THE EFFECT of FINANCIAL DISTRESS on OPERATING CASH FLOWS

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This paper provides new evidence on the financial performance of Joint stock firms by emphasizing the role played by financial distress. The purpose of this paper is specify a model for early predication of financial distress that allows us to predict the specific nature of financial distress that can effect operating cash flow and which can lead the firm toward bankruptcy and to see the effect of financial distress on operating cash flows of companies listed on Karachi Stock Exchange. Financial distress is a situation when a firm’s assets’ value falls below some threshold. Firm starts to incur losses and it is not in a position to generate positive cash flows. A firm enters to financial distress before it goes bankrupt. We have studied 67 firms listed on Karachi Stock Exchange to see the effect on financial distress on their cash flows. Our sample includes financially distressed as well as financially health firms. We have incorporated financial data of consecutive four years (2003 to 2008) of 67 firms. In order to measure the financial distress we have used Modified Altman Z-Score as a proxy. Other independent variables, which have been used, are size of the firm, Working Capital, Working capital productivity and Operating Profit. By regressing these Five variables (Financial Distress, Working Capital, Size of Firm, Working capital productivity and Operating Profit) on Operating Cash Flows we have found that financial distress have a negative effect on corporate cash flows. However Size of Firm, Operating Profit and Working capital productivity have positive effect on Corporate Cash Flows. Working Capital has a negative effect on operating cash flows. We have estimated our model with the help of regression analysis. Our study is unique in a sense that there is a dearth of literature on financial distress with special reference to Pakistan. Keywords: Financial distress, Working capital, Working capital productivity, Bankruptcy, Altman Z-Score, Corporate Failure, Insolvency, Survival Analysis.

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Table of Contents

2 Abstract 2 Table of Contents 4 1. Introduction 5 2. Literature Review 8 3. Methodology 13 Data and Variables 13 Measurement of Variables 14 Operating Cash Flows (OCF) 14 Explanatory Variables 14 Financial Distress (FD) 14 Size of Firm (SZ) 15 Operating Profit (OP) 15 Working Capital (WC) 15 Working capital Productivity (WCP) 15 Hypotheses Testing 15 4. Empirical Framework 17 5. Results 17 Model Summary (b) 20 6. Discussion 22 Conclusion 23 Refererences 25

