposed that operating distress affects more severely to firms’ value as compared to financially but not operating distress category. 3.1. Sample Selection In order to achieve the research objectives specific sample of 202 ongoing firms are selected. Unit of analysis of current study are organizations. Data is taken from the annual publications of “Balance Sheet Analysis of Joint Stock Firms Listed at KSE” issued by State Bank of Pakistan. In order to attain required sample following steps are taken into account. • All 425 manufacturing firms listed at KSE are selected after excluding all non financial firms. • 100 default firms are excluded. • 31 new firms that are registered during the years of analysis are excluded. • 87 firms who never file negative earnings during analysis period are also excluded. • Firms observations having zero sales are also excluded • In order to normalize data of opportunity loss 2.5 % data is trimmed off • At last 2096 firms’ observations during 1999 to 2009 for 202 firms are selected as final sample of current study. 3.2. Opportunity Loss Opportunity loss is calculated as the difference between firms’ sales growth and sector’s sales growth. Positive answer will demonstrate that firm bear opportunity loss and under perform as compared to its industry performance. It is argued that in the case of distress firms lose customers’ loyalty that results to decrease in sales growth as compared to its industry. Following formula illustrates that how opportunity loss is calculated. Opportunity Loss = [(Sales it - Sales it-1) / Sales it -1] sector - [(Sales it - Sales it-1) / Sales it -1] firm


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3.3. Defining Distress Statuses The purpose of current study is to explore the relationship between opportunity losses and distress situations. In doing so distress status is taken as independent variable. Previously researchers have defined financial distress into different ways in term of its technical expression. Asquith et al., (1994) considered a firm financially distressed if its interest coverage ratio is less than 0.8 for that year or less than one for previous two consecutive years. On the other hand DeAngelo and DeAngelo (1990) measured financial distress if that firm accounts losses for three consecutive years even in the absence of high debt ratio. However, current study use different measures and divide financial distress into two broad categories of operating distressed and financially but not operating distressed. Operating distress (OD) is measured as dummy variable that is equal to 1 if operating profits show negative results and zero if net profits are positive. However, firms that show positive operating profits but negative EBT (earning before tax) are not included in this variable. On the other hand financially but nor operating distressed (FDnOD) is measured as dummy variable that is equals to one if the firms’ EBT show negative results and zero for firms show positive net profits. However, firms reporting negative operating profits are excluded while calculating FDnOD. At last financial distress (FD) measured as dummy variable equals to 1 if respected firm show negative EBT and 0 otherwise. It is proposed that operating distress is more severe than financial but not operating distress and contribute more to opportunity losses.

4. Results This section will present the results of current study. In doing so, descriptive statistics, independent ttests, parametric and non parametric correlation and GLM regression analyses are conducted. 4.1. Descriptive Statistics Table 1 provides the descriptive statistics for sampled firms by categorizing into operating distressed (OD), financial but not operating distressed (FDnOD) and healthy firms. As expected the mean sales and earnings are lower for distress firms as compared to healthy firms. Moreover, results also show that distressed firms account positive mean opportunity loss as compared to healthy firms. Table 1:

Descriptive statistics

Mean

Std. Deviation

Minimum

Maximum

Category Healthy OD FDnOD Healthy OD FDnOD Healthy OD FDnOD Healthy OD FDnOD

Total Asset 3070.09 3167.19 3164.96 7237.16 9770.20 5891.26 37.80 40.70 66.30 70013.70 69375.30 51993.00

Sales 2958.19 2614.90 2007.08 7837.57 8968.18 2612.42 5.80 7.20 35.40 90663.50 85224.10 17473.50

EBIT 319.11 -64.06 119.78 801.53 102.51 305.79 0.00 -853.90 0.00 7193.80 -0.10 3159.60

OL -0.04 0.12 0.02 0.27 0.32 0.26 -1.14 -1.14 -1.03 0.78 0.79 0.73

Such operating losses can be due to economic shocks or decrease in customers’ loyalty in response to distress. However, if second source is true then it can be attributed to the cost of distress. Opler & Titman (1994) suggest that financial distress affects negatively to customer’s confidence and loyalty. However, mean EBIT for FDnOD is not that much severe as compared to operating distress where operating losses are higher. It is consistent with the proposed hypothesis of current study that operating distressed firms bear high opportunity losses as compared to financially FDnOD. Results

