Using cash flow ratios to predict business failures.

 

Subject:

Management research (Analysis)
Cash flow (Analysis)
Ratio analysis (Usage)
Business failures (Analysis)

Authors:

Rujoub, Mohamed A.
Cook, Doris M.
Hay, Leon E.

Pub Date:

03/22/1995

Publication:

Name: Journal of Managerial Issues Publisher: Pittsburg State University - Department of Economics Audience: Academic; TradeFormat: Magazine/Journal Subject: Business; Human resources and labor relationsCopyright: COPYRIGHT 1995 Pittsburg State University - Department of Economics ISSN: 1045-3695

Issue:

Date: Spring, 1995 Source Volume: v7 Source Issue: n1

Accession Number:

16838779

Full Text:

Cash flow may be viewed as the lifeblood of a corporation and the essence of its very existence. Numerous empirical studies that use financial and accounting measures to predict business performance (i.e., success or failure) emphasize the importance of cash flow information in predicting bankrupt and nonbankrupt firms (Bernard and Stober, 1989; Gentry, 1984 and 1985; BarNiv, 1990, Carslaw and Mills, 1991). Most of those studies conclude that the level of cash inflows and outflows from various activities are highly interrelated. A failure of any part of the system to operate may endanger or cause the entire firm to fail (Largay and Stickney, 1980).
The primary objective of this study is to assess the usefulness of cash flow disclosures as required by Statement of Financial Accounting Standards No. 95 (SFAS 95) in the prediction of bankruptcy, and whether cash flow data provide a superior prediction of business failure over the models employing conventional accrual accounting data. The business failure prediction criterion was used for two reasons: (1) Business success or failure has been causally linked to the volume of net cash inflow and outflow components from various activities (Gentry, 1985). For example, the inability of a firm to generate enough cash from its operations may force the firm to borrow more money or to dispose of its capital investments to meet its obligations. If this situation persists over an extended period of time, it may lead to an involuntary bankruptcy. (2) This criterion, which is empirically testable, has been successfully used for investigating the usefulness of accounting information in other studies (Altman and Spivack, 1983).
A second objective of this study is to present some new financial ratios derived from cash flow data and to highlight their potential use in financial analysis and prediction of business performance. Some of these are new ratios which have not been used in other studies.
MOTIVATION
The motivation for this study came from two important developments in the business world: (1) the multitude of business failures across all types of business, and (2) the emphasis placed on cash flow information by the Financial Accounting Standards Board in SFAS 95. Could the use of cash flow data help predict business failure and thus help prevent business failure?
The link between cash flow data and corporation net worth has been established in earlier research (Rayburn, 1986). However, these studies were done before the issuance of SFAS 95 and used different measures of cash flow from operations. Numerous studies show that financial ratios based on accrual accounting data possess significant ability to predict bankruptcy (Altman and Spivack, 1983; Beaver, 1966, 1968; Libby, 1975; Ohlson, 1980). Most of these studies concluded that companies with weak and unstable financial indicators (ratios) are more likely to fail than those companies with stronger and more stable financial indicators (ratios). However, these models did not emphasize cash flow data.
An ideal approach is probably an integrated one, such as the approach suggested in this study. This paper provides evidence on the usefulness of cash flow data in the prediction of business failure and whether the integration of cash flow data with accrual accounting data can provide a superior measure over accrual accounting data alone for predicting bankruptcy. It should be noted that this study does not suggest overlooking these earlier predictive methods, but rather it addresses whether cash flow information can complement the information already provided by accrual accounting data.
There are at least four key differences between the prior studies and this study. First, financial ratios based on conventional accrual accounting are modified to include cash flow information. Second, while prior studies use different approaches to measure cash flow from operations, this study bases its measures of cash from operations on those criteria required by SFAS 95. Third, this study uses the format in SFAS 95 requiring cash flow data to be divided into cash from operations, cash from investing and cash from financing activities. None of the preceding studies has used this approach. Fourth, none of the previous studies used the cash flow ratios which have been emphasized in this study.
RESEARCH HYPOTHESES
For testing the major objective of the research, three hypotheses, stated in the alternative form, have been examined. These include:

