Jan Szilagyi

Bio: Jan Szilagyi is an academic researcher from Duquesne University. The author has contributed to research in topics: Stock (geology) & Market capitalization. The author has an hindex of 5, co-authored 5 publications receiving 3114 citations.

In Search of Distress Risk

Abstract: 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.

In Search of Distress Risk

Abstract: 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

In Search of Distress Risk

Abstract: 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.

Predicting Financial Distress and the Performance of Distressed Stocks

Abstract: In this paper we consider the measurement and pricing of distress risk. We present a model of corporate failure in which accounting and market-based measures forecast the likelihood of future …nancial distress. Our best model is more accurate than leading alternative measures of corporate failure risk. We then use our measure of …nancial distress to examine the performance of distressed stocks from 1981 to 2008. We …nd that distressed stocks have highly variable returns and high market betas and that they tend to underperform safe stocks by more at times of high market volatility and risk aversion. However, investors in distressed stocks have not been rewarded for bearing these risks. Instead, distressed stocks have had very low returns, both relative to the market and after adjusting for their high risk. The underperformance of distressed stocks is present in all size and value quintiles. It is lower for stocks with low analyst coverage and institutional holdings, which suggests that information or arbitrage-related frictions may be partly responsible for the underperformance of distressed stocks.

Predicting Financial Distress and the Performance of Distressed Stocks

Abstract: In this paper, we consider the measurement and pricing of distress risk.We present a model of corporate failure in which accounting and market-based measures forecast the likelihood of future financial distress. Our best model is more accurate than leading alternative measures of corporate failure risk. We use our measure of financial distress to examine the performance of distressed stocks from 1981 to 2008. We find that distressed stocks have high stock return volatility and high market betas and that they tend to underperform safe stocks by more at times of high market volatility and risk aversion. However, investors in distressed stocks have not been rewarded for bearing these risks. Instead, distressed stocks have had very low returns, both relative to the market and after adjusting for their high risk. The underperformance of distressed stocks is present in all size and value quintiles. It is lower for stocks with low analyst coverage and institutional holdings, which suggests that information or arbitrage-related frictions may be partly responsible for the underperformance of distressed stocks.

Forecasting Default with the Merton Distance to Default Model

Abstract: We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a “naive” alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naive predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.

Digesting Anomalies: An Investment Approach

Abstract: Motivated from investment-based asset pricing, we propose a new factor model that consists of the market factor, a size factor, an investment factor, and a return-on-equity factor The new model [i] outperforms the Carhart (1997) four-factor model in pricing portfolios formed on earnings surprise, idiosyncratic volatility, financial distress, equity issues, as well as on investment and return-on-equity; [ii] performs similarly as the Carhart model in pricing portfolios on momentum as well as on size and book-to-market; but [iii] underperforms in pricing the total accrual deciles Our model's performance, combined with its clear economic intuition, suggests that it can serve as a new workhorse model for academic research and investment management practice

Digesting Anomalies: An Investment Approach

Abstract: An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model's performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies.