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Lai, Li, Conover, and Wu (2010) propose a four-factor financial distress model to explain stock returns in the U.S. and Japanese markets. We examine this model in the stock markets of Australia, and six Asian markets (Hong Kong, Indonesia, Korea, Malaysia, Singapore, and Thailand). We find broad empirical support for the four-factor financial distress risk asset-pricing model in those markets. The four-factor financial distress asset pricing model improves explanatory power beyond the Fama–French (1993) three-factor asset pricing model in six of the seven Asian-Pacific markets (12 of 14 portfolio groupings), while the Carhart (1997) momentum-based asset pricing model only improves explanatory power beyond the Fama–French model in three of the seven markets (4 of 14 portfolio groupings).

We examine explicitly priced financial distress risk in post-1990 equity markets. We add a financial distress risk factor to Fama and French's (1993) three-factor model, based on Griffin and Lemmon's (2002) findings that financial distress is not fully captured by the book-to-market factor. We test three-factor and four-factor capital asset pricing models using both annual buy-and-hold analysis and monthly time series analysis across portfolios adjusted for common book-to-market, size, and financial distress factors. We find empirical support for an Ohlson (1980) O-score-based financial distress risk four-factor asset pricing model in the U.S. and Japanese markets.

This study aims to analyse the following: first, the financial performance of General Insurance Re (GIC Re) using performance ratios (PRs); second, the uniformity of different financial performance indicators of GIC Re; third, the internal growth capacity of GIC Re; and finally, the likelihood of GIC Re going into financial distress.

As a sample, GIC Re, the lion shareholder in Indian Reinsurance Industry has been considered in the present study. All the necessary data have been extracted from the secondary sources over a time period of 16 years. The financial performance of GIC Re is assessed using five standard ratios, and the uniformity of different financial performance indicators of GIC Re has been examined using Kendall’s Coefficient of Concordance (W). To assess the internal growth capacity of GIC Re internal growth rate has been used, and the likelihood of GIC Re going into financial distress is analysed using multivariate discriminant approach, namely, modified Altman’s Z-score model and logit analysis technique, namely, Ohlson’s O-score model.

The results exhibit that financial performance of GIC Re is somewhat satisfactory over a few considerable areas. However, no notable degree of uniformity has been observed amongst the varied financial performance indicators, namely, performance ratio, expense ratio, return on assets, risk retention ratio and combined ratio of GIC Re. The results also reveal GIC Re is lacking ability of growing internally. Moreover, there remains a significant possibility of GIC Re going into financial distress in the near future and so.

This study is one of the first empirical research studies in India that examines the financial performance of GIC Re from different perspectives.

This paper aims to examine the empirical relationship between firm-level characteristics and the variability of the average portfolio returns of distressed firms. The cross-sectional role of momentum in the market mispricing of distressed firms is evaluated. Distress risk associated with size and book-to-market ratio is also disentangled.

All of NYSE, AMEX and NASDAQ stocks between January 1972 and December 2008 are used, and the individual and joint role of firm characteristics are studied in detail. Using a measure of distressed stocks based on Campbell, Hilscher and Szilagyi (CHS, 2008), new findings on how stock return anomalies are related to the interactions between firm characteristics and financial distress risk are provided.

The findings show that the size and value effects are not due to distress risk. Also, contrary to the existing empirical evidence, momentum does not proxy for distress risk. Furthermore, in the cross-sectional analysis, momentum subsumes the effect of size risk, and book-to-market acts as an independent state variable.

The exposition of the paper is limited in many directions. To measure the extent of financial distress, only the model of CHS (2008) is used. As the level of distress is the key input in the paper, it would be interesting to use some other measure of distress, such as Z-score and O-score in the sample.

Collectively, the pricing results in this paper help to foster a better understanding of the nature of distressed stocks, and the identification of distress risk premium. It will help scholars and investment professionals to make robust portfolio management decisions.

Overall, this paper investigates an important research direction that can potentially shed new light on our understanding of the risk–return relationship of financially distressed stocks. The individual effect of momentum on the variability of the distressed firm’s average returns is highlighted. A formal cross-sectional test of the relationship between distress risk and firm characteristics that include momentum is presented. None of them is quite known in the existing literature.

The authors study stock and option grants around abrupt performance declines for continuing CEOs and find that firms facing abrupt financial declines grant more options than stock, while firms facing operational decline grant more stock than options. Firms making these adjustments just prior to performance declines outperform those that do not for three years following the decline and are less likely to engage in asset restructuring. To establish causality, the authors exploit compensation changes instigated by FAS 123R accounting regulation in 2005 that mandated stock option expensing. The result is robust to numerous tests, including rebalancing of incentives and CEO turnover. The paper aims to discuss these issues.