1. Introduction

Financial Statements basically show the historical performance or record of the company at some previous point of time. By the time when financial statements are made public, changes are many economical areas such as market conditions, currency exchange rate and inflations can change the values of assets and liabilities. In this case there often exist discrepancies between book value of assets and their market values. In above case there might be companies that are healthy and many go through period of financial distress. In particular is the threat of not being able to meet debt obligations. The first Indication of financial distress is when firm does not have enough liquid assets (short-term assets) to cover (pay for) current liabilities (short-term liabilities) when this happen than firm ability to covering long-term liabilities is reduced resulting in creditors taking on more risk than the investment of loaning money to the firm is worth. When company is facing financial distress, book value of company liabilities can become worth more than the market value of the same liabilities. If this happen, than firm is in danger of not meeting its obligations to creditors. In this case creditors may not be paid and in worst of financial distressed time, the creditors may receive nothing in interest or principal, if the firm files for bankruptcy. The importance of financial-decision making goals is to increase shareholders’ value and to keep them away from financial distress. The Predicting of financial distress is an early warning signal to keep investors from being loss. It has been more than 70 years, since Ramser & Foster, and Fitzpatrich in 1931-1932, and 44 years, since Beaver (1966) but still they have not found the theory of financial distress ( Laclere M,2006). They were more statistical consideration then the intuitive models or fundamental causes of financial distress (Ooghe & Prijcker, 2007; Balcean & Ooghe, 2004). Since The Altman’s model widely used among the investors, though it is not an intuitive model, once a firm is predicted having a financial distress next year, it has been treated as it has been financial distress currently (whtaker, 1999). This work aims at studying the effect of financial distress on operating cash flows of corporations. The interest in the area of financial distress has increased due to considerable number of corporate failures around the globe in recent years especially since the early 1990s. Notable failures include Global Crossing, Enron, Adelphia, Worldcom, HH Insurance, One Tel, and Ansert Airlines in 2001, and most recently FIN Corp in 2007. Financial distress is defined as a low cash flow state of a firm in which it incurs losses without being insolvent or financial distress is a term in Corporate Finance used to indicate a condition when promises to creditors of a company are broken or honored with difficulty. Financial distress is different from insolvency. Financially distressed companies have lower profitability, higher leverage, lower past excess returns and larger size compared to active companies. The failure or bankruptcy of financially distressed firms results in significant direct and indirect costs to many stakeholders; including shareholders, managers, employees, lenders and clients. For instance Shareholders lost nearly $11 billion when Enron’s stock price, which hit a high of US$90 per share in mid-2000, plummeted to less than $1 by the end of November 2001. Failure of Australia’s second largest insurance company, HIH Insurance, in 2001 represents the 2nd largest corporate collapse in Australia’s history. The collapse of HIH entailed huge individual and social costs. The deficiency of the group was estimated to be $3.6 billion and $5.3 billion. The lineup of major corporate bankruptcies was capped by the mammoth filings of Conseco ($56.6 billion in liabilities), WorldCom ($ 46.0 billion), and Enron ($ 31.2 billion actually almost double this amount once you add in the enormous amount of off-balance liabilities making it the largest bankruptcy in the united states. Such costs may be avoided if financially distressed companies are identified well before failure. Then corrective measures can be taken to save the company from ominous bankruptcy. Much of the literary work on financial distress relates to failure prediction and survival analysis of firms. Some studies on financial distress have been made in the context of corporate risk management. Our study aims at studying the financial distress along with key performance indicators of the corporations to see how these indicators (profitability, Size of Firm, Working capital and Working capital productivity.) co-move with the financial distress. There is not sufficient literature on studying the effect of financial distress on corporate cash flows. Especially in Pakistan, the area has not been researched thoroughly. We estimate a linear model, which helps us in the measurement of magnitude of effect of financial distress on the operating cash flows. Along with financial distress, we also measure the effect of size of firm, operating profits, working capital and working capital productivity on operating cash flows. We have included both financially distressed and financially healthy firms in our sample. Our findings provide evidence that financially distressed Pakistani firms face adverse cash flow problems. The remainder of this paper is organized as follows. Section 2 presents a review of literature in the area of financial distress. Section 3 describes Methodology and research design, i.e. data and variables used in the study. Section 4 describes the empirical framework (Model Description). Section 5 presents the results of the regression analysis. Section 6 Discussion and concludes the paper.

