Capitalism and Freedom

Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review

How Much Does Performance Matter? A Meta-Analysis of CEO Pay Studies

We assess whether two popular accounting-based measures, Altman’s (1968) Z-Score and Ohlson’s (1980) O-Score, effectively summarize publicly-available information about the probability of bankruptcy. We compare the relative information content of these Scores to a market-based measure of the probability of bankruptcy that we develop based on the Black–Scholes–Merton option-pricing model, BSM-Prob. Our tests show that BSM-Prob provides significantly more information than either of the two accounting-based measures. This finding is robust to various modifications of Z-Score and O-Score, including updating the coefficients, making industry adjustments, and decomposing them into their lagged levels and changes. We recommend that researchers use BSM-Prob instead of Z-Score and O-Score in their studies and provide the SAS code to calculate BSM-Prob.

Assessing the Probability of Bankruptcy

This paper traces developments in the credit risk measurement literature over the last 20 years. The paper is essentially divided into two parts. In the first part the evolution of the literature on the credit-risk measurement of individual loans and portfolios of loans is traced by way of reference to articles appearing in relevant issues of the Journal of Banking and Finance and other publications. In the second part, a new approach built around a mortality risk framework to measuring the risk and returns on loans and bonds is presented. This model is shown to offer some promise in analyzing the risk-return structures of portfolios of credit-risk exposed debt instruments.

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Credit risk measurement: Developments over the last 20 years

Development of a class of stable predictive variables: the case of bankruptcy prediction

Financial distress precedes bankruptcy. Most financial distress models actually rely on bankruptcy data, which is easier to obtain. We obtained a dataset of financially distressed but not yet bankrupt companies supplying a major auto manufacturer. An early warning model successfully discriminated between these distressed companies and a second group of similar but healthy companies. Previous researchers argue the matched-sample design, on which some earlier models were built, causes bias. To test for bias, the dataset was partitioned into smaller samples that approach equal groupings. We statistically confirm the presence of a bias and describe its impact on estimated classification rates.

Predicting corporate financial distress: Reflections on choice-based sample bias

Probabilistic Neural Networks in Bankruptcy Prediction

The potential for industry-relative financial ratios to improve the prediction of firms in financial distress motivated this comparison of model specifications based on either unadjusted or industry-relative ratios. Both specifications yielded stable parameter estimates over the time periods examined. However, the industry-relative specification appeared to add incremental information not contained in the model based on the unadjusted financial ratios; the converse case did not hold. In addition, with the industry-relative specification, ex post forecast accuracy was slightly improved relative to the ex ante forecast, while with the unadjusted model specification, ex post forecast accuracy declined from that obtained ex ante.

Harlan D. Platt

Papers

Development of a class of stable predictive variables: the case of bankruptcy prediction

Predicting corporate financial distress: Reflections on choice-based sample bias

Probabilistic Neural Networks in Bankruptcy Prediction

A note on the use of industry-relative ratios in bankruptcy prediction

Corporate board attributes and bankruptcy