Multifactor Asset Pricing Models Research Articles

A six-factor extension of the Fama-French asset pricing model – the case of the Polish stock market

Multifactor asset pricing models evolved at an accelerated pace in the past few years after the publication of the Fama and French five-factor model. Despite the results from developed markets which arguably make the sixth momentum factor redundant, the authors showed in this study that in an emerging market, e.g. the Warsaw Stock Exchange, the momentum factor (persistence of returns) is still a major asset pricing factor. The data covers the period 2010-2018 on a monthly granularity, during which the Polish stock market was still considered ‘emerging’.

Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model

This paper tests a multi-factor asset pricing model that does not assume that the return’s beta coefficients are constants. This is done by estimating the generalized arbitrage pricing theory (GAPT) using price differences. An implication of the GAPT is that when using price differences instead of returns, the beta coefficients are constant. We employ the adaptive multi-factor (AMF) model to test the GAPT utilizing a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded exchange-traded funds. We compare the performance of the AMF model with the Fama–French 5-factor (FF5) model. For nearly all time periods less than six years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as five years) is more consistent with realized asset returns than is the FF5 model.

A fuzzy multifactor asset pricing model.

This paper introduces a new approach of multifactor asset pricing model estimation. This approach assumes that the monthly returns of financial assets are fuzzy random variables and estimates the multifactor asset pricing model as a fuzzy linear model. The fuzzy random representations allows us to incorporate bias on prices induced by the market microstructure noise and to reflect the intra-period activity in the analysis. The application of fuzzy linear regression enables the uncertainty assessment in an alternative way to confidence interval or hypothesis testing, which is subjected the binding assumption of normal distribution of returns. However, it is well known that the distribution of many asset returns deviates significantly from the normal assumption. We illustrate this estimation in the particular case of the Fama and French’s (J Financ Econ 33:3–56, 1993) three factor model. Finally, empirical studies based on Fama and French’s portfolios and risk factors, historical dataset highlight the effectiveness of our estimation method and a comparative analysis with the ordinary least square estimation shows its ability to be applied for an optimal decision decision making in the financial market.

Graph-based multi-factor asset pricing model

We propose a latent multi-factor asset pricing model that estimates risk exposure based on firm characteristics and connectivity between assets. To handle connected high-dimensional characteristics, we adopted a graph convolutional network while estimating the connectivity between assets from the correlation of asset returns. Unlike recent literature involving the deep-learning-based latent factor model, we propose a forward stagewise additive factor modeling architecture that constructs latent factors sequentially to maintain the previous stage’s factors. Our empirical results on individual U.S. equities show that the proposed graph factor model outperforms other benchmark models in terms of explanatory power and the Sharpe ratio of the factor tangency portfolio.

Higher moments and US industry returns: realized skewness and kurtosis

PurposeThe purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies.Design/methodology/approachDaily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships.FindingsPast skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns.Research limitations/implicationsThe calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor.Practical implicationsGiven the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking.Originality/valueWhile there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

The time-varying effects of oil prices on oil\u2013gas stock returns of the fragile five countries

This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study. Moreover, the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market. The results further suggest that, except for Indonesia, oil prices have a positive impact on the sectoral returns of all markets, whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.

Multi-Factor asset pricing model in Iranian Capital Market

مدل هشت‌عاملی اسکاکیر و لانکرسکی (2018)، بسط یافته مدل پنج‌عاملی فاما و فرنچ (2016) است. این دو محقق با افزودن سه عامل ریسک نکول، نقدشوندگی و مومنتوم سعی در بهبود قدرت تبیین‌کنندگی مدل داشته و دنبال یافتن پاسخی برای برخی نابهنجاری‌های این مدل بوده‌اند. هدف این پژوهش بررسی کارایی عوامل یادشده در قالب مدل قیمت‌گذاری دارایی ها در بازار سرمایه ایران است. یافته‌ها نشان می‌دهندکه قدرت توضیح‌دهندگی مدل‌ها تا حد بسیار زیادی به نحوه گزینش عوامل بستگی دارد. کارایی مدل‌ها در سطح معنی‌داری مورد توجه، مورد تأیید نبوده است. لیکن نتایج درباره قدرت عوامل جدید و اثر آن‌ها بر ارتقای مدل پنج‌عاملی در ترکیبات مختلف نشان می‌دهد که در ترکیب تنها یکی از عوامل با مدل پنج‌عاملی، مومنتوم عملکرد بهتری داشته و ریسک نکول ضعیف‌ترین عملکرد را ارائه کرده‌است. در ترکیب دو عامل از میان عوامل، بررسی عرض از مبدأ نشان می‌دهد که ترکیب ریسک نقدشوندگی و مومنتوم باعث بهبود بیشتری شده‌است.

