Default Risk in Equity Returns

In this paper, we address the question whether the impact of default risk on equity returns depends on the financial system firms operate in. Using an implementation of Merton's option-pricing model for the value of equity to estimate firms' default risk, we construct a factor that measures the excess return of firms with low default risk over firms with high default risk. We then compare results from asset pricing tests for the German and the U.S. stock markets. Since Germany is the prime example of a bank-based financial system, where debt is supposedly a major instrument of corporate governance, we expect that a systematic default risk effect on equity returns should be more pronounced for German rather than U.S. firms. Our evidence suggests that a higher firm default risk systematically leads to lower returns in both capital markets. This contradicts some previous results for the U.S. by Vassalou/Xing (2004), but we show that their default risk factor looses its explanatory power if one includes a default risk factor measured as a factor mimicking portfolio. It further turns out that the composition of corporate debt affects equity returns in Germany. Firms' default risk sensitivities are attenuated the more a firm depends on bank debt financing.

Default Risk and Equity Returns: A Comparison of the Bank-Based German and the U.S. Financial System

Prior research uses the basic one-period European call-option pricing model to compute default measures for individual firms and concludes that both the size and book-to-market effects are related to default risk. For example, small firms earn higher return than big firms only if they have higher default risk and value stocks earn higher returns than growth stocks if their default risk is high. In this paper we use a more advanced compound option pricing model for the computation of default risk and provide a more exhaustive test of stock returns using univariate and double-sorted portfolios. The results show that long/short hedge portfolios based on Geske measures of default risk produce significantly larger return differentials than Merton’s measure of default risk. The paper provides new evidence that mediates between the rational and behavioral explanations of value premium.

Systematic risk has been a focus for stress testing and risk capital assessment. Under the Vasicek asymptotic single risk factor model framework, entity default risk for a risk homogeneous portfolio divides into two parts: systematic and entity specific. While entity specific risk can be modelled by a probit or logistic model using a relatively short period of portfolio historical data, modeling of systematic risk is more challenging. In practice, most default risk models do not fully or dynamically capture systematic risk. In this paper, we propose an approach to modeling systematic and entity specific risks by parts and then aggregating together analytically. Systematic risk is quantified and modelled by a multifactor Vasicek model with a latent residual, a factor accounting for default contagion and feedback effects. The asymptotic maximum likelihood approach for parameter estimation for this model is equivalent to least squares linear regression. Conditional entity PDs for scenario tests and through-the-cycle entity PD all have analytical solutions. For validation, we model the point-in-time entity PD for a commercial portfolio, and stress the portfolio default risk by shocking the systematic risk factors. Rating migration and portfolio loss are assessed.

In the innovation and development of modern financial technology, the promotion scope of personal lending platform is more and more wide. Although it expands the construction scope of Internet financial platform, it also causes more security risks. In the typical P2P lending platform, for example, the system arrangement on the base of Internet financial platform user transaction data, through a comprehensive analysis of the transmission of personal loans overdue default behavior, and according to the specific characteristics of building model algorithm, not only can accurately identify high-risk users financial platform, also can be validated with the actual default data analysis, It provides technical basis for the effective operation of modern Internet financial platform. Therefore, on the basis of understanding the current development status of Internet financial platforms, this paper constructs an identification model based on users with high default risk, and proposes an identification model with the transmission of certain and probabilistic default risk as the core. Finally, by comparing the two models, it is found that in the identification of 5% and 10% high-risk groups, the algorithm has strong relevance and recall rate, which can be reasonably applied in the future Internet platform.

Is Idiosyncratic Volatility Risk Priced? Evidence from the Physical and Risk-Neutral Distributions

Purpose- The purpose of this study is to investigate the predictive power of various anomalies in the cross-section of equity returns in Borsa Istanbul. Methodology- Covering a sample period between 1988 and 2018, this study implements univariate portfolio analyses. Specifically, each month, stocks are sorted into quintile portfolios based on one anomaly at a time. Next, the one-month-ahead equal- and value-weighted portfolio returns are calculated for each quintile to test whether the return spread between the extreme anomaly portfolios is economically and statistically significant. Findings- The findings using the whole sample document that market beta, firm size, lagged return and lottery demand have a significant and negative relation with future stock returns. In contrast, book-to-market ratio, investment and profitability have a strong positive relation with expected equity returns. Conclusion- All anomalies that are shown to predict U.S. equity returns are not documented in Turkey. Except for the investment anomaly, the directions of these anomalies are in line with the existing literature in the U.S.

Purpose – The purpose of this paper is to examine the interrelationships among default risk, capital and efficiency of the Indian banking system over 1990-2011. This study also took into account the impact of ownership on these interrelationships Design/methodology/approach – This paper employed Data Envelopment Analysis (DEA) Windows Analysis to estimate efficiency levels and trends of individual banks. This paper then used a model of seemingly unrelated regression equations (SURE) to examine the interrelationships among default risk, capital and efficiency. Findings – This study found a two-way negative association between efficiency and default risk, and between capital ratio and default risk. However, this study found a two-way positive relationship between capital ratio and only profit efficiency. Public banks behaved differently from private banks regarding the association between capital and efficiency. Moreover, public banks had greater probability of default risk, lower capital ratio but higher efficiency level than private banks. Further, default risk, capital ratio and efficiency of the Indian banking system increased over time, but the two formers were driven by public banks while the latter was driven by private banks. Practical implications – The findings of this study appear to favour capital ratio as an efficient tool to improve efficiency and reduce default risk of the Indian banking system. Originality/value – This paper is the first investigating the interrelationships between bank risk, capital and efficiency of the Indian banking system, where bank risk is measured by Z-score value and efficiency is captured by cost, revenue and profit efficiencies, and then considering the impact of agency issues on these interrelationships.

