Alternative Asset Pricing Models Research Articles

Stock price delay and the cross-section of expected returns: A story of night and day

We examine how individual stock price reacts to intraday and overnight market information. By examining stock betas, we document a stark gap between day beta and night beta and nontrivial asynchronous beta, which captures the slow diffusion of information. We provide evidence that this information delay can predict future stock returns over a one-month period. Long-short portfolios sorted on the gap between day beta and night beta and asynchronous beta generate raw returns of 0.8% and 0.39% and risk-adjusted alphas of 0.77% and 0.28% per month. These results are robust to alternative asset pricing models and when controlling for firm characteristics, such as size, book-to-market ratios, liquidity, investor recognition and limits-to-arbitrage characteristics. We also explore the heterogeneity of the information delay premium and find that the predictive power of information delay for future return is stronger among small, value firms, less visible firms, illiquid firms and firms with higher limit-to-arbitrage. We also provide evidence that our measure of price delay indeed offers incremental information for cross-section return prediction after we explicitly control for the conventional measure of Hou and Moskowitz (2005)’s price delay.

In the shadow of country risk: asset pricing model of emerging market corporate bonds

We examine the covariances of corporate bonds in emerging markets (EM) and present an asset pricing framework using instrumented principal component analysis (IPCA) that includes characteristics at the sovereign and bond levels. Our results indicate that EM bond returns are significantly influenced by country-specific risks. Incorporating these characteristics can improve both the total and cross-sectional model fit. We demonstrate that a factor framework tailored to the nuances of the EM universe generates a significant alpha of 2% per annum against the market and a higher information ratio than alternative asset pricing models, such as a conditional beta model designed for developed market (DM) bonds.

Enhancing stock market anomalies with machine learning

We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8–2.0%, and over 80% of the models yield returns equal to or larger than our linearly constructed baseline factor. For the best performing models, the risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.

Problems Related to the Capital Assets Pricing Model on the Warsaw Stock Exchange: Applications of the 5-Factor Fama and French Model

This paper represents an attempt at empirically assessing the applicability of the Fama and French five-factor model in explaining the cross-sectional variation of stock return for the Polish market. Consistent with Fama and French results, this research shows that value, profitability and investment risk factors play an important role in assessing the expected return of an asset. Many researchers have since sought to identify alternative asset pricing models that could serve as the benchmark empirical asset-pricing model. It is hoped that the analysis conducted in this paper tests whether the revised five-factor model that incorporates the profitability and investment factors can address some of the issues of the three-factor model and could be incorporated in explaining the cross-section of stock returns for Poland.

Enhancing Stock Market Anomalies with Machine Learning

We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month-anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8-2.0%, and over 80% of the models yield returns equal or larger than our linearly constructed baseline factor. The risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.

Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test

This paper examines relative performance of alternative asset pricing models using individual security returns. The standard multivariate test used in studies comparing the performance of asset pricing models requires the number of stocks to be less than the number of time series observations, which requires grouping stocks into portfolios. This results in a loss of disaggregate stock information. We apply a different statistical test to overcome this problem and to investigate relative performance of alternative asset pricing models using individual security returns instead of portfolio returns. Our findings suggest that a parsimonious six-factor model that includes the momentum and orthogonal value factors outperforms all other models based on a number of measures as well as the average [Formula: see text]-test. Unlike the standard multivariate test, we find that the average [Formula: see text]-test has superior power to discriminate among competing models and does not reject all tested models.

Asset Pricing Test Using Alternative Sets of Portfolios: Evidence from India

Empirical test of asset-pricing models are typically performed on portfolios based on firm-characteristics such as size and book-to-market ratios etc. However, because of their strong factor structure, the characteristic sorted portfolios do not provide a sufficient test for asset pricing models. In recent, the appropriateness to use characteristics sorted portfolios has been debated. Literature suggests various alternative test portfolios sorted by other attributes to improve the empirical tests. To address this issue, we construct three sets of test portfolios sorted by firm beta, volatility, and clustering method to test various asset pricing models. We examine whether portfolios sorted by the above methods can improve the explanatory power of various alternative asset pricing models. Our test results suggest that for unconditional models, the statistical significance and estimated risk premiums depend on the choice of tests portfolios. The conditional model has more power to explain the variation of average returns than the unconditional model.

