Financial distress and equity returns: A leverage-augmented three-factor model

Boubaker, Sabri; Hamza, Taher; Vidal-García, Javier

doi:10.1016/j.ribaf.2016.09.003

Cited by 25 publications

(13 citation statements)

References 46 publications

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“…This shows that the additional factors are improving the prediction power of the returns obtained on different portfolios. These results support the findings of some of the major studies which contend that adding leverage as a variable increases the prediction ability of the asset pricing models (Chou et al, 2010;Boubaker et al, 2018).…”

Section: Results Of Hierarchical Linear Regressionsupporting

confidence: 88%

“…Koseoglu (2013) revealed that the leverage mimicking factor increased the descriptive ability of the model by providing a good description for crosssectional variations of stock returns in Istanbul Stock Exchange as compared to the Fama-French three-factor model. Boubaker et al (2018) found that leverage risk premium was positive for firms with high leverage. Maiti and Balakrishnan (2020) indicated that a substantial portion of stock returns are explained by debt to equity ratio which is a leveragebased risk factor.…”

Section: Review Of Literaturementioning

confidence: 98%

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Role of leverage and liquidity risk in asset pricing: evidence from Indian stock market

Jain

Singla

2021

XJM

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Purpose Asset pricing revolves around the core aspects of risk and expected return. The main objective of the study is to test different asset pricing models for the Indian securities market. This paper aims to analyse whether leverage and liquidity augmented five-factor model performs better than Capital Asset Pricing Model (CAPM), Fama and French three-factor model, leverage augmented four-factor model and liquidity augmented four-factor model. Design/methodology/approach The data for the current study comprises records on prices of securities that are part of the Nifty 500 index for a time frame of 14 years, that is, from October 2004 to September 2017 consisting of 183 companies using time series regression. Findings The results indicate that the five-factor model performs better than CAPM and the three-factor model. The model outperforms leverage augmented and liquidity augmented four-factor models. The empirical evidence shows that the five-factor model has the highest explanatory power among the entire asset pricing models considered. Practical implications The present study bears certain useful implications for various stakeholders including fund managers, investors and academicians. Originality/value This study presents a five-factor model containing two additional factors, that is, leverage and liquidity risk along with the Fama-French three-factor model. These factors are expected to give more value to the model in comparison to the Fama-French three-factor model.

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Section: Results Of Hierarchical Linear Regressionsupporting

confidence: 88%

Section: Review Of Literaturementioning

confidence: 98%

Role of leverage and liquidity risk in asset pricing: evidence from Indian stock market

Jain

Singla

2021

XJM

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“…Mselmi et al (2019) examined financial distress, liquidity, and valueat-risk effect on stock returns for the French stock market. Financial distress was found to be consistently and positively significant to the pricing of stock returns for the financial distress portfolio in all models used in the study, which was somewhat similar to the results of Boubaker et al (2018). Mselmi et al (2019) also found that financial distress was significant to the pricing of stock returns only in the absence of size and book-to-market factors.…”

Section: Financial Distress Risk and Stock Returnsupporting

confidence: 77%

“…The results indirectly showed that stock return has a positive relationship with financial distress risk. Boubaker et al (2018) examined whether financial distress risk affects stock return by using 12 portfolios sorted by size, book-tomarket, and leverage and a portfolio of distressed firms covering an 18-year period. The main goal of this study was to identify the risk factors that best captured the default risk in the French context.…”

Section: Financial Distress Risk and Stock Returnmentioning

confidence: 99%

Predicting Financial Distress in Malaysia and Its Effect on Stock Returns

Hanafi

Md-Rus

Mohd

2021

International Journal of Banking and Finance

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Unstable economic conditions have an adverse impact on the financial performance of firms, leading to financial distress, which is an unfavourable situation for investors as it may affect their investment returns. Thus, this study attempted to predict financial distress and to examine the effect of financial distress on stock returns by using firms listed on Bursa Malaysia from 1990 to 2020. This study used the logit model to find the probability of bankruptcy and also as a proxy for financial distress risk in the asset pricing model. From this study, financial distress risk was found to be insignificant in pricing stock returns in all tested models. This finding demonstrates that financial distress risk does not affect stock returns since this risk may be eliminated through diversification.

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“…López Gutiérrez et al (2009) note how the type of bankruptcy law determines the valuation of a firm's stocks, equally, a fall in share price is higher in creditororiented systems whereas unfavorable returns are lesser in debtor-oriented arrangements. Boubaker et al (2018) analyzed financial distress (as a systemic risk) and its effect on equity returns and state that; the risk premium for the relative aspect factor is crucial only for the shaky business portfolio(s). Hsieh et al (2012) indicate that the capability to produce competent forecasts about financial distress continues to be a significant problem in corporate monetary administration.…”

Section: An Overview Of Financial Distress Prediction Techniques Andmentioning

confidence: 99%

Financial Distress, Prediction, and Strategies by Firms: A Systematic Review of Literature

Mallinguh

Zéman²

2020

Period. Polytech. Soc. Man. Sci.

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The paper is systematic scrutiny of studies on financial distress, prediction, and strategies firms adapt to deal with the difficulty. To this end, the paper offers a dissection and assortment of 72 articles published between 2005 and 2017 in Scopus, Web of Science, and Science Direct. The authors chose the three databases as articles that are published only in indexed journals. The studies were selected based on the key terms "financial distress", "financial strategies", "financial distress prediction", and "financial distress strategies". The selected articles were evaluated based on seven categories: content, methodology, scope, and data analysis techniques, study period, study focus, and data analyzed. The evaluation and assortment of studies identified existing disparities in the literature on financial distress, offering opportunities for future researchers. Exceptional articles on financial challenges, prediction, and strategies adopted by firms were identified. The study finds that most of the studies centered on mature economies, whereas those on emerging markets-focused only on Asian markets. Equally, there are very few qualitative studies on the subject matter. Through the study, the authors paint a picture of existing literature on the subject matter; further, the authors expect the review to stimulate debate and further research among scholars.

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Financial distress and equity returns: A leverage-augmented three-factor model

Cited by 25 publications

References 46 publications

Role of leverage and liquidity risk in asset pricing: evidence from Indian stock market

Role of leverage and liquidity risk in asset pricing: evidence from Indian stock market

Predicting Financial Distress in Malaysia and Its Effect on Stock Returns

Financial Distress, Prediction, and Strategies by Firms: A Systematic Review of Literature

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