Small and medium-sized textile enterprises generally experience financial difficulties, with a high default risk because of the textile industry’s characteristics of low concentration, long industrial chains, and large seasonal fluctuations. Using a two-echelon supply chain model of the textile industry comprising a core retailer and capital-constrained supplier, this study investigates disclosed and undisclosed factoring while considering the default risk of both the supplier and retailer under two guarantee mechanisms: no guarantee and a third-party partial credit guarantee. Utilizing a Stackelberg game model, this study finds that both the default risk and financial institutions’ loan-to-value ratio for accounts receivable significantly affect optimal financing decisions and financing efficiency. First, excessively pursuing higher loan-to-value ratios lowers financing efficiency. In addition, a partial credit guarantee from a third party can effectively reduce the financing interest rate but cannot improve financing efficiency if the supplier assumes the guarantee fee. Thus, while introducing a guarantee mechanism to control financing risk, financial institutions should consider supply chain participants, rather than the supplier, to assume the guarantee fees. Furthermore, both the supplier and retailer should finance through disclosed factoring regardless of a guarantee. Our findings offer textile industry-specific financing insights regarding the options of guarantee and factoring financing type based on the accounts receivable.

We examine how the information in public signals (e.g., earnings, analysts’ forecasts) affects firms’ voluntary disclosures. We consider levered firms and information structures such that investors update their beliefs about both the mean and variance of the firm’s cash flows. We predict that firms that, conditional on the public signal, have a higher default risk are less likely to disclose. For firms with low ex ante default risk, larger positive surprises following the public signal initially increase the disclosure likelihood because they indicate a higher mean of cash flows. However, too large positive surprises may reduce the disclosure likelihood of these firms, because the larger surprise is also indicative of higher variance and, thus, higher probability of default. Symmetrically, larger negative surprises may increase disclosure for firms with high ex ante default risk, despite the more pessimistic inference about the mean, because the higher variance makes it more likely that these firms avoid default. Extending our analysis to multiple analysts’ forecasts, we predict that not only consensus, but also dispersion affect firms’ disclosure likelihood. Consensus conveys information about the mean and variance of cash flows, whereas dispersion is informative about the precision of analysts’ signals, which in turn affects the information content of forecasts. This paper was accepted by Suraj Srinivasan, accounting. Funding: D. Lee-Lo gratefully acknowledges financial support from the Accounting Research Center at Kellogg.

Examining the relationship between default risk and efficiency in Islamic and conventional banks

ABSTRACTThe Fama–French factors HML and SMB are correlated with innovations in variables that describe investment opportunities. A model that includes shocks to the aggregate dividend yield and term spread, default spread, and one‐month Treasury‐bill yield explains the cross section of average returns better than the Fama–French model. When loadings on the innovations in the predictive variables are present in the model, loadings on HML and SMB lose their explanatory power for the cross section of returns. The results are consistent with an ICAPM explanation for the empirical success of the Fama–French portfolios.

In this study we present a comprehensive forward-looking portfolio simulation methodology for assessing the correlated impacts of market risk, private sector and Sovereign credit risk, and inter-bank default risk. In order to produce better integrated risk assessment for banks and systemic risk assessments for financial systems, we argue that reasonably detailed modeling of bank asset and liability structures, loan portfolio credit quality, and loan concentrations by sector, region and type, as well as a number of financial and economic environment risk drivers, is required. Sovereign and inter-bank default risks are increasingly important in the current economic environment and their inclusion is an important model extension. This extended model is demonstrated through an application to both individual Brazilian banks (i.e., 28 of the largest banks) and groups of banks (i.e., the Brazilian banking system) as of December 2004. When omitting Sovereign risk, our analysis indicates that none of the banks face significant default risk over a 1-year horizon. This low default risk stems primarily from the large amount of government securities held by Brazilian banks, but also reflects the banks' adequate capitalizations and extraordinarily high interest rate spreads. We note that none of the banks which we modeled failed during the very stressful 2007-2008 period, consistent with our results. Our results also show that a commonly used approach of aggregating all banks into one single bank, for purposes of undertaking a systemic banking system risk assessment, results in a misestimate of both the probability and the cost of systemic banking system failures. Once Sovereign risk is considered and losses in the market value of government securities reach 10% (or higher), we find that several banks could fail during the same time period. These results demonstrate the well known risk of concentrated lending to a borrower, or type of borrower, which has a non-zero probability of default (e.g., the Government of Brazil). Our analysis also indicates that, in the event of a Sovereign default, the Government of Brazil would face constrained debt management alternatives. To the best of our knowledge no one else has put forward a systematic methodology for assessing bank asset, liability, loan portfolio structure and correlated market and credit (private sector, Sovereign, and inter-bank) default risk for banks and banking systems. We conclude that such forward-looking risk assessment methodologies for assessing multiple correlated risks, combined with the targeted collection of specific types of data on bank portfolios, have the potential to better quantify overall bank and banking system risk levels, which can assist bank management, bank regulators, Sovereigns, rating agencies, and investors to make better informed and proactive risk management and investment decisions.

Using a transactor/revolver scorecard to make credit and pricing decisions

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