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show that operating distressed firm bear mean 12 % sales losses with respect to its industry while FDnOD firms account 2% average opportunity losses. This augmented effect can be due to the severe liquidity problems in operating distress that ultimately affects their productivity and customer’s confidence. However, financially but not operating distressed do not face such severe liquidity problems but profitability problems that contribute less negative to firm’s value. 4.2. T-test for Mean Differences Table 2 explores the results from independent t-test to conclude mean differences of opportunity losses for distressed and healthy firms. Significant mean differences are found for pre operating distressed era (ODt+1). However, insignificant results are found for pre financially but not operating distressed (FDnODt+1) firms. This implies that only operating distress contributes to opportunity loss prior to its occurrence. It is found that prior to operating distress firms bear 9.1% opportunity losses while pre financially but not operating distress (FDnODt+1) document insignificant 0.70% opportunity losses as compared to healthy firms. On the other hand significant mean differences are found for all three categories of post distress era. Results show that firms in post operating distress (ODt) bear 16.3% opportunity losses while FDnODt firms accounts 6.1% opportunity losses as compared to healthy firms. It is consistent with proposed hypothesis that firms who are in operating distress bear high opportunity losses as compared to firms who generate enough proceeds to cover their operating expenses but still announce negative earnings. In conclusion firms bear opportunity losses prior or after entering to such distressed situations as compared to healthy firms while these losses become more severe in case of operating distressed firms. Table 2:

OD t FDnOD t FD t OD t+1 FDnOD t+1 FD t+1

Independent t-statistics for Mean Differences Category 0.00 1.00 0.00 1.00 0.00 1.00 0.00 1.00 0.00 1.00 0.00 1.00

N 1,397 420 1,122 279 1,397 699 1,271 358 1,114 274 1,271 632

Mean -0.045 0.118 -0.0415 0.0198 -0.0450 0.0789 -0.0229 0.0678 -0.0232 -0.0161 -0.0229 0.0314

Difference

t-Value

Sig

-0.163

-9.379

0.000

-0.061

-3.358

0.001

-0.124

-9.098

0.000

-0.091

-4.724

0.000

-0.007

0.379

0.705

-0.054

-3.753

0.000

4.3. Correlation Analysis Table 3 and 4 provides the results from correlation analyses between opportunity loss and post and pre distressed eras respectively along with other variables of control. Upper right triangular results show non parametric Spearmen correlation statistics while lower left triangular of the table demonstrates the results from parametric Pearson’s correlation. Results show positive relationships between OL (opportunity losses) and all categories of pre and post distress eras. These results are signified and validated by both Spearman’s and Pearson’s coefficient of correlation except in case of FDnODt-1. This implies that only operating distress contribute to opportunity losses prior to its occurrence rather than financially but not operating distress. Moreover, results also show augmented effects of operating distress to opportunity losses as compared to FDnOD in both pre and post distress eras. Other variables of control including ACP, TAGrw, STTA and SecDist refer to average collection period, total assets growth, sales to total assets and dummy variable of distressed sector respectively.

