Multifactor Asset Pricing Models Research Articles

Asset pricing in African frontier equity markets

This paper undertakes a horse races style comparison of the efficacy of a range of multifactor asset pricing models in explaining the cross section of stock returns in African securities markets. Valuation factors used include size, book-to-market value, momentum, operating profit, asset growth or investment, liquidity and investor protection. Using monthly returns of 375 blue chip firms from 8 African equity markets over 23 years, we undertake a horse-race style comparison of various classes of augmented CAPM models. We show that both the Fama & French (2015) five factor and Fama & French (2018) six factor framework yield the highest explanatory power. Analysis of costs of equity and optimized portfolio opportunity set simulations reveal substantial differences arising and borne by practitioners from the contrasting application of different asset pricing models underscoring the timely importance of our study.

Application of Multi-Factor Financial Models in Asset Pricing

This paper explores the theoretical underpinnings and practical applications of multi-factor models in stock portfolio management. It outlines the theoretical frameworks of multi-factor asset pricing models such as APT theory, Fama-French three-factor, and five-factor models, analyzing how various risk factors influence asset returns. The study then discusses methods for identifying core risk factors empirically, constructing portfolios, optimizing weight allocations, and evaluating investment performance. Empirical testing using data from the CSI 300 and CSI 800 indices confirms that optimized multi-factor portfolios achieve significant excess returns while managing risk effectively, showcasing the practical utility of this quantitative investment framework in the Chinese A-share market.

On the importance of asset pricing factors in the relative valuation

In this study, we examine the implications of multi-factor asset pricing models in corporate valuation, focusing on the relative valuation approach. Our investigation centers around an eight-factor model, which we compare against traditional valuation methods that rely on industry or risk grouping and cross-sectional averages. The purpose of this comparison is to assess the efficacy of incorporating pricing factor variables into the valuation process and to understand their impact on valuation accuracy. The findings of our analysis suggest that the use of the eight-factor model leads to a reduction in estimation errors of valuation multiples, indicating an important improvement in precision compared to conventional valuation methods. Additionally, our research offers insights into the practicality of applying multi-factor models. It contributes to the intersection between empirical asset pricing and corporate finance by providing a detailed evaluation of advanced valuation models and their relevance in improving the accuracy of relative valuation.

Are minimum variance portfolios in multi-factor models long in low-beta assets?

Within the one-factor capital asset pricing model (CAPM), the minimum-variance portfolio (MVP) is known to have long positions in those assets of the underlying investment universe whose betas are less than a well-defined long-short threshold beta. We study the structure of MVPs in more general multi-factor asset pricing models and clarify the low-beta puzzle for multi-factor models: For multi-factor models we derive a similar criterion in terms of the betas with explicit closed-form formulas. But the structural relationship is now more involved and the long-short threshold turns out to be asset-specific. The results rely on recursive inverse-free formulas for the precision matrix, which hold for multi-factor models and allow quick computation of that inverse matrix without the need to invert matrices going beyond diagonal ones. We illustrate our findings by analyzing S &P 500 asset returns. Our empirical results of the S &P 500 constituents between 2019 and 2022 confirm the theoretical findings and shows that the minimum variance portfolio is long in low-beta assets when applying estimates of the established asset-specific thresholds.

Multi-factor Asset Pricing Models in Emerging and Developed Markets

Multi-factor Asset Pricing Models in Emerging and Developed Markets

Testing multifactor asset pricing models in the stock market

lthough the superiority of Fama-French (FF) five-factor model in capturing the United States (US) equity returns, this model performs poorly in other stock markets (Fama & French, 2017). Using the monthly data of nearly 600 Vietnamese published firms from 2008 to 2022, the primary purpose of this paper is to analyze and examine the performance of four famous multifactor asset pricing models: the capital asset pricing model (CAPM), the Carhart four factor model, and the FF three-factor and five-factor models. We document the preference for the Carhart four-factor model over other models in producing a precise description to Vietnamese stock returns. The CAPM cannot give a reasonable explanation to the variation of Vietnamese stock returns, implying that market risk only accounts for a small proportion of the risk of holding Vietnamese stocks. Furthermore, adding the profitability and investment factors does not improve the explanatory power of asset pricing models in Vietnam, inconsistent with the result reported in the US stock market (Fama & French, 2015, 2020).

An Investigation of Multi-factor Asset Pricing Models in the UK

An Investigation of Multi-factor Asset Pricing Models in the UK

Digital maturity index for Russian public companies

Subject. The article addresses the digital premium in stock prices as a risk factor in multi-factor asset pricing models in the Swedish and Russian stock markets. Objectives. The purpose is to assess the level of digital premium as a risk factor in multi-factor pricing models, by using the corporate digital maturity index developed by L. Blixen-Fineke and his colleagues, and E.I. Gibadullin, based on data from Swedish and Russian issuers, respectively. Methods. The main research method is the analysis of financial and non-financial statements of 26 Swedish public companies and 51 domestic issuers from 11 sectors of the Russian economy. Results. The analysis revealed statistical significance of digital premium factor in the domestic stock market, as well as an incipient trend of excess of historical return of the investment portfolio of shares of companies with a high level of digital maturity over the portfolio of shares of companies with a low level of digital maturity. These results enable to modify current multi-factor asset pricing models to improve their explanatory power. Conclusions. The revealed empirical evidence of the existence of price anomaly in the form of digital premium creates the necessary evidence base for the development of research aimed at the evolution of multi-factor asset pricing models in the domestic stock market. The findings create a foundation for the development of factor-based investing on the Russian stock market, based on selection of company shares for the investment portfolio, considering the level of digital development of companies.

Factor Sufficiency in Asset Pricing: An Application for the Brazilian Market

The multifactor asset pricing model derived from the Fama–French approach is extensively used in asset risk premium estimation procedures. Even including a considerable number of factors, it is still possible that omitted factors affect the estimation of this model. In this work, we compare estimators robust to the presence of omitted factors in estimating the risk premium in the Brazilian market. Initially, we analyze the panel of asset returns using the mean group and common correlated effect estimators to detect the presence of omitted factors. We then compare the results with those obtained by a estimator robust to omitted variables, which uses a principal components approach to correct the estimation in the case of the omission of latent factors. We conclude that there is evidence of omitted factors, and the best predictor for the expect returns is the common correlated effects estimator.

PREDICTING PAKISTANS’ MUTUAL FUND PERFORMANCE: AN EVALUATION OF TRADITIONAL AND MODERN MEASURES

Mutual Funds enable small investors to benefit from the capital market with minimal investments, thanks to the expertise of professional managers. This study focuses on the Pakistani Mutual Fund industry and evaluates the suitability of traditional and multifactor asset pricing models in assessing Mutual Fund performance. Since multifactor models are rarely used in Pakistani research, this study employs the CAPM, Fama French, and Carhart models to assess the performance of Pakistan Mutual Funds. Data from 100 open-end Mutual Funds between 2005 and 2017 was collected from the Mutual Fund Association of Pakistan, while risk-free rates data was gathered from the State Bank of Pakistan and stock data from the Pakistan Stock Exchange. Ratio analysis, CAPM, Fama French-3 Factor, and Carhart-4 Factor models were used to analyze the data and determine which model was most suitable. The results indicate that the CAPM affects the market factors of the majority of portfolios. Conversely, the size factor, value factor, and momentum factor in the Fama French and Carhart models have an insignificant effect on the majority of portfolios. The Gibbon Rose Shanken test reveals that the CAPM model is the most suitable for evaluating Mutual Fund performance in Pakistan. The findings of this study have implications for asset management company managers and investors alike. It provides valuable insights into which funds perform better and which types of funds are ideal for investment.

Is downside risk priced in cryptocurrency market?

This paper explores the cryptocurrency market through the prism of traditional multifactor asset-pricing models with particular attention to the downside market risk. The analysis for about 2000 cryptocurrencies reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. This finding is irrespective of whether we use cryptocurrency market or equity market as the benchmark. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor Liu, Tsyvinski, and Wu (2022) model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important component in the multifactor cryptocurrency pricing model. The result is robust in sub-samples of cryptocurrencies with different market capitalizations.