To establish causality, the authors exploit compensation changes instigated by FAS 123R accounting regulation in 2005 that mandated stock option expensing.

Firms making these adjustments just prior to performance declines outperform those that do not for three years following the decline and are less likely to engage in asset restructuring. The result is robust to numerous tests, including rebalancing of incentives and CEO turnover.

Several studies examine the relationship between poor performance and compensation of newly appointed CEOs. But firms regularly employ retention or incentive plans when experiencing distress to prevent critical employees from leaving when they are most needed (Goyal and Wang, 2017). Employee turnover results in a loss of continuity coupled with high search and training costs for replacement personnel. Beneish et al. (2017) find that 57 percent of CEOs associated with intentional misreporting retain their jobs, implying the costs of removing CEOs is high, especially if the incumbent CEO has a strong track record relative to industry peers prior to the period before the misreporting begins. The board fires the CEO if future firm value under the CEO is expected to be lower than under the best alternative CEO less adjustment costs (e.g. search costs, severance pay).

This study seeks to explore developments in corporate creditworthiness before and after ownership events.

The paper uses the Blockholders database of Dlugosz, Fahlenbrach, Gompers, and Metrick, and three credit quantities, and deploys a standard event study methodology to examine the relation to corporate creditworthiness.

The paper discovers that ownership‐construction is generally associated with prior‐ and post‐improvement in creditworthiness, while a block‐destruction is typically surrounded by deterioration in corporate creditworthiness. The paper also finds proper evidence for a relation between the construction or destruction of managerial block and future developments in corporate creditworthiness. The paper further realizes that outside shareholders exhibit higher impact than inside block‐holders on later variations in credit risk.

The paper is unable to conduct further robustness checks with structural credit methodologies due to the reduced number of valid observations.

Market participants can utilize the conclusions to better predict future trends in corporate creditworthiness.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

Earlier research found that firms with the highest distress risk have low book‐to‐market (B/M) ratios and low returns. This paper aims to examine the robustness of those's results and provide further evidence that high distress‐risk firms do not enjoy the same high returns earned by high B/M firms and that distress risk is unlikely to explain the Fama and French high‐minus‐low (HML) B/M factor.

A distress‐risk measure, distressed‐minus‐solvent (DMS), is calculated and a range of zero investment distress‐risk trading strategies is investigated. Value‐ and equal‐weighted portfolios are examined both with negative book‐equity firms and without. These most distressed firms have low or negative B/M values and would either not be included in the Fama and French sample or included in the low B/M portfolio.

The paper finds that the DMS factor is negative and significant, and none of the zero investment strategies earns significantly positive returns.

The findings suggest that exposure to distress risk does not earns investors a positive risk premium. It appears that over the period examined, market inefficiencies drive the market value and returns of high distress‐risk firms.

The distress‐risk premium is shown to be negative and, therefore, cannot be driven by bankruptcy risk alone. The negative premium is not consistent with a financial distress explanation for the Fama and French HML factor.

Examines the capital structure decisions of restaurant firms. Hypothesizes that these decisions are based upon a financial “pecking‐order” as well as the position of the firm in the financial growth cycle. Using ratios from publicly‐traded restaurant firms in the USA and ordinary least squares regression models, the results tend to support the notion that both the pecking‐order and the financial growth cycle influence financing decisions. However, the results also indicate that there may be separate factors affecting long‐term and short‐term debt decisions made by restaurant managers.

The purpose of this paper is to investigate whether a non-monotonic relationship may exist between financial distress and foreign exchange (FX) exposure. The authors hypothesize that firms with higher FX exposures are those with the lowest levels of financial distress because the costs of hedging exceed the benefits and those with highest levels of financial distress due to the conflict of interest between shareholders and bondholders.

The methodology allows for the possibility of a non-monotonic relation between financial distress and FX exposure for firms known to have ex-ante exposures. The approach is to include a Black-Scholes-Merton financial distress measure and standard accounting-based financial distress measures.

The results support the hypothesis of a non-monotonic relationship between financial distress and exposure; companies with the lowest and highest levels of financial distress are willing to bear greater FX exposures.

The authors examine whether a non-monotonic relationship may exist between distress and FX exposure. Intuition for this non-monotonic relationship is provided by Stulz (1996) as he describes the risk management practices of firms with low, medium, and high default probabilities.