Ha1: The discriminant ability of cash flow data, in the form of financial ratio models, to predict bankruptcy is significant.
Ha2: The classification accuracy of cash flow information, in the form of financial ratio models, to predict bankruptcy is greater than the classification accuracy of accrual accounting information, in the form of financial ratio models.
Ha3: The use of cash flow data in conjunction with accrual accounting data, in the form of financial ratio models, can improve the overall classification accuracy of accrual accounting models to predict bankruptcy.
RESEARCH DESIGN
The discussion of the research design starts with sample and variables selection, and then addresses each of the three hypotheses.
SAMPLE SELECTION
The Wall Street Journal Index (WSJI), the Standard and Poor's Compustat (SPC) Industrial Annual Research File of Companies, and the Compustat Industrial Files (CIF) were employed to choose a sample consisting of 33 failed firms and 33 nonfailed firms for a five year period following the issuance of SFAS 95. The sample was limited to those companies who provided a statement of cash flows or sufficient data for the statement for at least three years prior to failure. Failure of firm was defined as the act of filing a petition for Chapter 11 bankruptcy (Zmijewski, 1984). Failed and nonfailed firms were identified and matched on the basis of their industry and asset size. The sample cut across various types and sizes of firms. Because there are many more nonfailed firms than failed firms equal sized groups were used. Similar sampling techniques have been used in other studies (Zmijewski, 1984; BarNiv, 1990).
VARIABLES SELECTION
The approach used in this study involves the use of financial ratios in the prediction models. Ratios are used for two key reasons: (a) financial ratios have been successfully used in other empirical studies (Largay and Stickney, 1980; Ohlson, 1980; Giacomino and Mielke, 1995) and (b) the use of financial ratios can make comparisons of corporations of different sizes more meaningful than the use of absolute figures. The financial ratios used in the tests were divided into two groups: (a) those ratios derived from cash flow data, and (b) those ratios based on conventional accrual accounting.
RATIOS DERIVED FROM CASH FLOW DATA
The ratios derived from cash flow data are classified under the following groups as suggested by Mielke and Giacomino (1988): (1) management financial decisions, (2) quality of earnings, (3) mandatory cash flows, and (4) discretionary cash flows. These ratios used data as classified in the Statement of Cash Flows required by SFAS 95 (FASB, 1987). This classification is: (1) cash inflows and outflows for operating activities (primarily income statement ordinary activities), (2) cash inflows and outflows from investing activities (sale or purchase of plant assets, long-term investments, etc.) and (3) cash inflows and outflows from financing activities (increase or decrease in financing from owners or long-term creditors).
As can be seen from Table 1, eighteen financial ratios based on cash flow data were used in the research analysis. Some of these are new ratios developed for this study, as indicated in Table 1. Then, stepwise discriminant procedures were used for selecting the financial ratios that are most useful in discriminating between bankrupt and nonbankrupt firms. Eight financial ratios derived from the cash flow data, which were found to be significant, were selected and used in the final models.
The ratios selected for this study are:
(1) External financing index ratio = Cash from operations/Total external Financing sources (debt)
This ratio shows a firm's ability to provide sufficient cash from its operations to meet its external obligations when they mature. Generally speaking, the higher the ratio, the stronger the firm's liquidity, the greater the firm's ability to meet its obligations as they become due, and the greater the probability of success of the firm. This ratio is significant because it is important to view the liquidity of a firm from an external conservative point of view.
(2) Cash sources component percentages ratio = Cash from financing/Total sources of cash
This ratio relates the cash from financing activities to total cash sources during the period. In this computation, the cash generated from financing activities is compared with the total cash generated from all activities. This ratio also indicates how much the firm relies on debt and investment by owners rather than cash generated internally from operating activities or from investing activities. In general, the lower the ratio, the better the firm's financial position and the greater the probability of success of the firm. This ratio may be compared with industry average and competitive firms or be analyzed by trend analysis over time. For example, it may be used in assessing and comparing the use of outside financing versus internal financing over time.
(3) Financing policies ratio = Cash from financing activities/Total Assets