LGBT CEOs and stock returns: Diagnosing rainbow ceilings and cliffs

This study is the first to investigate the implications of lesbian, gay, bisexual, and transgender chief executive officers (LGBT CEOs) for stock performance, using an exhaustive sample of 26 LGBT publicly listed company CEOs since 2000. Firms with openly LGBT CEOs generate a statistically and economically significant monthly alpha of 0.69%− 1.08%, robust to portfolio weighting schemes, estimation frequency, multi-factor asset-pricing models, multicollinearity, sectoral allocation effects, and in subsamples in the United States and globally. The results imply that a “rainbow ceiling” exists with regards to LGBT executives, and that companies they lead are persistently undervalued due to discrimination by investors. LGBT CEOs on average represent small growth stocks with poor past performance, supporting the “rainbow cliff” hypothesis that the LGBT community is overrepresented in precarious leadership positions. Portfolios formed of stocks with LGBT CEOs robustly outperform broad market indices, evidencing attractiveness for ethical investment from both selfish and social perspectives.

Asymmetric and Lag Effects of Industry Risk Factors on the Malaysian Oil and Gas Stocks

This study examines asymmetric and the lag effects of oil price, gas price, and exchange rate on stock performance of the Malaysian oil and gas sub-industries. Using company-level data and multi factor asset pricing models, this study found that oil price, gas price, exchange rate, and common systematic risk factors such as market, size, and book-to-market have significant effects on the stock performance of Malaysian oil and gas sub-industries, but these exposures remain heterogeneous. Oil price and exchange rate show strong asymmetrical impacts on stock performance when decomposed into oil price increases (decreases) and exchange rate depreciation (appreciation). Increases in oil price and exchange rate depreciation have a greater impact on stock performance of oil and gas industry than decreases in oil price and exchange rate appreciation. Oil price, gas price, and exchange rate exhibit lag effects on the performance of oil and gas stocks. The findings have important asset pricing implications for profitable investment and portfolio strategies based on oil and gas risk factors for oil-exporting country.JEL Classifications: C13; C58; G10; G12; G14; Q4

Power enhancement for testing multi-factor asset pricing models via Fisher’s method

Testing multi-factor asset pricing models is instrumental for asset pricing theory and practice. However, due to the accumulation of errors in estimating high-dimensional parameters, traditional quadratic-form tests such as the Wald test perform poorly against the sparse alternative hypothesis, i.e., a few mispriced assets. Fan et al. (2015b) introduced a powerful testing procedure by adding a power enhancement component to the Wald test statistic and proved power enhancement properties. To provide an alternative to their methodology, we first instantiate the power enhancement component by introducing a new maximum-form test statistic and then study the asymptotic joint distribution of the Wald test statistic and the maximum test statistic. We prove that these two test statistics are asymptotically independent. Given their asymptotic independence, we propose a new power-enhanced testing procedure to combine their respective power based on Fisher’s method (Fisher, 1925). Theoretically, we prove that the new power-enhanced test retains the desired nominal significance level and achieves asymptotically consistent power against more general alternatives. Furthermore, we demonstrate the finite-sample performance of our proposed power-enhanced test in both simulation studies and an empirical study of testing market efficiency using asset returns of the Russel-2000 portfolio.

Modeling the Time Variation in Factor Exposures

Abstract This paper offers new evidence on the dynamic behavior of multifactor models. Specifically, we investigate the significance and temporal stability of conditional factor betas in the context of multifactor asset pricing models. Using a Kalman filter approach, we find that conditional factor betas are dynamic and their statistical significance varies over time. Furthermore, the inclusion of more factors improves that statistical significance and time stability of the market factor. Overall, our empirical results support the view that multifactors may not be independent risk factors but help to better identify the market factor. JEL classification numbers: G11; G12. Keywords: Asset Pricing, Risk Factors.

News-based economic policy uncertainty and financial contagion: An international evidence

In this paper, we examine the relative importance of economic policy uncertainty as a potential source intensifying market integration/contagion for domestic stock markets of 31 countries. Our data spans the period June 2001-October 2021 that encompasses several episodes of economic and financial turmoil. We use the multifactor asset pricing model of Bekaert et al. (2005) with a two-factor setting which are the U.S. market return and the regional equity portfolio return to reflect the local, regional and global risk sources. Our findings show that economic policy uncertainty has an important role in intensifying spillover effects among regional European markets. Results also show an increased interdependency between used Asia Pacific and Latin American markets and the U.S. market during periods of high U.S. and global uncertainties induced by the increased portion of return shock variance explained by the U.S. factor. Considering contagion, we fail to provide evidence of additional unconditional correlation of returns with the United States and the region during these same periods of time, which suggests that the influence is rather induced by fundamentals. The implications of this study will be of interest to both practitioners and market regulators to better adopt proactive awareness regarding stock market behavior.