2. Literature Review

The effect of financial distress on financial structure decisions is another conflicting point. According to the static trade-off theory, both the advantages of debt (tax shields) as well as its disadvantages (insolvency costs) have been traditionally considered in the capital structure literature. This trade-off between the benefits and costs of debt focuses on ex-ante insolvency costs, whose negative effect on leverage has been theoretically justified (Barnea et al., 1981) as well as empirically documented (Miguel & Pindado, 2001). According to (Warner (1977), Altman (1984), Franks & Touros (1989), Weiss (1990), Asquith, Gertner and Scharfstein (1994), Opler &Titman (1994), Sharpe (1994), Denis & Denis (1995), Gilson (1997) Financial distress has both direct and indirect costs. (Opler & Titman (1994), (Shleifer & Vishny (1992), Direct costs of distress, such as Litigation fees are relatively small. Indirect costs, such as loss of market share and inefficient asset sales are believed to be more important, but they are also much harder to quantify. The debate on financial distress started after the occurrence of corporate failures. Theorists and researchers emphasized on how to save a firm from being financially distressed. Opler & Titman (1994) provide empirical evidence that financially distressed firms lose significant market share to their health competitors in industry downturns. Chevalier (1995) was of the view that financially distressed firm is likely to violate the debt covenants and these violations put heavy costs on the firm. Froot et al. (1993) established that financially distressed firms forego positive NPV projects. Researchers are of the view that a firm with a high leverage has an incentive to engage in hedging activities. The measurement of financial distress has also been debatable in the literary circles. Some researchers use leverage as a proxy for financial distress. Failure prediction models use firm’s distance to default as a proxy of the financial distress. Some models used accounting based measures of financial distress. Hill, Perry & Andes(1996), Ward & Foster(1997), DeYoung(2003), Nikitin(2003) and laitinen(2005) use only financial ratios as financial distress predictors; while Altman(1969), Ahrony, Jones and Swary(1980), Altman & Brenner(1981), Broenstein & Rose(1995) and Fama & French(1995) used only market based covariance. Majority of researchers believe that financially distressed firms appear to exhibit lower profitability, lower historic excess returns and larger size than active companies. Beaver (1966) pioneered the development of model for corporate failure prediction. He found that the model can predict failed firms for at least five years before to failure. His model was based on financial ratios as single predictors of financial distress. Altman (1968) criticized the model and upheld that the model may give inconsistent and confusing classifications results for different ratios on the same firm. Altman (1968) came up with his own model which can handle multiple financial ratios in predicting company’s failure. In Altman (1968) study, five financial ratios include (1) working capital to total assets (2) retained earnings to total assets (3) earnings before interest and tax to total assets (4) market value of equity to par value to debt and (5) sales to total assets. His model found to be the best predictor of corporate bankruptcy. The model is very popular and is called Z Score model. The critics of this model say that it violates the assumption about the multivariate normal distribution of independent variables. Castagna & Matolcsy (1981) pioneered the study of corporate financial distress and failure .In USA and Europeon countries, survival analysis techniques form the basis for a number of studies in financial distress research area. Cash flow is strongly related to financial distress. Henbry (1996) studied whether adding cash flow information will improve current bank failure prediction models. Some researchers were of the view that combining market-driven variables with accounting ratios provide more accuracy to the financial distress models. Compartive studies have also been done in the area of financial distress. Rommer(2005) compared the financial distress predictors between French, Italian and Spanish firms using competing risk models. There are few research studies on financial distress in Asian context. For example, Honjo(2000) employs multiplicative hazards model for investigating business failure for new firms in Japanese manufacturing industry whereas Raj & Rinastiti(2002) use Cox proportional hazards model to examine the failed banks in Asia during 1997 Asian crisis. Some of the prior corporate failure studies focus the analysis on specific industry sector. Chen and Lee (1993) focus the study on oil and gas industry. Similarly, Lee & Urrutia(1996) have studied the property liability insurance industry. Researchers have established that income capacity, operating efficiency and leverage are important factors in explaining corporate failure and financial distress.According to Hossari & Rahman (2005), empirical investigation of corporate failure may be classified in to two categories; the studies that do not use financial data and those which use financial data which may be further classified in to those that use non ratio financial data and those that make use of financial ratios in modeling corporate collapse. The use of financial ratios to predict corporate failure has been well established since the original study of Beaver (1966). Most of the empirical research in this area has used financial ratios and have been successful in discriminating between failed and successful firms. However despite this success, financial ratio models have been criticized because of window dressing of figures on the part of the firm by use of creative accounting. Critics emphasize the use of market-based data along with financial ratios. Many studies make use of market data for analyzing the financial distress of companies. Aharony, Jones and Swary (1980) find differences in the behavior of total and firm-specific variances in returns four years before formal bankruptcy is announced. Altman and Brenner (1981) suggest bankrupt firms experience deteriorating capital market returns for at least a year before to bankruptcy. Clark and Weinstein (1983) suggest that there is negative market return at least three years before to bankruptcy. Mossman et al. (1998), Shumway (2001) and Turetsky and McEwen (2001) also support that there is a relationship between market based variables and the likelihood of corporate financial distress. Company specific variables such as company age, size of the firm and squared size have also been used in the prediction of financial distress. Prior studies suggest that company age and size effect its endurance. The younger and smaller firms are more likely to fail than established or bigger firms as they don’t have sufficient experience in the business. Larger firms are expected to better manage and protect them from financial distress than smaller firms (Audretch & Mahmood, 1995; Honjo, 2000). Small firms have a higher probability of entering financial distress because they are not resistant to the shocks they might encounter and the large firms have a high probability of entering financial distress because they might have inflexible organizations, problems with monitoring managers and employees and difficulties with providing efficient intra-firm communications. Researchers have also established that probability of financial distress is a decreasing function of firm size. Luoma & Laitinen ( 1991) established that the symptoms of financial distress are observable from the deterioration of financial ratios or the effect of such ratios on corporate failure don’t stay constant over time. Studies provide evidence that financial distress is not without costs. Financially distressed firms have to incur direct bankruptcy costs, higher contracting costs, the loss of tax shields and loss of valuable investment opportunities All the above studies provide us a solid base and give us idea regarding effect of financial and its components on operating cash flow. They also give us the results and conclusions of those researches already conducted on the same area for different countries and environment from different aspects. On basis of these researches this paper extends the previous research work done on financial distress. We have used modified Altman Z Score as a proxy for the financial distress. After including the financially distressed and financially healthy firms in our sample, we have seen the effect of financial distress on corporate cash flows. Prior to this work hardly any paper can be seen which studies the impact of financial distress on corporate cash flows, especially in Asian context. Our work adds to the literature in a sense that it not only identifies the financially distressed firms but also measures the effect of financial distress on operating cash flows of the firms listed on Karachi Stock Exchange. Our work also contributes to the literature in establishing a fact that whether the model of financial distress developed by Altman is relevant in Pakistan’s Corporate Environment.