This ratio shows the percentage of assets that were funded by creditors and owners during the period. This ratio also helps accounting information users to evaluate a firm's financing policies. In general, the lower this ratio, the better the firm's financial position. A high ratio may indicate that the firm is not using its resources (assets) effectively or to best advantage. A high ratio also may indicate that the firm faces a problem due to additional cash burden in the future as the interests and loans repayments become due.
(4) Operating cash index ratio = Cash from operations/Net income
This ratio assists current or potential investors and creditors in evaluating the "quality" of a firm's earnings. It compares accrual net income and the related cash from operations. Earnings are judged to be of high quality if they are stable, the major source appears to be the operating activities, and the methods used in determining earnings are conservative. Determining net income under accrual accounting requires the use of judgmental decisions in measuring depreciation, estimating bad debts, etc. Cash flow from operations is considered to be a more objective measure. Generally, the higher this ratio, the better the quality of earnings. This ratio also indicates a firm's ability to produce cash internally from its ongoing operations. Further analysis may be made by comparison with industry data and by trend analysis over time.
(5) Operating cash inflow ratio = Cash from operations/Total sources of cash
This ratio indicates what proportion of cash inflows is generated internally from operating activities. In general, the larger the ratio, the greater will be the firm's ability to withstand adverse changes in economic conditions. A high ratio generally indicates a strong financial position for the company. In this case, the firm will probably be less dependent on external sources of funds.
(6) Operating cash outflow ratio = Cash used in operations/Total sources of cash
This ratio indicates what proportion of total cash generated from all sources is used in operations. This ratio also helps users of accounting information in evaluating a firm's ability to control and contain costs. In general, the lower the ratio, the higher the profitability and the greater the probability of success of the firm.
(7) Long-term debt payment ratio = Cash applied to long-term debt/Cash supplied by long-term debt
This ratio compares a firm's cash disbursements to pay long-term liabilities with cash receipts from long-term liabilities. Generally, the higher the ratio, the stronger the firm's ability to settle its long-term liabilities as they become due. This ratio may be used by current or future long-term creditors who must evaluate the probability of obtaining repayment in the future for any funds loaned to the company.
(8) Productivity of assets ratio = Cash from operations/Total assets
This ratio shows the percentage of cash generated from operating activities on each dollar of asset invested and measures the productivity of assets. It also helps accounting information users in assessing a firm's financial flexibility and management's ability to generate cash and control costs. Financial flexibility may be viewed in terms of a firm's ability to produce enough cash internally to respond to unforeseen problems and utilize profitable opportunities. An evaluation of a firm's ability to survive an unexpected drop in revenues, for example, may include a review of its past cash flows from operations. In general, the higher the ratio, the greater the efficiency of the use of assets and the better the firm's financial position.
FINANCIAL RATIOS BASED ON CONVENTIONAL ACCRUAL ACCOUNTING
As can be seen from Table 2, thirty financial ratios based on conventional accrual accounting have been used in earlier studies for predicting business failure (Beaver, 1966, 1968; Altman and Spivack, 1983). These were divided into six "common elements" groups. Only one ratio from each group was found to be a significant predictor of bankruptcy in the previous studies.
These six ratios were thus selected for use in the final bankruptcy models in this study. These ratios are:
(1) Net income to total assets
(2) Cash flow to total debt
(3) Current assets to current liabilities
(4) No credit interval (defensive assets minus current liabilities to fund expenditures for operations)
(5) Working capital to total assets
(6) Current liabilities plus long-term liabilities to total assets
TEST OF HYPOTHESIS ONE
Multivariate Discriminant Analysis (MDA) was used to test each of the three hypotheses. MDA is a statistical technique that can be used to classify observations into one of two or more categories - bankrupt or nonbankrupt (Hair et al., 1979). Bankruptcy models were constructed for all three tests for one, two, and three years prior to the bankruptcy, using equal prior probabilities and an equal cost of misclassification. The MDA bankruptcy model to test hypothesis one, using the eight selected cash flow ratios, is expressed mathematically as follows:
Zi = B1X1i + B2X2i + B3X3i + B4X4i + BSX5i + B6X6i + B7X7i + B8X8i
Where:
Zi= discriminant function (i.e., bankrupt and nonbankrupt firms)
B1, B2 ........., B8= the weighing coefficient
X1i = external financing index ratio X2i = cash sources component percentage ratio X3i = financing policies ratio X4i = operating cash index ratio X5i = operating cash inflow ratio X6i = operating cash outflow ratio X7i = long-term debt payment ratio X8i = productivity of assets ratio
This model can be used for classifying a firm as either bankrupt or nonbankrupt. The MDA model is appropriate only under the following conditions: (1) the groups being classified are categorical (i.e., zero and one, or failed and nonfailed firms), (2) each observation in each group can be measured by a set of (X) continuous independent variables, and (3) these (X) variables are assumed to have a multivariate normal distribution in each population and equal covariances. In order to test whether the accounting data used in this research meet the assumptions of normality and equal covariance matrices, the Kolomogrov (D) statistical technique and the likelihood ratio test were performed. When nonnormality of the data was observed, transformation was performed in an attempt to eliminate or reduce the extent of the violation.
The classification accuracy of the Multivariate Discriminant Analysis (MDA) may suffer from potential upward search bias which may be due to the fact that the discriminant coefficient, the reduced ratio sets (models), and the group distribution are derived from the original (same) sample. In this study, the Frank and Morrison (1965) approach and the Jackknife (U-Method) technique were used to determine whether such bias occurs in the classification accuracy derived. It was found that the model can be used to discriminate between bankrupt and nonbankrupt firms, using samples other than that sample used to derive the discriminant coefficient of the model. This result suggests that there is significant statistical evidence to show that search bias is not significant. Therefore, the results of the tests explained below are not affected by search bias.
TEST OF HYPOTHESIS TWO
For testing hypothesis two, bankruptcy models were constructed by using the six selected financial ratios based on conventional accrual accounting. The MDA bankruptcy model is written as follows:
Zi = M1Y1i + M2Y2i + M3Y3i + M4Y4i + M5Y5i + M6Y6i
Where:
Zi= discriminant function (i.e., bankrupt and nonbankrupt firms)
M1, M2, ...., M6= the weighing coefficient
Y1i = Net income/Total assets Y2i = Cash flow/Total debt Y3i = Current assets/Current liabilities Y4i = No credit interval Y5i = Working capital/Total assets Y6i = Total liabilities/Total assets
The outcome of this accrual accounting model and that based on cash flow data as described above were then compared by using the McNemar tests of changes. The McNemar test is a nonparametric statistical technique that may be used for analyzing frequency data from related samples (Daniel, 1990). This test can be used to examine whether there exists a differential rate of classification accuracy between cash flow data and accrual accounting data. Accordingly, the set of classification accuracy for each firm in the sample was classified into one of four categories: (1) firms which were classified correctly by the cash flows and accrual accounting data, (2) firms which were misclassified by the cash flow information and by accrual accounting data, (3) firms which were classified correctly by the cash flow data and misclassified by the accrual accounting data, and (4) firms which were classified correctly by the accrual accounting data and misclassified by the cash flow data.
TEST OF HYPOTHESIS THREE
For testing hypothesis three, bankruptcy models were constructed by using the eight selected ratios based on the cash flows data in conjunction with the six selected ratios based on conventional accrual accounting data. The MDA bankruptcy model is written as follows:
Zi = B1V1i + B2V2i + B3V3i + B4V4i + B5V5i + B6V6i + B7V7i + B8V8i + M1Y1i + M2Y2i + M3Y3i + M4Y4i + M5Y5i + M6Y6i
Where:
Zi = discriminant function (i.e., bankrupt and nonbankrupt firms)
B1, B2 ........., B8= the weighing coefficient cash flow data
M1, M2 ........., M6= the weighing coefficient of accrual accounting data.
V1i, V2i ........, V8i= cash flow variables
Y1i, Y2i ........., Y6i= accrual accounting variables
The results of this combined bankruptcy model and that based on accrual accounting data alone were compared using the McNemar test of changes. This test was used for comparison of the classification accuracy of both models. Accordingly, the set of classification accuracy for each firm in the sample was classified into one of four categories: (1) firms which were classified correctly by both models, (2) firms which were misclassified by both models, (3) firms which were classified correctly by the combined model and misclassified by accrual accounting data, and (4) firms which were classified correctly by accrual accounting data and misclassified by the combined model.