High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model

The paper proposes a new algorithm for the high-dimensional financial data — the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama–French 5-factor model.

High Dimensional Estimation and Multi-Factor Models

The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

Liquidity, time‐varying betas and anomalies: Is the high trading activity enhancing the validity of the CAPM in the UK equity market?

AbstractIn the last decades, a large number of multifactor assetpricing models have emerged with the aim of correcting the estimated equity risk premiums for some well‐documented market anomalies. In any case, recent research on asset pricing shows how the higher liquidity resulting from the globalization of financial markets has significantly reduced returns tied to many strategies based on market anomalies. In this framework, questions arise about the possible renovated validity of classic assetpricing models. On this basis, in this paper we study to what extent the capital assetpricing model (CAPM) has recovered its past explanatory power. Specifically, we propose a time‐varying beta CAPM in order to control for the variable nature of beta risk to changes in the market liquidity, using the variation of the Amihud illiquidity measure to account for the degree of trading activity. We test both the time‐varying and constant beta models on different sets of anomaly portfolios for the UK equity market, and we compare their performance to that of the Fama–French three‐ and five‐factor models. Additionally, we test the constant beta model on a set of actively managed portfolios, formed according to the variation in market illiquidity in the previous year. Our results show that the pricing errors of the CAPM have significantly decreased with respect to those of previous literature. Furthermore, the time‐varying beta model performs similarly to the Fama–French models in most cases. These results are consistent with increased trading activity that reduces arbitrage opportunities and, therefore, enhances market efficiency.

Idiosyncratic momentum on the JSE

ABSTRACT Idiosyncratic momentum, like price momentum, is a trading strategy that considers a share’s recent relative performance over the short to medium term. Idiosyncratic momentum differs from price momentum as it uses residual returns post-orthogonalization on a single or multi-factor asset pricing model. Recent literature has shown that idiosyncratic momentum consistently outperforms price momentum on a risk-adjusted basis, is less prone to long-term reversal and has been proven successful in regions that have previously shown to have a non-existent price momentum premium. Previous studies attribute the success of idiosyncratic momentum to ‘underreaction’, whereby market participants tend to underreact to idiosyncratic momentum signals. We attempt to determine whether idiosyncratic momentum displays the same positive attributes found in international literature. We find that idiosyncratic momentum is superior to price momentum in terms of performance and explanatory power. The results reject a risk-based explanation of idiosyncratic momentum as minimising factor exposure (by using residual returns) improves performance. However, we find limited evidence of underreaction driving idiosyncratic momentum. Notwithstanding the lack of an a priori exposition of idiosyncratic momentum’s existence, the results provide concrete evidence of idiosyncratic momentum’s superiority over price momentum on the JSE, a finding important for both practitioners and academics alike.

Downside Risk in Cryptocurrency Market

I look at the cryptocurrency market through the prism of standard multi-factor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins 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. 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 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 forth component in the multi-factor cryptocurrency pricing model.

Performance Evaluation of Pakistan's Mutual Fund Industry: Validating Fama French 3 And 5 Factor Model

In order to evaluate the performance of the mutual fund industry in various financial markets, a wide variety of researches have been conducted, which lead to different results. As the Pakistani mutual fund industry is much younger as compared to the US and UK fund industries and thus limited work has been done to evaluate the Pakistani mutual fund industry.Over the past few years, the industry has shown a phenomenal growth and it makes it worthwhile to study the performance of mutual funds. The aim of this research study is to validate the Fama French 3-Factor Model and Fama French 5-Factor Model. Also, this research attempts to test that which one of the included model performs better than the other so as to check their preferred suitability in measuring and evaluating the mutual fund performance in Pakistan. The monthly data of 323 open ended mutual funds for the period of 2008 to 2018 is analyzed. The GRS model validation test was applied, the results of the test found that the Fama French 5-Factor Model performed much better than the Fama French 3-Factor Model.This research contributes to the body of knowledge by providing academicians and practitioners more knowledge regarding multifactor asset pricing model so as to make better investment decisions.

Robust Inference in the Capital Asset Pricing Model Using the Multivariate t-distribution

In this paper, we consider asset pricing models under the multivariate t-distribution with finite second moment. Such a distribution, which contains the normal distribution, offers a more flexible framework for modeling asset returns. The main objective of this work is to develop statistical inference tools, such as parameter estimation and linear hypothesis tests in asset pricing models, with an emphasis on the Capital Asset Pricing Model (CAPM). An extension of the CAPM, the Multifactor Asset Pricing Model (MAPM), is also discussed. A simple algorithm to estimate the model parameters, including the kurtosis parameter, is implemented. Analytical expressions for the Score function and Fisher information matrix are provided. For linear hypothesis tests, the four most widely used tests (likelihood-ratio, Wald, score, and gradient statistics) are considered. In order to test the mean-variance efficiency, explicit expressions for these four statistical tests are also presented. The results are illustrated using two real data sets: the Chilean Stock Market data set and another from the New York Stock Exchange. The asset pricing model under the multivariate t-distribution presents a good fit, clearly better than the asset pricing model under the assumption of normality, in both data sets.