3. Methodology

The purpose of this research is to contribute towards a very important aspect of financial management known as financial distress effects on operating cash flow with reference to Pakistan. Here we will see the relationship between financial distress effect on profitability of 64 Pakistani Joint stock firms listed on Karachi stock Exchange for a period of six years from 2003 – 2008. This section of the article discusses the firms and variables included in the study, the distribution patterns of data and applied statistical techniques regression analysis in investigating the relationship between financial distress and operating cash flow.

Data and Variables

Secondary data has been used in this study. The financial data of 67 companies listed on Karachi Stock Exchange has been compiled. The source of data is Statistics Department, State Bank of Pakistan. We have used financial data of 67 companies for four consecutive years i.e. from 2003 to 2008. We have selected 67 companies from different sectors such as Fuel and Energy, Cement, transport and communication, Engineering, Sugar, Chemical, Paper and Board and Miscellaneous sectors. Our sample consists of financially healthy as well as financially distressed companies. In this study we have operating cash flows as dependent variable and Financial Distress as independent variable. Along with financial distress we have used four other variables; firm size, operating profit working capital and working capital productivity.

Measurement of Variables

Operating Cash Flows (OCF)

OCF has been arrived at by adding depreciation and current liabilities to the operating profit and deducting the accounts receivables there from have measured OCF. OCF is a dependent variable in this study.

Explanatory Variables

Financial Distress (FD), Size of Firm (SZ), Working Capital (WC), Working capital productivity (WCP) and Operating Profit are explanatory variables.

Financial Distress (FD)

In order to measure financial distress we have used modified Altman Z-Score model. It has been calculated as follows Altman Z Score= EBIT/Total Assets + Sales/Total Assets + 1.4*Retained Earnings/Total Assets + 1.2*Working Capital/Total Assets Where EBIT stands for earnings before income tax and interest. If Altman Z-Score is 3 or greater than 3, firm is said to be in good financial health. If Altman Z Score is greater than 2 but less than 3 firms has some risk of entering financial distress. And if firm has Altman Z Score of less than 2, it means that firm has entered financial distress and it may become bankrupt.

Size of Firm (SZ)

We have measured the size of firm (SZ) by taking the natural logarithm of the total sales of the firm.

Operating Profit (OP)

Operating profit means the profit associated with the core operations of the business.

Working Capital (WC)

Working Capital has been measured by deducting current liabilities from current assets. WC= Current Assets – Current Liabilities

Working capital Productivity (WCP)

Working capital productivity is an expression of how effectively a company spends its available funds compared with sales or turnover, the working capital productivity figure helps to establish a clear relationship between its financial performance and process improvement. Higher will be the figure better would be working capital productivity. Working capital productivity = Sales ÷ (Current assets – Current liabilities)

Hypotheses Testing

Since the aim of this study is to examine the relationship between financial distress and operating cash flow, the study makes a set of testable hypothesis {the Null Hypotheses H0 versus the Alternative ones H1}.

Hypothesis 1

The first hypothesis of this study: H01: There is positive effect of financial distress on operating cash flow of Pakistani firms. H11: There is a negative effect of financial distress on operating cash flow of Pakistani firms.

