The use of earnings and cash flows model in predicting corporate financial distress: Evidence from retail merchandizing enterprises listed in IDX

A. Selowidodo, R. Wikansari, B.P. Sutjiatmo & M.S. Kurniawan

Polytechnic APP Jakarta, Ministry of Industry, Indonesia

A.T. Rachmadi

Hokkaido University, Hokkaido, Japan

ABSTRACT: This research is aimed to examine and analyze the empirical effect of earnings and cash flows in predicting corporate financial distress of the retail merchandizing enterprises.

1 INTRODUCTION

Corporate financial distress is the situation when a company cannot meet or face difficulty to pay off its financial obligation to the creditors. Recently, cor­porate financial distress has become a famous topic in finance. Thus, firm’s financial health is a crucial indicator for interested users to know more about company’s financial condition. That is why corporate financial distress has a big impact on management, shareholders, employees, creditors, customers, and other stakeholders.

The prediction of corporate financial distress and bankruptcy has been a great interest of research in the late 1960s. In addition, corporate financial dis­tress prediction has become an integral part of cor­porate governance as it helps all the stakeholders analyze to which direction a company is taking. According to Warner (1977), companies that are in financial distress tend to exhibit decline in market value over time.

Some studies found that earnings may be more useful in predicting corporate financial distress. McCue (1991) investigated the hospitals’ financial health in California. He showed that earnings model is more powerful than cash flows model. Similar research has been conducted by Djongkang & Rita (2014) on whether earnings or cash flows model can be used in predicting corporate financial distress of the company sector of textile mill product and apparel and other textile products listed in IDX. The result shows that earnings model can better predict corporate financial distress. In other words, it is harder to predict corporate financial distress by cash flows model than by earnings model. In contrast, Azis & Lawson (1989) found that cash flows-based model is more effective to predict corporate financial distress. However, according to Sharma (2001), stud­ies using cash flows model for predicting company failure is not conclusive due to the limitations such as improperly measured cash flow operations.

In Indonesia, little empirical research has been done so far to assess the factors that are significant under corporate financial distress. Lack of research on the factors that contribute to corporate financial distress signifies that users do not have proper infor­mation about future prospects of a firm. Therefore, it is important to study the factors that are significant to financially distressed companies in Indonesia and thus provide a clearer picture in determining the sig­nificance of financial indicator while detecting finan­cially distressed companies.

An interesting discussion issue in predicting cor­porate financial distress should be focus on both earnings and cash flows. Hence, the objective of this study is to empirically examine and analyze whether earnings model or cash flows model can better to predict corporate financial distress of the retail mer­chandizing enterprises listed in IDX for the period of 2014-2017.

2 LITERATURE REVIEW AND HYPOTHESIS

In modern concepts, financial distress is defined in many ways.

(1990) defined distress firms as firms that declared bankruptcy and firms that had negative cumulative earnings over three consecutive years. Ross (1996) stated, “Financial distress is a situation where a firm’s operating cash flows are not sufficient to sat­isfy current obligations and the firm is forced to take corrective action”. It is difficult to define precisely what distress or bankruptcy is, due to the variety of accounting procedures or rules in different countries or at different time spots as well as various events that put firms into financial distress.

2.1 Earnings-financial distress relationships

Corporate financial distress happens before bank­ruptcy. According to Wruck (1990), there are several pointers that can be used to detect corporate financial distress such as a reduction in the level of dividends issued out and retrenchment of employees and resig­nation of top management. In addition, Whitaker (1999) stated that the process of financial distress starts with a company’s inability to pay short-term obligations at the due date. The main reasons behind financial distress can be attributed to inappropriate asset management, corporate governance, or finan­cial structure (Gilberst et al. 1990). Adeyemi (2012) suggested that lack of adequate funds is one of the major factors leading to financial distress as capital has the capability to absorb losses. Nevertheless, most managers focus on and blame external factors when their businesses fail instead of evaluating the internal factors (Scherrer 2003).

There have been many models developed in order to predict corporate financial distress. Many of these models involve the use of ratio analysis to analyze failed companies. Beaver (1966) and Altman’s (1968) studies consider five independent variables, and each of them represents the financial ratios.

H1: Earnings can predict corporate financial distress.

