1. ACCOUNTING DATA, BANKRUPTCY, AND RISK

2. Introduction  Earnings is not the only accounting number available to investors in the capital market  CAPM  an asset’s market value is a function of its expected future cash flows, the risk of its future cash flows (  ), the market price of a risk, and the risk-free rate of return  The risk-free rate and the market price of a risk are determined in the capital market  A firm’s accounting data are likely to be more useful in estimating the firm’s securities expected cash flows and risk

3. Accounting Data and Bankruptcy  The nature of bankruptcy  Corporate bankruptcies are “proceedings which are undertaken under bankruptcy laws when a corporation is unable to pay or reach agreement with its creditors outside of court”  Ceteris paribus, the firm’s expected future cash flows and market value fall as the probability of bankruptcy increases  Estimates of the value of stocks, bonds, and other claims on the firm depend on the probability that the firm will go bankrupt

4. Accounting Data and Bankruptcy (Cont’d)  The role of accounting data in predicting bankruptcy  Most bankruptcy prediction models use accounting data.  Often expressed in ratio form  Reason: bond indentures and lending agreements often use accounting ratios to restrict managers’ actions  Breach of the accounting ratio covenant places the firm in default and can lead to bankruptcy

5. Accounting Data and Bankruptcy (Cont’d)  Multivariate approaches to predicting bankruptcy  Discriminant analysis  Involves many variables  Only the set of variables that “best” distinguishes between the failed and nonfailed groups is used to estimate z scores.  Altman (1968):  22 variables are considered as candidates for the discriminant function  The combination finally chosen consists of 5 variables (working capital/total assets, retained earnings/total assets, earning before interest and taxes/total assets, market value of equity/book value of total debt, sales/total assets)

6. Accounting Data and Bankruptcy (Cont’d)  Multivariate approaches to predicting bankruptcy (Cont’d)  Discriminant analysis (Cont’d)  Use holdout sample: to evaluate the discriminant function’s predictive ability

7. Accounting Data and Bankruptcy (Cont’d)  Multivariate approaches to predicting bankruptcy (Cont’d)  Altman’s finding and those of other studies suggest that accounting data are useful in predicting bankruptcy.  However, they do not provide evidence that accounting-based models outpredict the market  Westerfield (1970) and Aharony, Jones, and Swary (1980) find that he bankrupt firm’s stock begin earning negative abnormal returns five years before bankruptcy

8. Accounting Data and Bankruptcy (Cont’d)  Multivariate approaches to predicting bankruptcy (Cont’d)  Extensions  Altman, Haldeman & Narayanan (1977)  Estimate the relative costs of the two types of errors.  Ohlson (1980)  Earlier studies assume (often incorrectly) that the financial statements for the bankruptcy year are disclosed prior to the bankruptcy filling  Requires the sample proportions of bankrupt and nonbankrupt firms be the same as the population proportions.

9. Accounting Data and Bankruptcy (Cont’d)  Multivariate approaches to predicting bankruptcy (Cont’d)  A problem common to all studies is ad hoc selection of independent variables

10. Accounting Data and Stock Risk   - the measure of risk   is estimated from the market model  Assume that  is stationarity over time, but it could vary from period to period.  Trade-off: if  is stationary, the longer the estimation period the better the  estimate. But, the longer the estimation period, the more likely  changes.  Bogue (1972) and Gonedes (1973): 60 months is the optimal period for their sample

11. Accounting Data and Stock Risk (Cont’d)  It is possible that additional information can be used to obtain a “better” estimate of  than that obtain form market model  Accounting data can be used to provide estimates of  for unlisted securities  It is possible that better  estimates can be obtained for listed securities by using accounting data in combination with the market model estimate of 

12. Accounting Data and Stock Risk (Cont’d)  Accounting data and  estimates  Accounting earnings are surrogate for cash flows  Financial leverage  Operating leverage

13. Accounting Data and Stock Risk (Cont’d)  Accounting data and  estimates (Cont’d)  Ball & Brown (1968)  “Accounting betas” are associated with market model  estimates  Beaver, Kettler & Scholes (1970)  Variables: dividend payout, asset growth, leverage, liquidity, asset size, earnings variability, accounting beta  Significant correlation: payout, leverage, earnings variability, accounting beta

14. Accounting Data and Stock Risk (Cont’d)  Accounting data and  estimates (Cont’d)  Beaver, Kettler & Scholes (1970), Bildersee (1975), Rosenberg & Maranthe (1975), Eskew (1979): models based on accounting variables forecast future levels of market risk more accurately than do models relying solely on prior market model estimates of   Elgers (1980): accounting variables do not produce more accurate estimates of 

15. Accounting Data and Bond Risk  Bond ratings and risk  If bond ratings are measuring risk, ratings should be cross-sectionally correlated with   Percival (1973) and Rozeff (1976): corporate bond  ’s are systematically negatively related to ratings  Urwitz (1975) finds that the correlation for ratings and  is significantly higher than is the correlation for ratings and variance

16. Accounting Data and Bond Risk (Cont’d)  Evidence that rating agencies use accounting data  Wakeman (1981):  Explanations  Accounting-based reasons accounted for more than two-thirds of the changes not involving new financing  S&P’s assesses 5 areas in determining a bond’s rating: indenture provision, asset protection, financial resources (liquidity), future earning power, and management  Timing of ratings changes  Ratings changes are most common in May and June, both of which shortly follow the availability of most annual reports  Empirical studies  Kaplan & Urwitz (1979) find that total asset, long-term debt to total assets, and stock’s beta are statistically associated with ratings.

17. Accounting Data and Bond Risk (Cont’d)  Bonds ratings and market efficiency  Weinstein (1977) and Wakeman (1981): find no bond price effect at the time of rating change.