Impact of disputed tax litigation risk on firm performance: evidence from India

PurposeThe purpose of this study is to investigate the impact of disputed tax litigation risk on firm performance and stock return behavior using a sample of Indian listed firms.Design/methodology/approachThe authors use disputed tax liability, reported as a contingent liability by the listed firms, as a proxy for the disputed tax litigation risk. To examine the impact of disputed tax litigation risk on firm performance (measured by accounting and market-based measures), the empirical approach used in this study focusses on the panel estimation technique. A portfolio-based approach using alternative asset pricing models examines the cross-sectional return variation because of the influence of disputed tax litigation risk.FindingsThe results of this study show a negative relationship between firm performance measures and disputed tax litigation risk. Cross-sectional test results reveal that higher disputed tax litigation risk is associated with higher expected returns.Research limitations/implicationsThis study focusses on disputed tax reported under the heading of contingent liability as a proxy for litigation risk. The study will help investors and portfolio managers to consider disputed tax litigation risk as an important parameter in the evaluation of firm performance. This study will also help regulators to get feedback on tax related policies and improve the dispute resolution process.Originality/valueThis study adds to the existing literature on the relationship between litigation risk and firm performance. In the context of emerging market, this study is the first-of-its-kind study, which focusses on disputed tax as a litigation risk proxy and examines its possible impact on firm performance and stock return behavior.

Cross-Section of Asset Returns: Emerging Markets and Market Integration

Asset pricing in its essence is a very controversial topic. Despite numerous research papers criticising traditional approaches, such as linear factor models, practitioners as well as academics repeatedly return to the milestone models such as the Capital Asset Pricing Model (CAPM), mainly due to their attractive simplicity. This article focuses on the risk-return relationship by comparing the power of traditional and alternative asset pricing models in explaining the cross-section of asset returns. The focus is on unconditional models, commonly used among investors and equity analysts. This paper is based on the research performed by Estrada in 2004 and it extends his approach by introducing the use of GMM. The results suggest that for Emerging markets' investors should give preference to total risk measures over systematic risk measures. Within the category of systematic risk measures, downside beta proved its superiority to traditional CAPM beta. The results can be attributed to delayed integration process, partially justified by the lower FDI and portfolio investments into Emerging markets.

Bank Capital and Bank Stock Performance

Little is known about how bank capital affects bank stock performance. We show that capital does not affect returns unconditionally, but high-capital banks have higher risk-adjusted stock returns (alphas) than low-capital banks in bad times in and out of sample. Trading strategies earn 3.60%–4.44% annually. The results are robust to: using different bad times and capital definitions, alternative asset pricing models, and ex-ante expected returns; controlling for performance-type delistings, short-sale constraints, and trading costs; and dropping the largest or smallest banks. Our results seem to be driven by a “Surprised Investor Channel” rather than by an “Informed Investor Channel.”

Evaluating alternative performance benchmarks for Indian mutual fund industry

PurposeThe purpose of this paper is to perform a relative assessment of performance benchmarks based on alternative asset pricing models to evaluate performance of mutual funds and suggest the best approach in Indian context.Design/methodology/approachSample of 237 open-ended Indian equity (growth) schemes from April 2003 to March 2013 is used. Both unconditional and conditional versions of eight performance models are employed, namely, Jensen (1968) measure, three-moment asset pricing model, four-moment asset pricing model, Fama and French (1993) three-factor model, Carhart (1997) four-factor model, Elton et al. (1999) five-index model, Fama and French (2015) five-factor model and firm quality five-factor model.FindingsConditional version of Carhart (1997) model is found to be the most appropriate performance benchmark in the Indian context. Success of conditional models over unconditional models highlights that fund managers dynamically manage their portfolios.Practical implicationsA significant α generated over and above the return estimated using Carhart’s (1997) model reflects true stock-picking skills of fund managers and it is, therefore, worth paying an active management fee. Stock exchanges and credit rating agencies in India should construct indices incorporating size, value and momentum factors to be used for purpose of benchmarking.Originality/valueThe study adds new evidence as to applicability of established asset pricing models as performance benchmarks in emerging market India. It examines role of higher order moments in explaining mutual fund returns which is an under researched area.

Testing Stock Market Efficiency and the Asset Pricing Model: Some Evidence from Sri Lanka

This paper examined the Price-Earnings anomaly and the Capital Asset Pricing Model (CAPM) for the Colombo Stock Exchange (CSE) in Sri Lanka over the period 2004 to 2013. Stock market anomalies inevitably challenge the validity of the Efficient Market Hypothesis and the accuracy of the asset pricing model used in measuring stock returns. Therefore, the accuracy of the asset pricing model used to measure stock returns is in the centre of the market efficiency debate, and the CAPM is an extensively applied asset pricing model in the Sri Lankan capital market. A stochastic version of the CAPM time-series regression was therefore adopted in this study to estimate portfolio abnormal returns as measured by Jensen’s Alpha. The analysis found systematically high abnormal returns from portfolios of stocks with a low Price-Earnings ratio relative to the portfolios consisting of stocks with a high Price-Earnings ratio. This suggests anomalous pricing behaviour in capital market operations in Sri Lanka. Price-Earnings anomaly hypothesises either the CAPM is mis-specified and/or capital markets are inefficient. If the CAPM appears to be a correctly specified asset pricing model that accurately explains stock returns in response to their true level of systematic risk, Price-Earnings anomaly evidently undermines the semi-strong form stock market efficiency. To shed light on this joint hypothesis problem, the study investigated the accuracy of the CAPM. Results of a two-pass regression approach similar to Black, Jensen and Scholes (1972) and Fama and Macbeth (1973) empirical work indicated that the CAPM-beta does not appropriately explain stock returns in Sri Lanka. As such, it appears that the CAPM suffers from omitted variable bias problem and hence mis-specified. In the absence of statistically significant evidence to constitute accuracy of the CAPM, Price-Earnings anomaly observed in this study appears to have been produced either by measurement inconsistencies of the mis-specified CAPM or by a combined effect of both CAPM-misspecifications and capital market inefficiency. From the policy front, market efficiency is an important attribute and the accuracy of the asset pricing model is a vital strand in market efficiency tests. An investigation of market anomalies using alternative asset pricing models such as Fama-French (2015) five-factor model might be a potential area for future research. Asset pricing models are being constantly investigated and these studies may find further specifications in the contemporary asset pricing model.

Does investor sentiment as conditioning information help to explain stock returns behaviour? A test of alternative asset pricing models

Purpose The purpose of this paper is to use investor sentiment (IS) as a conditioning information variable for the cross-sectional return predictability tests of alternative asset pricing models (APMs).

Test of CAPM: A Study of India and US

Asset pricing is one of the most important research areas in the field of finance. The simple CAPM model (capital asset pricing model) relates the return of the stocks and portfolios to the market factor captured by beta. Since the formulation of CAPM in 1960s, asset pricing has covered a long distance. We conduct the test of CAPM for India and US by using data from January 2001 to December 2015. We run 84 second pass cross-sectional regression equations to test the applicability of CAPM. The results of our test find that CAPM is not able to capture the cross section of average returns both in India and US and we should consider the alternative asset pricing models to establish the risk-return relationship.

Factors Associated With and Long Term Stock Price Performance of Firms Making Disclosures of Auditor-Client Frictions