RESULTS OF TESTING HYPOTHESIS
The remainder of this paper is devoted to presenting the results of tests of the research hypotheses.
RESULTS OF TESTS OF HYPOTHESIS ONE
The first hypothesis tested was to determine whether cash flow data can be used to discriminate between bankrupt and nonbankrupt firms. The results of testing hypothesis one are presented in Table 3. The null version of this hypothesis was rejected at the 0.0003 and 0.0360 level of significance for data one year and two years prior to bankruptcy, respectively. Cash flow data classify 86.36% and 78.79% of the total sample correctly for one year and two years prior to failure, respectively. These results suggest that cash flow data can generate information needed to derive statistically significant bankruptcy prediction models. This led to the conclusion that cash flow models provide information useful to users of accounting information for predicting business failure. It appears that by understanding what cash flow data reveal about a firm's ability to meet its obligations users of accounting information may be able to use such data as indicators of potential financial trouble for a firm.
The data for three years prior to bankruptcy produced lower classification performance. This result suggests that cash flow data are useful for predicting bankruptcy up to two years prior to bankruptcy but not for three years prior to bankruptcy. It appears that bankruptcy indicators become less clear three years prior to bankruptcy.
RESULTS OF TESTS OF HYPOTHESIS TWO
The classification accuracy of accrual accounting data for one year, two years, and three years prior to bankruptcy is presented in Table 4. For testing the second research hypothesis, the McNemar test for related samples was performed to investigate whether there exists a differential rate of classification between the cash flow and accrual accounting model. The results of this test indicate that the cash flow data appear to provide predictive power superior to that of the accrual accounting model. The null version of this hypothesis was rejected at the 0.053 level of significance. Cash flow data provide a basis for discriminating correctly between bankrupt and nonbankrupt firms in 86.36% and 78.79% of the cases for one year and two years [TABULAR DATA FOR TABLE 3 OMITTED] prior to bankruptcy, respectively. Accrual accounting data provide a basis for discriminating correctly between bankrupt and nonbankrupt firms in 81.82% and 71.21% of the cases for one year and two years prior to bankruptcy, respectively.
RESULTS OF TESTS OF HYPOTHESIS THREE
The third hypothesis tested was to determine whether the combined data can improve the overall ability to predict bankruptcy. The eight cash flow ratios and the six accrual financial ratios were used in the final model for testing this hypothesis.
The classification accuracy for data one year, two years, and three years prior to bankruptcy is presented in Table 5. As can be seen from Table 5, the predictive accuracy of cash flow data plus accrual accounting data for one year prior to bankruptcy was significant at the 0.0002 level of significance. This result suggests that this model was found to be useful for predicting business failure. This model classifies 90.91% of the total sample correctly (i.e., 81.82% (27/33) of the failed firms and 100.00% (33/33) of the nonfailed firms were classified correctly).
The classification accuracy of the combined cash flow data and accrual accounting model for two years prior to bankruptcy was significant at the 0.0131 level of significance. For the test using data three years prior to bankruptcy, Table 5 shows that the predictive accuracy of the combined model was not significant at the .05 level of significance (P-value = 0.3213). These results suggest that the combined cash flow and accrual accounting data model can be used [TABULAR DATA FOR TABLE 4 OMITTED] [TABULAR DATA FOR TABLE 5 OMITTED] [TABULAR DATA FOR TABLE 6 OMITTED] to predict bankruptcy up to two years prior to bankruptcy.
The McNemar test of related samples was performed to examine whether there exists a differential rate of classification between cash flow data plus accrual accounting data model and accrual accounting data model alone. The results of this test suggest that when cash flow data are combined with accrual accounting data, the results outperform accrual accounting data alone in discriminating between bankrupt and nonbankrupt firms. The null version of this hypothesis was rejected at the 0.017 level of significance. These results suggest that cash flow data combined with accrual accounting data produces superior discriminant power over accrual accounting data alone (90.91% versus 81.82% and 86.36% versus 71.21% for one year and two years prior to bankruptcy, respectively). Thus, cash flow data can improve the overall accuracy of predicting business failure when used in combination with accrual accounting data.
CONCLUSIONS AND IMPLICATIONS FOR MANAGERIAL USE