Validity of the Fama-French Three- and Five-Factor Models in Crisis Settings at the Example of Select Energy-Sector Companies during the COVID-19 Pandemic

This study empirically analyzes return data from select energy companies in developed and emerging markets using the Fama-French three- and five-factor asset-pricing models in crisis settings. It researches whether these models are suitable to produce meaningful return data in challenging economic circumstances. We use panel data covering 12 of the largest globally-operating energy companies from Russia, China, the US, the EU, and Saudi Arabia, covering a period between 2000 and 2022. The results undermine the general notion that the usage of available multi-factor asset-pricing models automatically yields meaningful data in all economic situations. The study reiterates the need to reconsider the assumption that the addition of more company-specific factors to regression models automatically yields better results. This study contributes to the existing literature by broadening this research area. It is the first study to specifically analyze the performance of companies from the energy sector in a crisis like the COVID-19 pandemic with the help of the Fama-French three- and five-factor models.

Comparison of Multifactor Asset Pricing Models in the South African Stock Market [2000–2016

The quest for parsimonious models has been a key objective in asset pricing. However, there appears to be no consensus on the most successful asset pricing strategy in the literature, especially for the South African Market. Using financial statements from January 2000 to December 2015, this article explores how market anomalies affect the performance of securities in the Johannesburg Stock Exchange’s (JSE’s) resources, industrial, and finance sectors. We investigated the efficacy of several asset pricing models and their capacity to account for market anomalies in the JSE’s resources, industrial, and financial sectors, as well as the applicability of the Fama and French five-factor model. The study used multiple regression techniques and applied stationarity and cointegration methods to ensure robust results. Results also suggest that when the FF5FM is implemented, there is statistical significance at the 10% level for the CMA in the resources sector as the value factor disappears. The FF5FM results in the industrial sector show a significance level of 5% in the SMB. The financial sector seems to have the majority of the style-based risk factors as the SMB is positively significant at a 5% level, the HML is significant at a 1% level, and the CMA is negatively significant at a 10% level of significance. The results suggest that the Carhart Four Factor model is the best to use in all market conditions. Results also show that value becomes redundant in a bullish market, but the opposite holds in a bearish market for a model with operating profitably and investing factors. These findings highlight the necessity for investors to determine which investment risk elements produce abnormal returns in both bearish and bullish market circumstances before investing.

Relationship of public farmland and timberland REITs with their private equity counterparts and selected asset classes

PurposeThe purpose of this study is to examine the return sensitivity of public farmland and timberland real estate investment trusts (REITs) to private-equity farmland, timberland and real estate, long-term corporate bonds and large- and small-cap stocks. The study also examines time-dependent contributions of selected asset classes to farmland and timberland REIT volatility.Design/methodology/approachThe authors use a multi-factor asset pricing model under a seemingly unrelated regression framework to evaluate farmland and timberland REIT returns, and a state-space model with the Kalman filter to evaluate the time-dependent contributors of farmland and timberland REIT volatility. The authors first perform orthogonalized regressions to obtain pure independent factors, and then decompose volatility into individual asset components.FindingsSignificant loadings on financial assets are found for both farmland and timberland REITs, suggesting that they are generally driven by some common state variables. Large-cap stocks are found to be the major contributor of farmland and timberland REIT volatility, despite some differing patterns over time.Originality/valueEmpirical analysis of farmland REIT is very scarce. The authors compare the risk-return characteristics of farmland and timberland REITs under a state-space framework with the Kalman filter. This study can improve the understanding of the roles of farmland and timberland REITs in a multi-asset portfolio.

Testing multi-factor asset pricing models in Borsa Istanbul

This study aims to test the validity of multi-factor asset pricing models on the portfolios of non-financial companies whose shares are traded on Borsa Istanbul and to identify the model with the best explanatory power. Accordingly, the relationships between annual book-to-market equity ratio, firm size, market portfolio return, return on capital, operating profitability, momentum and value-added intellectual coefficient between 2008-2019 were analyzed using panel data analysis. As a result of the analyses made, it has been observed that Fama French's three and five factors, Carhart (momentum), q-factor, and suggested models are successful in explaining the returns of portfolios formed by non-financial companies. Furthermore, according to the GRS-F test statistic, the q-factor model was found to have higher explanatory power than other models.