Operating or Financial Distress? How much Costly these are? Table 3:

Correlation Statistics for Post Distress era OL

OL OD t FDnOD t FD t ACP TAGrw STTA SecDist

Table 4:

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0.23(**) 0.09(**) 0.20(**) 0.16(**) -.15(**) -.11(**) 0.01

OD t 0.24(**) .(a) 1.00(**) 0.11(**) -.17(**) -.19(**) 0.02

FDnOD t 0.10(**) .(a) 1.00(**) -.01 -.10(**) -.13(**) 0.09(**)

FD t 0.21(**) 1.00(**) 1.00(**)

ACP 0.12(**) 0.02 -.002 0.01

0.07(**) -.15(**) -.21(**) 0.08(**)

-.07(**) -.18(**) -0.06(**)

TAGrw -.17(**) -.26(**) -.15(**) -.23(**) -.06(**)

STTA -.18(**) -.27(**) -.15(**) -.26(**) -.29(**) -.03

-.06(*) -0.03

0.001

SecDist 0.03 0.02 0.09(**) 0.08(**) -0.13(**) -0.03 0.02

Correlation Statistics for Pre Distress era

OL OL 0.13(**) OD t+1 FDnOD 0.01 t+1FDnOD t+1 0.09(**) FD t+1 0.16(**) ACP -.15(**) TAGrw -.11(**) STTA 0.01 SecDist (**) Significant at 1% (*) Significant at 5%

OD t+1 0.14(**)

FDnOD t+1 0.02 .

.(a) 1.00(**) 0.09(**) -.10(**) -.13(**) 0.02

1.00(**) -.01 -.04 -.12(**) 0.09(**)

FD t+1 .103(**) 1.00(**)

ACP .120(**) 0.00

TAGrw -.17(**) -.16(**)

STTA -.18(**) -.19(**)

SecDist 0.03 0.02

1.00(**)

.007

-.04

-.14(**)

0.09(**)

.001

-.11(**) -.06(**)

-.20(**) -.29(**) -.03

0.08(**) -0.13(**) -0.03 0.02

(*)

0.06 -.08(**) -.16(**) 0.08(**)

-.068(**) -.184(**) -0.06(**)

-.06(*) -0.03

0.001

Both total assets growth (TAGrw) and sales to total assets (sales to total assets) show negative affects on opportunity losses that reveal that increase in total assets and high productivity positively contribute to firm’s operations. However, average collection period (ACP) is positively related to opportunity losses that indicate that loose credit policy block firm’s cash flow and affect negatively to its operations. It is also argued that such opportunity losses can be due to economic shocks smacked by external environment. To check the effects of such economic shocks, dummy variable of distressed industry (SecDist) is taken. SecDist is measured as dummy variable that is equal to 1 if respected industry files negative EBT and 0 otherwise. Result show positive but insignificant affect of SecDist to opportunity losses. So, due to its low intensity and insignificance, positive affects of distress to opportunity losses can be labeled as cost of distress. 4.4. Regression Analysis Table 5 presents the results from post distressed GLM regression analysis into three different proposed models. Dependent variable is opportunity losses as measured in previous section. Results reveal that there is significant positive relationship between opportunity losses and OD t, FDnOD t and FD t at time t as demonstrated by model 1, 2 and 3 respectively. It is consistent with Opler & Titman (1994) who also significant opportunity losses in response to ex-post financial distress. All three proposed models also show high χ2 values to signify the contribution of independent variables in explaining the variations in opportunity losses. It is found that firm’s sales growth underperforms 11.9% than its industry when respected firm is in operating distress. If this underperformance is purely due to operating distress then it can be attributed to cost of operating distress. Various studies have also argued to the contribution of economic shocks while calculating cost of financial distress (Weiss, 1990; Altman, 1994; Gilson, 1997). To control the affects of economic shocks, SecDist variable is also included in proposed models that show insignificant results. So, one can conclude that operating distress leads to high opportunity losses rather than economic shocks. It is argued that in operating distress firms face liquidity problems that affect their operations and customers’ confidence more negatively.