This study has attempted to examine whether cash flow data can provide a superior measure to predict bankruptcy over accrual accounting data. Several financial ratios, based on cash flow data, were used as independent variables for the investigation. Some of these are new and have never been used before in other studies. From these, eight financial ratios derived from cash flow data were selected as best predictors and used in the final models, along with six ratios based on accrual accounting used in previous studies.
In testing the research hypotheses, it was found that (a) cash flow data predict bankruptcy better than accrual accounting data, and (b) the use of cash flow data in conjunction with accrual accounting data improves the overall predictive power of accrual accounting data used in previous studies for predicting business failure. This comparison is shown in Table 6. These findings, as in other studies, may be subject to some limitations because a limited number of firms were used and selected in a nonrandom sample. Additional research in this area might be useful to add credence to the results.
In summary, these results lead to the conclusion that cash flow data, in the form of financial ratios, are useful by themselves or as a supplement to accrual accounting data in predicting bankruptcy.
The implications of the study from the standpoint of management or other users are many. For example, the volume of net cash inflows from operations may indicate whether an enterprise can generate funds internally and meet its current and future obligations. Therefore, measuring the change in the volume of the cash flow components is considered to be critical in determining the future success or failure of a corporation. The inability, of a corporation to generate cash from its operations over time may cause a default on its debt and bankruptcy. This situation indicates a basis for discriminating between financially successful and financially troubled enterprises.
The models used in this study may help users of accounting information to detect the deterioration of a firm's financial position. Management may use this information to forecast business failure and take the necessary action to avert a potential failure. Management may also use cash flow information as a planning tool. Some of the significant ways in which management may use this information for planning and managing purposes encompass: (1) to establish goals and allocate internal resources effectively by integrating this information into the budgeting process, (2) to coordinate cash dividends policy with other actions of the company, (3) to evaluate investment opportunities such as financing of new product lines, additional machinery, or acquisition of other competitors, (4) to evaluate the efficiency and effectiveness of managers and units, and (5) to find ways of strengthening a weak cash position or credit lines.
External users such as investors, creditors, auditors and others may use cash flow information to make more effective decisions. For example, investors may use this information to evaluate the quality of management and whether it is pursuing corporate goals stated by stockholders. Investors may also use this information to update their prior beliefs regarding their current and future investments. Creditors such as bank loan officers may use this information to aid in improving critical lending decisions and monitoring loans. Improvements could be exhibited in many areas, such as a reduction in loans made to potential defaulters, and an increase in loans made to clients that repay their debt in a timely manner. Cash flow data may provide valuable information to auditors. It might help auditors in determining the analytical review (audit) procedures and in making critical and necessary decisions as to whether the firm is solvent and will stay in existence for awhile as a going concern.
Finally, the eight ratios emphasized in this study, based on cash flow data, may be useful as individual ratios. Management or investors may use these ratios, perhaps with other analytical procedures, to detect problems in various areas of the firm and take corrective action. For example, the External Financing Index Ratio (1) shows the firm's ability to provide sufficient cash from its operations to meet its external obligations when they mature. Generally speaking, the higher the ratio the stronger the firm's liquidity, and the greater the probability of success of the firm. The Cash Sources Component Percentages Ratio (2) relates the cash from financing activities to total cash sources during the period. In general, the lower the ratio the better the firm's financial position and the greater the probability of success. The Financing Policies Ratio (3) helps accounting information users to evaluate a firm's financing policies. In general, the lower this ratio, the better the firm's financial position. Operating Cash Index Ratio (4) assists current or potential investors and creditors to evaluate the "quality" of the firm's earnings. Generally, the higher this ratio, the better the quality of earnings.