Financial Reporting Release (FRR) No. 31 (SEC 1988) changed the disclosure requirements about auditor-client frictions (i.e., disagreements and reportable events) in auditor change announcements. The first goal of the study is to document factors associated with an auditor change sample disclosing auditor-client frictions compared with an auditor change sample not making such disclosures in auditor change announcements. We use 5,128 observations of auditor changes obtained from a sample period following enactment of FRR No. 31. In the fiscal periods in which the frictions are reported to have occurred, firms disclosing auditor-client frictions tend to have (1) higher levels of audit risk, (2) deteriorating financial condition, (3) higher levels of growth, (4) higher incidence of new going-concern audit opinion, (5) more auditor resignations, (6) greater incidence of Big 5/6 predecessor auditors, and (6) lower incidence of Big 5/6 successor auditors. We also find that auditor change firms with disclosures of auditor-client frictions tend to be (7) younger firms, (8) exhibit higher audit risk, and interestingly, occur in (9) larger firms with (10) increasing levels of institutional ownership. The second goal of the study is motivated by a growing body of empirical evidence from a variety of contexts that indicates market participants under-react to certain disclosures. Also, the SEC (1988) suggests that both types of auditor-client frictions may be potential early warning signs of trouble. We find that firms disclosing auditor-client frictions exhibit long-run drift in stock prices in the same direction as the initial price reaction. Stock prices indicate that investors under-react to disclosures about auditor-client frictions in auditor change announcements. The evidence of under-reaction to the auditor change sample disclosing auditor-client frictions is robust to alternative asset pricing models and tests suggested by the literature documenting appropriate research design choices for long-run stock price tests.

Test of CAPM: A Study of India and US

Asset pricing is one of the most important research areas in the field of finance. The simple CAPM model (capital asset pricing model) relates the return of the stocks and portfolios to the market factor captured by beta. Since the formulation of CAPM in 1960s, asset pricing has covered a long distance. We conduct the test of CAPM for India and US by using data from January 2001 to December 2015. We run 84 second pass cross-sectional regression equations to test the applicability of CAPM. The results of our test find that CAPM is not able to capture the cross section of average returns both in India and US and we should consider the alternative asset pricing models to establish the risk-return relationship.

Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test

Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test

Do Asset Pricing Models Explain Size, Value, Momentum and Liquidity Effects? The Case of an Emerging Stock Market

This article examines whether the alternative asset pricing models and more specifically the liquidity-augmented multifactor models can explain the effect of size, value, momentum and liquidity on cross section of stock returns in India during September 1995 to March 2011. We employ time series and panel data methodology to carry out the analysis. Our findings suggest that the value and liquidity effects are often explained, but the explanatory power of size and short-run past return or momentum effect remain consistent irrespective of alternative asset pricing models risk adjustment process. The liquidity-augmented multifactor models are found to have better explanatory power than to the other alternative multifactor models. The relative performance of liquidity-augmented multifactor modes for capturing the role of firm characteristics on stock returns varies across the individual firms’ liquidity sensitivity and the aggregate market liquidity conditions. JEL Classification: G11, G12, G14, G15

Multifactor Explanations of CAPM Anomalies: An Evidence for Indian Stock Market

We evaluate the ability of alternative asset pricing models in explaining returns on various characteristic (company size and value) sorted and prior return ranked portfolios. Data is employed from January, 1997 to June, 2012 for 488 companies listed on BSE-500 index. We find that Fama-French three factor model performs better than one factor capital asset pricing model in explaining mean excess returns on characteristic sorted portfolios. We also observe that Fama-French model partly explains long term, reversal, and momentum profits. Asset pricing results are found to be vibrant to the alternate versions of company size and value factors and choice of different market proxies as the FF model (in all its versions) outperforms CAPM. We further show that the Carhart four factor model involving an additional momentum factor, does not significantly perform better than FF model for different portfolios except short term momentum profits.

Asset Pricing Tests in the Egyptian Stock Market

This paper empirically tests and compares the applicability of five alternative asset pricing models for 55 stocks listed on the EGX-100 for the Egyptian stock market: 1) the CAPM, 2) the Fama-French three factor model, 3) the Cahart model 4) the four factor model of Chan and Faff (2005, liquidity-augmented Fama-French three factor model) 5) and the five-factor model (liquidity and momentum-augmented Fama-French three factor model). This sample is split into six portfolios sorted on size and book-to-market ratio. The tests employ time series regression of Black, Jensen and Scholes (1972). Our results show evidence that with respect to the quality of prediction, FF three factor model leads to a remarkable enhancement over the CAPM, and the other models don’t show a significant increase over the FF three factor model. GRS (1989) F-statistics accept FF three factor model and reject the appropriateness of the other models.