Five-factor Asset Pricing Model Research Articles

Can market risk explain the systemic risk? Evidence from the US banking industry

PurposeThis paper sheds light on the impact of market risk measures on systemic risk. Market risk, which is captured by the volatility of stock market returns, is also decomposed into systematic and idiosyncratic risks.Design/methodology/approachThe author uses the five-factor asset pricing model and systemic risk methodologies to derive market and systemic risk measures, respectively. Using a sample of 2,667 US banks for over 30 years and employing panel data estimation techniques, the author tests the said relationship.FindingsIt is shown that idiosyncratic risk can surge systemic risk, while systematic risk plays a less important role. Results survive a battery of tests, including different systemic risk measures, controlling causality and interacting with bank size, market fear and crisis periods.Practical implicationsThese findings call for regulatory intervention, especially for large banks with high idiosyncratic risk.Originality/valueThis is the first paper that provides a more granular picture of the relationship between market and systemic risk from the US banking industry for more than 30 years.

The five-factor model, stock returns and idiosyncratic volatility: evidence from Sri Lanka

Traditionally, the success of asset pricing models is assessed in the absence of idiosyncratic volatility, as it is believed that the role of idiosyncratic volatility is irrelevant. Nevertheless, the existing literature shows that idiosyncratic volatility matters in asset pricing decisions. Hence, this study aims to test the performance of the five-factor asset pricing model of Fama and French (2015) in the presence of idiosyncratic volatility. This study utilizes a sample of 214 companies listed on the Colombo Stock Exchange (CSE) except for those listed under the banks, finance, and insurance sectors over 163 months from September 2004 to March 2018. Nelson’s (1991) Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models are used to estimate the idiosyncratic volatility of individual stocks. The empirical findings of the study confirm that the five-factor asset pricing model of Fama and French (2015) is less successful and failed to explain the stock returns in the presence of unsystematic risk in the Sri Lankan context. This finding yields striking evidence of the success of the five-factor asset pricing model in the presence of idiosyncratic volatility while it casts doubts on the applicability of such factor models in estimating the cost of equity of firms in the real world. Although the impact of idiosyncratic volatility on stock returns is well-researched in the finance literature, there is little structured research on how idiosyncratic volatility affects the performance of asset pricing models in the Sri Lankan context. This study fills this gap by investigating the performance of the five-factor asset pricing model of Fama and French (2015) using the firms listed on the CSE. The research findings should help academia develop more pronounced asset pricing models while tackling the idiosyncratic volatility of stocks.

The five-factor asset pricing model, short-term reversal, and ownership structure – the case of China

We find that the five-factor asset pricing model proposed by Fama and French (2015) is a better description of the Chinese stock market return than the three-factor model, but it is not a complete one. We propose a short-term-reversal (STR) factor and show it is highly significant. The STR factor substantially improves the pricing ability of three- and five-factor asset pricing models in explaining popular stock portfolio returns as well as Chinese mutual funds' returns. We also propose two additional factors based on state ownership and institutional ownership which further strengthen the existing asset pricing models. Finally, our test findings suggest that 17.57% of the Chinese mutual funds follow a money-losing short-term momentum strategy and around 98% of them have zero or negative abnormal returns.

Are Fama–French Factors Relevant? A Sub-Sectorial Analysis

The purpose of the study is to empirically examine the sectoral-specific performance of the five-factor asset pricing model comprising of 17-years’ data in the Indian stock market using the Fama–French methodology. The results highlighted the better performance of a five-factor model in the “Basic Material” and “Oil” industries. However, for the “consumer” industry, there is an existence of other risk factors which can better explain the portfolio’s excess returns. The result further demonstrates the better explanatory power of the five-factor model in explaining the portfolio excess return for the “Industrial” sector. However, the findings support the better applicability of market mode for the “financial” sector in the Indian stock market. For the “Health Care” and “Technology” industries, the addition of two more risk factors does not lead to much improvement in the model’s explanatory power. The current study evaluating the applicability of the asset pricing model will have a practical implication for portfolio managers, policymakers, researchers, and academicians in evaluating the performance of the portfolios on a sectoral basis and in determining the cost of equity in the overall cost of capital. The study will also aid the investors in their investment decision-making by helping them to identify the average stock return in different sectors.

Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

Fama-French five-factor asset pricing model: empirical evidence from Indian stock market

Fama-French five-factor asset pricing model: empirical evidence from Indian stock market

Impact of Idiosyncratic Volatility on Average Stock Returns: Evidence from Sri Lanka

The complete diversification of idiosyncratic volatility is questionable due to factors such as market imperfections, investor irrationality and managerial decisions. Therefore, the purpose of this study is to investigate the impact of idiosyncratic volatility on average stock returns in the Sri Lankan context. Using the five-factor asset pricing model of Fama and French (2015) along with Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) estimated idiosyncratic volatility of stocks of firms listed on the Colombo Stock Exchange (CSE), except for firms in banks, finance and insurance sectors, this study reveals a positive and statistically significant association between average stock returns and idiosyncratic volatility for the sample period from September 2004 to March 2018. The empirical findings on firm profitability and investment yield striking evidence on idiosyncratic volatility of stocks from a frontier market perspective, while uncovering the importance of further research on the investor behaviour on asset pricing decisions.