Operating Cash Inflow Ratio (5) indicates what proportion of cash inflows is generated internally from operating activities. A high ratio generally indicates a strong financial position for the company. Operating Cash Outflow Ratio (6) indicates what proportion of total cash generated from all sources is used in operations. In general, the lower the ratio the higher the profitability. Long-Term Debt Payment Ratio (7) compares a firm's cash disbursements to pay long-term liabilities with cash receipts from long-term liabilities. Generally, the higher the ratio, the stronger the firm's ability to set fie its long-term debt. Productivity of Assets Ratio (8) measures the productivity of assets. In general, the higher the ratio the greater the efficiency of use of assets.
References
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RELATED ARTICLE: Table 1
Ratios Derived from Cash Flow Data
Group I - Management Financial Decisions
(1) Productivity ratio = Cash from operations/Capital investment
(2) External financing index = Cash from operations/Total external financing sources (debt)
(3) Cash sources component percentages ratio = Cash from financing/Total sources of cash
(4) Financing policies ratio = Cash from financing activities/Total assets
Group II - Quality of Earnings
(5) Cash flow adequacy ratio = Cash from operations/Capital investments + inventory additions + dividends + debt uses
(6) Operating cash index ratio = Cash from operations/Net income
(7) Operating cash inflow ratio = Cash from operations/Total sources of cash
(8) Reinvestment (investment) ratio = Capital investments/Depreciation + Sale of assets
(9) Capital investments per dollar of cash ratio = Capital investments/Total sources of cash
(10) Productivity of assets ratio = Cash from operations/Total assets
Group III - Mandatory Cash Flows
(11) Operating cash outflow ratio = Cash used in operations/Total sources of cash
(12) Long-term debt payment ratio = Cash applied to long-term debt/Cash supplied by long-term debt
(13) Percentage of cash sources required for long-term debt = Cash applied to long-term debt/Total sources of cash
(14) Short/long-term ratios = Current debt sources/Total debt sources and Long-term debt sources/Total debt sources
Group IV - Discretionary Cash Flows
(15) Discretionary cash index ratio = Cash used for investing + Dividends/Total sources (inflows) of cash
(16) Dividend payout of cash from operations = Dividends/Cash from operations
(17) Investing policies ratio = Cash from investing activities/Total assets
(18) Cash changes to assets ratio = Total changes in cash/Total assets
Ratios 2, 3, 4, 6, 7, 10, 11, 12 were selected and used in the final bankruptcy models in this study.
Source: Ratios 4, 7, 10, 11, 17, 18 developed by authors; others from Mielke and Giacomino, 1988.
RELATED ARTICLE: Table 2
Financial Ratios Based On Conventional Accrual Accounting
Group I - Cash Flow Ratios
(1) Cash flow to sales (2) Cash flow to total assets (3) Cash flow to total debt (used as a predictor in this study) (4) Cash flow to net worth
Group II - Net Income Ratios
(5) Net income to sales (6) Net income to total assets (used as a predictor in this study) (7) Net income to total worth (8) Net income to total debt
Group III - Debt to Total Assets Ratios
(9) Current liabilities to total assets (10) Long-term liabilities to total assets (11) Current liabilities plus long-term liabilities to total assets (used as a predictor in this study) (12) Current liabilities plus long-term preferred stock to total assets
Group IV - Liquid Assets to Total Assets Ratios
(13) Cash to total assets (14) Cash and receivables to total assets (15) Current assets to total assets (16) Working capital to total assets (used as a predictor in this study)
Group V - Liquid Assets to Current Debt Ratios
(17) Cash to current liabilities (18) Cash and receivables to current liabilities (19) Current assets to current liabilities (used as a predictor in this study)
Group VI - Turnover Ratios
(20) Cash to sales (21) Accounts receivable to sales (22) Inventory to sales (23) Quick assets to sales (24) Current assets to sales (25) Working capital to sales (26) Net worth to sales (27) Total assets to sales (28) Cash interval (cash to fund expenditures for operations) (29) Defensive interval (defensive assets to fund expenditures for operations) (30) No credit interval (defensive assets minus current liabilities to fund expenditures for operations) (used as a predictor in this study).
Where:
Cash flow = Net income + Depreciation + Depletion + Amortization Net worth = Common stockholders' equity + Deferred income taxes Cash = Cash + Marketable securities Quick assets = Cash + Accounts receivable Working capital = Current assets - Current Liabilities Fund expenditures for operations = Operating expenses - Depreciation - Depletion - Amortization Defensive assets = Quick assets
Source: Beaver, 1966, 1968; Altman and Spivack, 1983.

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