Hypothesis 2

The second hypothesis of the study is: H02: There is positive effect of operating profit on operating cash flow of Pakistani firms. H12: There is negative effect of operating profit on operating cash flow of Pakistani firms

Hypothesis 3

The Third hypothesis of the study is: H03: There is positive effect of size of firms on operating cash flow of Pakistani firms. . H13: There is negative effect of size of firms on operating cash flow of Pakistani firms.

Hypothesis 4

The Fourth hypothesis of the study is: H04: There is positive effect of working capital on operating cash flow of Pakistani firms. H14: There is negative effect of working capital on operating cash flow of Pakistani firms.

Hypothesis 5

The Fourth hypothesis of the study is: H05: There is positive effect of working capital productivity on operating cash flow of Pakistani firms. H15: There is negative effect of working capital productivity on operating cash flow of Pakistani firms.

4. Empirical Framework

Our estimated model, which shows the effect of financial distress on corporate cash flows, is as under: OCF = B – B1FD + B2 SZ + B3 OP -B4 WC + B5WCP

In this equation:

OCF = Operating Cash Flows B= Constant Term or intercept of the equation B1= Slope of the variable financial distress (FD) FD= Financial Distress B2= Slope of the size variable SZ= Size of the firm B3= Slope of the operating profit variable OP= Operating Profit B4= Slope of the working capital WC= Working Capital B5= Slope of the working capital productivity WCP= Working capital productivity

5. Results

The model shows that variable FD has a negative coefficient, which means that with the FD has a negative effect on the operating cash flows. Variable Size (SZ) has a positive coefficient which means that greater the size of the firm, the more cash flows for the firm from operations. Operating Profit (OP) has a positive coefficient, which means that OP has robust effect on Operating cash flows. Working capital has negative coefficient, which means that it is negatively related to cash flows from operations and working capital productivity (WCP) has a positive coefficient, which means Sales growing faster than the resources required to generate them is a clear sign of efficiency. B in this equation is intercept of the model or constant term. Let us see some descriptive statistics of our analysis. The table shows the mean values of OCF, FD, OP, SZ, WC and WCP.

Descriptive Statistics

Mean Std. Deviation N OCF 4525.2953 12646.70110 67 FD (Altman Z-Score) 1.926 1.5573 67 Firm Size 7.45587 2.162929 67 Working Capital 882.35 2587.491 67 Working Capital Productivity Operating Profit 6.75426 1348.82373 1.876545 5619.621546 67 67 Let us see the correlation matrix of the dependent and explanatory variables. The matrix shows that OCF is negatively related to FD while it is positively related to SZ, WC, and OP. It shows that FD is negatively related to OCF and OP while positively related to SZ and WC. Firm Size (SZ) is positively related to all variables. Similarly WC is negatively related to WCP and positively related to positive correlation with all other variables. Operating Profit (OP) has negative correlation with FD while positive correlations with OCF, SZ, WC and WCP .Working Capital Productivity (WCP) is negatively related to WC and positively related to all other variables.

Correlations

OCF FD(Altman Z-Score) Firm Size Working Capital Working Capital Productivity Operating Profit Pearson Correlation OCF 1.000 -.110 .443 .645 .387 .928 FD(Altman Z-Score) -.110 1.000 .174 .020 .225 -.044 Firm Size Working Capital Working Capital productivity Operating Profit .443 .421 .645 .928 .174 .225 .020 -.044 1.000 1.500 -.043 .309 .343 .348 1.000 .752 .174 -.100 2.50 .285 .309 1.032 .752 1.000 Sig. (1-tailed) OCF

.

.189 .000 .000 .000 .000 FD(Altman Z-Score) .189

.

.079 .437 .000 .363 Firm Size .000 .079

.

.002 .079 .005 Working Capital Working Capital Productivity .000 .000 .437 .072 .002

.

.387.

.

.000 Operating Profit .000 .363 .005 .000 .005

.