2.2 Cash flows-financial distress relationships

The information contained on a cash flow statement shows the decrease or increase in cash flow balance over an accounting period. Significance of a cash flow analysis towards a corporate financial distress prediction has been augmented by Ward and Foster in 1997. They compared the trends in three compo­nents of a cash flow statement, i.e., operating cash flow, investing cash flow, and financing cash flow (Tam & Kiang 1992).

The observations of Ward and Foster (1997) con­cluded that healthy companies have a tendency towards comparatively stable association among the three components of cash flow, i.e., operating, investing, and financing activities. In addition, it is noted that unhealthy companies were characterized by decreasing cash flow from operating, investing, and financing cash flow about one or two years before they failed for financial distress. This study is vital as it portrays the significance of assessing cash flow information while examining corporate finan­cial distress. Based on the above arguments, the second hypothesis is developed as follows:

H2: Cash flows can predict corporate financial distress.

3 METHOD

3.1 Populationsandsamples

The populations in this study related to all retail merchandizing enterprises listed in IDX. The sam­ples were classified into two groups, namely analysis samples and holdout samples. The analysis samples were retail merchandizing enterprises listed in IDX for the period of 2014-2016. And the holdout sam­ples were retail merchandizing enterprises listed in IDX for the period of 2017. The samples were selected by using purposive sampling method using the following criteria: (1) retail merchandizing enterprises that are listed in IDX for the period of 2014-2017, (2) having published audited financial statements for the fiscal period of 2014-2017, (3) companies are grouped into two groups for each model. Groups in the earning model are those with positive earnings and negative earnings. Groups in the cash flows model are positive cash flows and negative cash flows; and (4) the analysis samples consist of 45 firm-year from 15 different firms in earnings model and 45 firm-year from 15 different firms in cash flows model. The holdout samples con­sist of 15 firms.

3.2 Dependent variables

Dependent variables, corporate financial distress based on earnings model and cash flows model, are expressed as dummy variable (1) if the firm has posi­tive earnings, and (0) if the firm has negative earn­ings; (1) if the firm has positive cash flows, and (0) if the firm has negative cash flows. In this study, EBT is used as a proxy for earnings, where extraor­dinary items and discontinued operations are not included in order to avoid the effect of different tax tariffs at different periods. In other words, the reason for excluding extraordinary items and discontinued operations is to eliminate elements that may show increased profit growth in one period but will not appear in any other periods. However, the data of cash flows are derived from the cash flow figures presented in the audited financial statements of the company.

3.3 Independent variables

The list for independent variables which may influ­ence financial distress is as follows: Sales, Inventory turnover, Size, Employees, Current ratio, Acid ratio, Days in account receivables, Return on assets, Oper­ating profit margin, Operating expenses, Total assets turnover, Net fixed assets turnover, Net fixed assets, Total debt to total assets, Long-term debt to total assets, and Equity to total assets (Atmini & Wuryana 2005).

The independent variables in this study are the variables used to measure the company’s product market, institutional factors, operations, liquidity, income and expenses, profitability, activity and investment, and coverage (McCue 1991).

The institutional factor variable is the com­pany size data. The operating variable is the number of employees. The liquidity variables are the current ratio, acid ratio, and days in account receivable. The income and expenses variables are sales and operating expenses. The Profitabil­ity variables are operating profit margins and return on assets. The activity and investment variables are inventory turnover, total asset turn­over, net fixed assets turn over, and net fixed assets. The coverage variables are ratios of total debt to total assets, long-term debt to total assets, and equity to total assets.

3.4 Analysis method

In this study, data analysis is performed by using dis­criminant analysis. The following equation is used for judging corporate financial distress: equation (1) for the earnings model and equation (2) for the cash flows model.

The effect of discriminant analysis on sample ana­lysis is to determine whether the model can be used to predict corporate financial distress. If a model is statistically significant, the model is strong enough to predict corporate financial distress (Atmini & Wuryana 2005). The first step is done by carrying out the classical assumption test.