104 Table 5:

Umar Farooq, Mian Sajid Nazir and Muhammad Musarrat Nawaz Post distress GLM Regression Analysis

(Constant) ODt FDnODt FDt SecDist Collection Period Total Assets Growth Sales to total assets Model χ2 No. of Observations

β -0.024 0.119

0.007 0.001 -0.143 0.000 179.015 1735

Model 1 χ2 2.376 50.866

0.163 33.418 36.227 6.764

Sig. 0.123 0.000

0.686 0.000 0.000 0.009

β -0.026

Model 2 χ2 2.143

Sig. 0.143

0.045

5.703

0.017

-0.016 0.001 -0.102 0.000 51.716 1349

0.771 16.273 17.115 2.436

0.380 0.000 0.000 0.119

β -0.029

0.091 -0.001 0.001 -0.124 0.000 168.351 2005

Model 3 χ2 3.639

Sig. 0.056

42.630 0.002 39.796 32.975 6.197

0.000 0.969 0.000 0.000 0.013

It is also found that firm’s sales growth decreases by 4.5% than its industry when they are in FDnODt as revealed by model 2 of table 5. This implies that operating distress is more severe than FDnOD. It is argued that firms face profitability problems in FDnOD and affect customer’s confidence less severely. It is consistent with Babenko (2003) who argued that lose customers’ loyalty decreases more as firms progress to default.On the other hand collection period documents positive results for all three models in table 5. High collection period indicates loose credit policy and blocks firm’s cash flow to affect productivity negatively. So, cost of loose credit policy is more than its benefits of increasing market share. However, results for TAGrw (total assets growth) show negative impacts of increased total assets to opportunity losses. 4.4.1. Pre Distress Era Table 6 provides the results from GLM regression analysis for pre distressed era into three models. The dependent variable is opportunity loses. Positive relationships are found between opportunity losses and all three distress categories. It is found that firms bear 5.2% opportunity losses prior to operating distress as demonstrated in model1. It is consistent with previous studies that reveal that firms bear opportunity losses before and after entering to financial distress. Chen and Merville, (1999) and Pindado & Rodrigues (2005) also found that firms bear opportunity losses prior to financial distress. However, our results are found insignificant for FDnOD t+1 that indicates that only operating distress contributes to opportunity losses prior to its occurrence. Table 6:

Pre distress GLM Regression Analysis

(Constant) OD t+1 FDnOD t+1 FD t+1 SecDist Collection Period Total Assets Growth Sales to total assets Model χ2 No. of Observations

β -0.004 0.052

0.014 0.001 -0.149 0.000 102.669 1,554

Model 1 χ2 0.073 8.834

0.492 26.881 37.937 6.382

Sig. 0.787 0.003

0.483 0.000 0.000 0.012

β -0.005

Model 2 χ2 0.092

Sig. 0.762

0.004

0.046

0.830

0.030 0.000 -0.154 0.000 52.362 1,337

2.303 7.112 36.289 1.898

0.129 0.008 0.000 0.168

β -0.002

0.030 0.017 0.001 -0.149 0.000 108.702 1,818

Model 3 χ2 0.010

Sig. 0.919

4.519 0.968 30.483 42.701 8.782

0.034 0.325 0.000 0.000 0.003

Other controlled variables also show similar results of table 5 and document positive coefficients for average collection period and sales to total assets while negative effects are evidenced for total assets growth as demonstrated in table 6.


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4.4.2. Post Year Effects of Distress In order to explore the effects of distress in post year to its occurrences to opportunity losses, GLM regression analyses are used and presented in table 7. Results reveal that firms show negative opportunity losses in post year to distress. It is consistent with the view that distress leads to organizational efficiencies and firms learn how to overcome such problems. It is logical as entering to such anguish situations enforce managers to take corrective measures in order to improve organizational efficiencies. Makridakis (1991) also suggest that in distress period firms learn how to tackle dynamic environment to gain subsequent organizational efficiencies. It is done through change in operational strategies as demonstrated by Ofek (1993) or through managerial effectiveness as described by Diamond (1993) and Gilson (1989). One can not refuse the negative effects of distress even post year to distress but still its benefits are more than its costs. It is consistent with Khal (2002) who also suggest that financial distress contain more benefits than its costs. However, these studies found such organizational efficiencies in early stages of distress rather than to its full blow or at end stage. It is found that firm’s sale growth increased by 5.1% than its industry in post year to operating distress. While FDnOD firms show insignificant increase in sales growth of 1.7% than its industry. Results show organizational efficiencies in pot year to distress because of sample of ongoing firms. However, results for firms at full blow distress or at end stages of its life can be different and lead to high opportunity losses in post year to distress. Table 7:

Post year to Distress GLM Regression Analysis

(Constant) OD t-1 FDnOD t-1 FD t-1 SecDis Collection Period Total Asset Growth Sales to total assets Model χ2 No of Observations

β 0.032 -0.051

0.030 0.001 -0.183 0.000 132.943 1,783

Model 1 χ2 4.001 10.292

2.863 34.744 59.544 17.766

Sig. 0.045 0.001

0.091 0.000 0.000 0.000

β 0.022

Model 2 χ2 1.525

Sig. 0.217

-0.017

0.736

0.391

0.020 0.001 -0.125 0.000 64.961 1,225

1.164 19.775 25.668 8.715

0.281 0.000 0.000 0.003

β 0.030

-0.039 0.033 0.001 -0.158 0.000 134.394 2,005

Model 3 χ2 3.885

Sig. 0.049

8.243 4.382 37.527 52.763 20.668

0.004 0.036 0.000 0.000 0.000

It is also found that the value of organizational efficiencies in the case of OD firms show augmented coefficient as compared to value of organizational efficiencies for FDnOD firms. However, insignificant results are found in case of FDnODt-1. It can be due to less attention given by managers to FDnOD due to its less severe effects. Despite such organizational efficiencies, still firms face opportunity losses due to economic shocks. Table 7 shows that dummy variable of distressed industry significantly contribute to opportunity losses. Moreover, other controlled variables also show same results as described in previous section. So, ongoing Pakistani manufacturing firms listed at KSE show organizational efficiencies in term of better sales growth in post distressed year.

5. Conclusion Current study tried to explore the cost of financial distress that is one of the most important topics of corporate finance as all stakeholders are interested to know firms’ especially in the case of distressed situations. Researchers have defined financial distress into different ways. Some of them viewed it as bankruptcy or default synonymously while other suggested it as a state between firms’ healthy and bankrupt status. A number of researchers have explored the concept of cost of financial distress with different methodologies earlier. They divide cost of financial distress into direct cost of default or bankruptcy such as administrative or legal costs or indirect cost of financial distress such as opportunity losses. The concern of current study is only indirect cost of financial distress in term of

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opportunity losses. It is argued that ongoing firms also bear indirect cost of financial distress before and after entering to distressed situations. It is consistent with Chen and Merville, (1999) and Pindado & Rodrigues (2005) who also found high opportunity losses for ongoing firms before and after entering to financial distress. Current study divides financial distress into operating distress where firms are unable to meet their operating expenses and financial but not operating distress where firms are unable to meet their financial costs from heir current proceeds. Moreover, sample of ongoing firms who shows at least one distress observation is taken. These two methodological attributes differentiate current study from previous works. Results show that firms bear opportunity losses before and after to distress. Reason behind such underperformances can be lose of customers’ confidence and loyalty. It is found that firms’ sale growth decreased by 5.2% and 11.9% before and after entering to operating distress respectively. Theses results are 0.4% and 4.7% in case of pre and post FDnOD respectively. However, results for pre financial but not operating distress are found insignificant. Moreover, augmented opportunity losses are also found in case of operating distressed firms as compared to FDnOD firms. This augmented effect is attributed to severe liquidity problems in operating distress as compared to FDnOD. In conclusion firms bear opportunity losses before and after entering to distress while operating distress documents high results in this respect. Moreover, it is also found that firms gain organizational efficiencies in post year to distress that is consistent with previous literature. Such organizational efficiencies can be in the form of operational efficiencies or change to effective management in post year of distress. It is found that industry adjusted sales growth increased by 5.1% in post year to operating distress. While FDnOD show increase of 1.7% of industry adjusted sales growth in post year to distress. However, statistics do not signify the results for FDnOD. So, in post year to distress firms also gain operational efficiencies especially in case of operating distress.

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