N OCF 67 67 67 67 67 67 FD(Altman Z-Score) 67 67 67 67 67 67 Firm Size 67 67 67 67 67 67 Working Capital Working Capital Productivity 67 67 67 67 67 67 67 67 67 67 67 67 Operating Profit 67 67 67 67 67 67

Variables Entered/Removed (b)

Model Variables Entered Variables Removed Method 1 Operating Profit, FD(Altman Z-Score), Firm Size, Working Capital(a) Working Capital Productivity

.

Enter a. All requested variables entered. b. Dependent Variable: OCF Consider the Model Summary of our Estimated Regression Model.

Model Summary (b)

Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson 1 .954(a) .911 .905 3892.72617 2.145 a. Predictors: (Constant), Operating Profit, FD (Altman Z-Score), Firm Size, Working Capital (WC), Working Capital Productivity (WCP) b. Dependent Variable: OCF Coefficient of determination (R Square) or Model Fit is 0.911 which means that explanatory variables are capable of explaining 91% variations in the dependent variable i.e. Operating cash flows OCF. The ANOVA Table shows us the F-statistics. F-Statistics shows the overall strength of the model. F Value is 158.653 which is quite high. Hence we reject the null hypothesis that explanatory variables have positive effect on operating cash flows and we establish that Financial distress (FD) has a negative effect on operating cash flows (OCF). ANOVA shows that our model is quite good to estimate the effect of financial distress (FD), Size of the Firm, Operating Profit, Working Capital and Working Capital Productivity on Operating Cash Flows.

ANOVA (b)

Model Sum of Squares df Mean Square F Sig. 1 Regression 9616471554.991 4 2404117888.748 158.653 .000(a) Residual 939505654.596 62 15153317.010 Total 10555977209.586 66 a. Predictors: (Constant), Operating Profit, FD (Altman Z-Score), Firm Size, Working Capital, Working Capital Productivity b. Dependent Variable: OCF Consider the table which shows the t-values for our variables. The table shows that the size of the firm (SZ), operating profit and Working Capital Productivity (WCP) are statistically significant to affect the operating cash flows. If we ignore the sign FD is statistically significant to affect the corporate cash flows.

Coefficients (a)

Model Standardized Coefficients t Sig. Correlations Beta Zero-order Partial Part 1 (Constant) -3.051 .003 FD(Altman Z-Score) -.101 -2.605 .011 -.110 -.314 -.099 Firm Size .214 5.192 .000 .443 .550 .197 Working Capital Working Capital Productivity -.165 .245 -2.818 5.428 .006 .000 .645 .389 -.337 .500 -.107 .187 Operating Profit .982 16.916 .000 .928 .907 .641 a. Dependent Variable: OCF

Coefficient Correlations (a)

Model Operating Profit FD(Altman Z-Score) Firm Size Working Capital Working Capital Productivity 1 Correlations Operating Profit 1.000 .105 -.100 -.724 -.200 FD(Altman Z-Score) .105 1.000 -.187 -.046 -.185 Firm Size -.100 -.187 1.000 -.165 -.285 Working Capital Working Capital Productivity -.724 1.500 -.046 -.187 -.165 -0.45 1.000 -.058 -0.56 1.000 Co-variances Operating Profit .017 4.327 -3.154 -.027 -2.564 FD(Altman Z-Score) 4.327 98915.750 -14174.525 -4.175 -12178.252 Firm Size -3.154 -14174.525 58048.854 -11.340 58045.85 Working Capital Working Capital Productivity -.027 -3.254 -4.175 -12175.252 -11.340 4.327 .082 -.028 -4.585 .958 a. Dependent Variable: OCF

Case wise Diagnostics (a)

Case Number Std. Residual OCF Predicted Value Residual 56 3.892 11960.00 -3190.8577 15150.85766 62 4.706 27198.30 8880.1328 18318.16716 a. Dependent Variable: OCF

Residuals Statistics (a)

Minimum Maximum Mean Std. Deviation N Predicted Value -7234.8931 94892.6719 4525.2953 12070.79593 67 Residual -6178.19580 18318.16797 .00000 3772.92117 67 Std. Predicted Value -.974 7.486 .000 1.000 67 Std. Residual -1.587 4.706 .000 .969 67 a. Dependent Variable: OCF