Common Fund Flows: Flow Hedging and Factor Pricing

Active mutual funds care about fund size, affected by common fund flows driven by primitive shocks. Funds hedge against common-flow shocks by tilting their portfolios toward low-flow-beta stocks. In equilibrium, common fund flows earn a risk premium, leading to a multi-factor asset-pricing model similar to the ICAPM, even with all agents behaving myopically and using naive asset-pricing models. Empirically, fund flows obey a strong factor structure with the common component earning a risk premium, and fund portfolios are, on average, tilted toward low-flow-beta stocks. This tilt increases in magnitude when flow-hedging motives strengthen following natural disasters and unexpected trade-war announcements.

Data Envelopment Analysis and Multifactor Asset Pricing Models

Recent literature shows that market anomalies have significantly diminished, while research on market factors has largely improved the performance of asset pricing models. In this paper we study the extent to which data envelopment analysis (DEA) techniques can help improve the performance of multifactor models. Specifically, we test the explanatory power of the Fama and French three-factor model, combined with an additional factor based on DEA, on a sample of 2101 European equity funds, for the period from 2001 to 2016. Accordingly, we first form the fund portfolios that constitute our test assets and create the efficiency factor. Secondly, we estimate the prices of risk tied to the four factors using ordinary least squares (OLS) on a two-stage cross-sectional regression. Finally, we use the R-squared statistic estimated by generalized least squares (GLS), as well as the Gibbons Ross and Shanken test and the J-test for overidentifying restrictions in order to study the performance of the model, including and omitting the efficiency factor. The results show that the efficiency factor improves the performance of the model and reduces the pricing errors of the assets under consideration, which allows us to conclude that the efficiency index may be used as a factor in asset pricing models.

Asset Pricing Through Downside Risk Based Arbitrage Pricing Theory: Empirical Evidence from Pakistan Stock Exchange

This study extends the downside risk applications in multifactor asset pricing model by incorporating the downside risk spillovers from economic and financial factors to stock returns. We amplify the conventional APT model by replacing the variance-based betas with semivariance based downside betas that better capture the risk volatilities in varying market conditions. The inclusion of downside risk betas based on semivariance and semideviation methods in the augmented asset pricing model improves both the theoretical and methodological applications relative to the limitations and restriction of conventional APT factors model. The mean-variance hypothesis replaced by meansemivariance hypothesis and asymmetric behaviour of stock returns distribution, empirically suggest the use of an alternative factors model. The models based on downside risk premia for asset pricing in emerging markets. The study tested the downside risk-return relationship based on the excess monthly stock returns of listed PSX firms and observed economic, financial and global factors representing spillover triangulation from 1997 to 2017. The findings of the study indicate that the augmented DR-APT model with pricing restrictions of unconditional linear factors method could not be deserted over the targeted period of study. The selected observed pricing factors except exports are significant enough for pricing the security returns in the augmented DR-APT Model. Findings of the panel regression, likelihood ratio tests and F-test corroborate DR-APT as a better model to price stock returns in volatile situations compare to conventional APT model. Our findings are consistent with the downside risk-return framework based on mean semi variance hypothesis and have implications for managers and decision markets that incorporate downside risk in asset valuation, cost of capital estimations, portfolio construction and investment analysis decisions. Key Words: Downside Risk, Semi variance, Semi covariance, Downside Beta, Downside risk-based Arbitrage Pricing Theory (DR-APT).

Multifactor capital asset pricing model in emerging and advanced markets using two error components model

Multifactor capital asset pricing model in emerging and advanced markets using two error components model

Multifactor Capital Asset Pricing Model in Emerging and Advanced Markets using Two Error Components Model

We aim to explore the macroeconomic factors that are able to influence the return of the stock market in emerging and advanced markets. To find such factors, we analyse the data from three emerging countries and three developed countries. The multifactor capital asset pricing model (CAPM), which includes the overall market return and the selected macroeconomic variables, is employed to achieve our study. However, the assumption of CAPM, which displayed only one error term, has been concerned in our analysis as one error term may fail to capture and define the whole erroneousness in the model. Thus, we prove the existence of the second error components of the CAPM. In addition, the concept of a seemingly unrelated regression (SUR) model is also applied to join the CAPM of each country. As a consequent, we propose a new SUR model which compounds two errors to investigate the multifactor CAPM.

Is Downside Risk Priced In Cryptocurrency Market?

I look at the cryptocurrency market through the prism of standard multifactor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins 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. 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 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 forth component in the multifactor cryptocurrency pricing model.