4 RESULTS AND DISCUSSION

4.1 Classical assumption test

The first test is testing the normality using Kolmogorov-Smirnov test. The result shows that both models are statistically significant at the level of 5 percent. The significance level of earnings model and cash flows model are 0.120 and 0.849, respectively. Thus, it is assumed that error of both models is normally distributed. Therefore, all the used data are normally distributed. The second test is heteroscedasticity testing using Glejser test. The result shows that the if significance values of the earnings model and cash value flows model are more than 5 percent, then there is no heteroscedasticity. The third one is testing Multicollinearity using the correlation coefficient among independent variables. The results found that the R values of any relation among independent variables in both earnings model and cash flows model are less than 0.8. This means that there is no multicollinearity or there is no linear relationship among independent variables. Finally, the fourth test is testing autocorrelation using Durbin Watson test. The output of the regression model for earnings model is 2.445, and for the cash flows model is 1.932. Durbin Watson test with significant level of 5 percent and samples total of 45 was dL =

0. 6915, and dU = 2.5856 was used. The models’ values are between dL and 4-dU. Therefore, there is no autocorrelation.

4.2 Earnings models

Table 1 presents the Wilks’ Lambda of the earnings model. Table 1 shows that the value of discriminant analysis for this model is statistically significant (sig = 0.000) at the level of 1 percent. Thus, earnings model has significant ability to predict corporate financial distress. Besides, Table 2 shows that the earnings model is able to predict corporate financial distress, in terms of whether company reports

Table 1. Earningsmodel.

Wilks’ Lambda

Table 2. Classification results.

a Cross-validation is done only for those cases in the ana­lysis. In cross-validation, each case is classified by the functions derived from all cases other than that case.

b 95.5% of original grouped cases correctly classified. c 88.6% of cross-validated grouped cases correctly classified.

Table 3. Holdout samples predicted earnings model.

negative earnings or positive earnings, with a total of 95.50 percent for the original grouped cases, and of 88.60 percent for the cross-validated grouped cases accurately.

Further, to examine the power of earnings model in predicting corporate financial distress is to per­form prediction on the holdout samples. The predic­tion of earnings model is as follows:

The model is used to determine the Z (EBT) scores of each firm in the holdout samples. Based on discriminant analysis, the centroids group A of firms with positive earnings is 3,788 and centroids group B of firms with negative earnings is 0.598. The opti­mal cutting score is calculated by basic formula as follows: ZCE = (ZA + ZB)/2, where ZCE = the crit­ical cutting score between groups A and B, ZA = centroid for group A, and ZB = centroid for group B. The cutting score (Z) = (3.788 - 0.598)/2 = 1.595. This means that if a company has a score lower than 1.595, these companies are predicted to report nega­tive earnings. Otherwise, companies would be pre­dicted to report positive earnings when their score is higher than 1.595.

To determine the predictive accuracy of the earn­ing model, we can see Table 3 which demonstrates that the earnings model is able to classify approxi­mately 80 percent of holdout samples or 12 of 15 companies. Therefore, it can be seen that earnings model is powerful enough to predict corporate finan­cial distress, since the number of holdout samples

Table 4. Cash flows model.

test indicates more than 50 percent classification accuracy.

4.3 Cash flows model

Based on the Wilks' Lambda (see Table 4), we found that discriminant analysis for cash flows model is statistically insignificant (sig = 0.197 ≥ 1%) at α level of 0.01. Since the cash flows model is not significant, it cannot be used as a predictor model for corporate financial distress. Thus, there is no further testing of the holdout samples is needed.

5 CONCLUSIONS

The following results were obtained. The earnings model is strong enough to use as a model in predict­ing corporate financial distress. This model is able to predict correctly corporate financial distress of 95.5 percent for original grouped case by grouping companies into negative earnings and positive earn­ings. Further, holdout samples indicate that earnings model is able to classify 80 percent accurately, above 50 percent. Therefore, it can be concluded that the earnings model is a strong predictor model.

The cash flows model cannot be used as a pre­dictive model of corporate financial distress, because of statistical insignificance. This model has

a significance level of 0.197 which is more than 1 percent. Thus, there is no further testing of the holdout samples required.

Investors, creditors, fund managers, and stock­holders can use earnings model to screen out undesirable investments, and reduce losses by with­drawing investments from unhealthy retail merchan­dizing enterprises listed in IDX. Government and the market authorities can use earnings model as guide­line to increase the transparency of regulatory objectives.

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