6. Discussion

Analysis on financial distress prediction model with modified Altman-Z Score results shows that our model is robust in explaining the variations in dependent variable i.e. Operating Cash Flows (OCF). Our estimated model shows that the variable Financial Distress (FD) is negatively related to corporate cash flows. However Firm Size (SZ) Operating Profit (OP) and Working Capital Productivity (WCP) are positively related to FD. In this study we found another negative relationship between Working Capital (WC) and operating cash flow. This study shows that financial distress negatively affects the operating cash flow of firm and if firm would be big in case of size than effect of financial distress on operating cash flow would not be as negative as this will be in case of small firm and positive effect of Working capital productivity and operating cash flow shows that how effectively a company spends its available funds compared with sales or turnover, the working capital productivity figure helps to establish a clear relationship between its financial performance ,process improvement and operating cash flow. Negative effect of working capital on operating cash flow is obvious because it shows that capital not being put to work properly is being wasted, which is certainly not in investors’ best interests.

Conclusion

Our results show that our model is robust in explaining the variations in dependent variable i.e. Operating Cash Flows (OCF). We have used the financial data of 67 firms, half of which were facing financial distress. We measured the effect of Financial Distress (FD) on the Operating cash flows. Our estimated model shows that the variable Financial Distress (FD) is negatively related to corporate cash flows. However Firm Size (SZ) Operating Profit (OP) and Working Capital Productivity (WCP) are positively related to FD. The notion that large firms in Size have more probability of entering financial distress has not been substantiated by our study. Rather our study shows that the larger the size of the firm, the more the operating cash flows and company effectively spends its available funds compared with sales or turnover, the working capital productivity figure helps to establish a clear relationship between its financial performance and process improvement and therefore less chances of being financially distressed. Another important finding of the study is negative relationship between working capital (WC) and Operating Cash Flows (OCF). It means the more working capital we have, the less operating cash flows we have. Actually greater working capital means we have more funds tied up which have not been gainfully utilized in the business. This may be as a result of an error of estimating cash for business requirements on the part of the management. Huge working capital has its opportunity cost and that cost may be in the shape of less operating cash flows and less profitability. Our analysis strongly supports that higher operating profits result in higher operating cash flows for the firm; and this is true for small firms as well as for large firms in size. Summing up we can say that by using this model, on large data set we can obtain more generalize ability of the results.

Refererences

Altman E. (1968). Financial Ratios, Discriminant Analysis and the prediction of Corporate Bankruptcies. Journal of Finance, 23,589-609. Aharony, J., Jones, C. P. & Swary, I. (1980).An analysis of risk and return Characteristics of corporate bankruptcy using capital market data.Journal of Finance, 35(4), 1001-1016. Altman, E.I. & Brenner, M.(1981).Information effects and stock market responses to signs of firm deterioration. Journal of Financial and Quantitative Analysis, 16(1), 35-51. Audretsch, D.B. & Mahmood, T. (1995). New Firm Survival: New Results using a hazard function. The review of Economics and Statistics, 77(1), 97-103. Beaver, W. H. (1966). Financial Ratios as predictor of failure’, Empirical Research in Accounting: Selected Studies. Supplement to Vol. 4,71-111. Borenstein, S. & Rose, N. L.(1995). Bankruptcy and pricing behavior in U.S. airline markets. The American Economic Review, 85(2) ,397-402. Castagna, A. D. & Matolcsy, Z. P. (1981).The prediction of Corporate Failure: Testing the Australian experience. Australian Journal of Management, 6(1) ,23-50. Chen, K. C. W. & Lee, C. W. J. (1993).Financial ratios and corporate endurance: A case of the oil and gas industry. Contemporary Accounting Research, 9(2), 667-694. Clark, T. A. and Weinstein, M . I. (1983). The behavior of the common stock of bankrupt firms. Journal of Finance, 38(2),489-504. Chevalier, J., (1995).Capital Structure and Product Market Competition? An Empirical Evidence from Super Market Industry’,Journal of Finance, 50,1112-1195. Froot, K. A., D. S. Scharfstein and J.C. Stein, (1993).Risk Management: Coordinating Corporate Investments and Financing Policies. Journal of Finance ,5,1629-1658. Opler, T. & S. Titman, (1994). Financial Distress and Corporate Performance. Journal of Finance 49,1015-1040.

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