I argue that hazard models are more appropriate than single-period models for forecasting bankruptcy. Single-period models are inconsistent, while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model. I find that about half of the accounting ratios that have been used in previous models are not statistically significant. Moreover, market size, past stock returns, and idiosyncratic returns variability are all strongly related to bankruptcy. I propose a model that uses both accounting ratios and market-driven variables to produce out-of-sample forecasts that are more accurate than those of alternative models. Copyright 2001 by University of Chicago Press.

https://www.jstor.org/stable/10.1086/209665

Forecasting Bankruptcy More Accurately: A Simple Hazard Model

I argue that hazard models are more appropriate for forecasting bankruptcy than the single-period models used previously. Single-period bankruptcy models give biased and inconsistent probability estimates while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model with a logit model estimation program. Applying my technique, I find that about half of the accounting ratios that have been used in previous models are not statistically significant bankruptcy predictors. Moreover, several market-driven variables are strongly related to bankruptcy probability, including market size, past stock returns, and the idiosyncratic standard deviation of stock returns. I propose a model that uses a combination of accounting ratios and market-driven variables to produce more accurate out-of-sample forecasts than alternative models.

Predicting takeover targets: A methodological and empirical analysis

This paper explores the determinants of corporate failure and the pricing of financially distressed stocks using US data over the period 1963 to 2003. Firms with higher leverage, lower profitability, lower market capitalization, lower past stock returns, more volatile past stock returns, lower cash holdings, higher market-book ratios, and lower prices per share are more likely to file for bankruptcy, be delisted, or receive a D rating. When predicting failure at longer horizons, the most persistent firm characteristics, market capitalization, the market-book ratio, and equity volatility become relatively more significant. Our model captures much of the time variation in the aggregate failure rate. Since 1981, financially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and small-cap risk factors than stocks with a low risk of failure. These patterns hold in all size quintiles but are particularly strong in smaller stocks. They are inconsistent with the conjecture that the value and size effects are compensation for the risk of financial distress.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w12362.pdf?abstractid=917567&mirid=1

In Search of Distress Risk

This paper explores the determinants of corporate failure and the pricing of financially distressed stocks whose failure probability, estimated from a dynamic logit model using accounting and market variables, is high. Since 1981, financially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and small-cap risk factors than stocks with low failure risk. These patterns are more pronounced for stocks with possible informational or arbitrage-related frictions. They are inconsistent with the conjecture that the value and size effects are compensation for the risk of financial distress. THE CONCEPT OF FINANCIAL DISTRESS has been invoked in the asset pricing literature to explain otherwise anomalous patterns in the cross-section of stock returns (Chan and Chen (1991) and Fama and French (1996)). The idea is that certain companies have an elevated probability that they will fail to meet their financial obligations; the stocks of these financially distressed companies tend to move together, so their risk cannot be diversified away; and investors charge a premium for bearing such risk. 1 The premium for distress risk may not be captured by the standard Capital Asset Pricing Model (CAPM) if corporate failures

/pdf/in-search-of-distress-risk-1nbpnxwcsk.pdf

Researchers typically estimate financial distress prediction models on nonrandom samples. Estimating models on such samples can result in biased parameter and probability estimates if appropriate estimation techniques are not used. This paper examines conceptually and empirically two estimation biases which can result when financial distress models are estimated on nonrandom samples. The first bias results from "oversampling" distressed firms and falls within the topic of choice-based sample biases. The second results from using a "complete data" sample selection criterion and falls within the topic of sample selection biases. The two issues examined in this paper arise because of sample selection/data collection constraints typically faced by financial distress researchers. The first constraint is the extremely low frequency rate of firms exhibiting financial distress characteristics (e.g., petitioning for

https://www.jstor.org/stable/pdfplus/2490859.pdf

Mark E. Zmijewski

Papers

Methodological issues related to the estimation of financial distress prediction models