Fama-French Five-Factor Asset Pricing Model: Testing Validity for Borsa Istanbul and German Stock Exchange

Purpose – Fama and French (2015, 2017) proposed to expand Fama and French (1993) three-factor (portfolio & the overall market factor, firm size, and book-to-market equity) model building two new factors (profitability and investment) on, resulting in a five-factor model. This paper investigates the validity of the five-factor asset-pricing model (FF5F) in BIST30 index in Borsa Istanbul and DAX30 Index in Germany Stock Exchange. Design/methodology/approach – Static panel data analysis is employed by the fixed-effects withingroup estimator for the period 2009Q2-2018Q4 with two different types of market indices BIST30 Index and DAX30 Index in order to investigate the validity of the five-factor asset-pricing model (FF5F). Findings – In overall, results demonstrate that there i s n ot e nough e vidence to s uggest t he robustness of the five-factor model may be valid for stocks listed in DAX30 Index but provide evidence for two-factor model. As for, stocks listed in BIST30, we find enough empirical evidence to suggest that the four-factor model holds rather than the five-factor model. Discussion – Due to the differentiation of developed markets in terms of higher returns and different market dynamics it is therefore of utmost importance to assess the applicability of the FF5F model for developing countries. The findings of the present paper will assist a better evaluation of the risk factors faced by firms and investors.

Fama-French five-factor asset pricing model: empirical evidence from Indian stock market

The present study examines the applicability of five-factor model of Fama-French (2015). This model considers five factors namely market, size, value, profitability and investment in explaining the cross-sectional variations, i.e., variations across different stock or portfolios in the equity returns in Indian stock markets using the monthly closing price data of a sample of 486 companies which are a part of CNX 500 index over the period from July 2000 to June 2015. The paper investigates whether including any or all of the additional four factors can better explain the cross-sectional variations than the single factor capital assets pricing model. The investment strategies based on size, value and profitability provided abnormal returns over the study period. By making use of the Davis et al. (2000) methodology, we find that Fama-French five-factor model based on market premium, size premium, value premium, profitability premium and investment premium explains the variations in the equity returns better than single factor capital assets pricing model but it does not increase the adjusted R2 significantly. The results have implications for asset pricing, market efficiency and investment strategies in the Indian stock market.

Valuation Beta: Addressing Inadequacies of Book to Price with Intrinsic Value, Stewardship and Leverage

A five-factor asset pricing model that incorporates direct measures of excess intrinsic value, financing yield, and leverage outperforms commonly studied models built solely upon book to price and dividend discount model frameworks. Excess intrinsic value captures firm value derived from discounted future economic profits net of the book equity reported on a firm’s balance sheet. Financing yield consolidates independent profitability and growth factors motivated from a dividend discount model framework into a single factor, untethering the incorrect assumption that predicts capital growth unconditionally leads to negative stock returns. A direct measure of leverage subsumes book to price over our study horizon. Studying the common ex-post horizon of the book to price factor and Applied Finance Intrinsic Value Factor provides compelling out-of-sample performance to support the theoretical motivation of a valuation-based approach to explain cross-sectional returns.

The Empirical Test of Fama-French Five-Factor Model: Evidence from Pakistan Stock Exchange

This study provides an empirical examination of the Fama and French five-factor asset pricing model (FF5FM) in the equity market of Pakistan. Using data from 2007 to 2017of non-financial firms listed on PSX. The univariate approach is used to construct the dependent portfolios based on four firms characteristics, while a 2x3 approach is used to construct size, value, profitability, and investment factors. Time series regression is used to analyze the data to obtained results. The empirical evidence demonstrates that FF5FM performs better the three-factor model in the Pakistan stock market and the performance of the four-factor model that drops investment factor is similar to FF5FM except for portfolios constructed based on investment factor.

Multifactor Assets Pricing Model: A Review Based Study

Generous consideration has been pursued to the empirical testing of multi factor assets pricing models. However, literature provides mixed kind of evidences in the support of multi factor assets pricing model. This study reviews 20 research articles based on multi factor assets pricing model and examines 25 research papers based on the empirically testing of multi factor assets pricing model published during 2001 and 2018 to study the multi factor assets pricing model in the Indian context as well as foreign context. CAPM is a popular normative model used by researchers to explain the relationship between risk and expected return of a risky asset which was developed by Sharpe (1964) and Lintner (1965). This model takes only one risk factor which is the excess market portfolio return (Market premium). Because of poor performance of CAPM in explaining realized returns, the Fama and French three factor asset pricing model (1993) was developed. Fama and French (1993) documented the size effect and the value effect that were not included in the CAPM, generally known as CAPM anomalies. Mark M. Carhart (1997) developed the Carhart four factor model. It is an extension of the FF three factor model with one another factor i.e. momentum factor effect for asset pricing of stocks. In view of the limitations of the earlier three-factor model, Fama and French five-factor asset pricing model (2014) was developed. Fama and French (2014) came with profitability pattern and investment pattern in average stock return along with the market premium, size premium and value premium. This paper may be an expedient source of information to the academics, financial analyst and researchers to understand the asset pricing model.

Testing Liquidity Augmented Fama-French Five-Factor Model in Pakistan Stock Exchange

This study examines the empirical evaluation of the six-factor asset pricing model that augments the Fama-French (2015) five-factor asset pricing model with liquidity factor. Using data from July 2010 to June 2017 of non-financial firms listed on PSX, a 2x3 sort approach is used to construct six left-hand side portfolios, and a 2x2 approach is used to construct right-hand side factors namely, SMB, HML, RMW, CMA, and IML. Time series regression is used to analyze the data to obtained results. The empirical evidence illustrates that in PSX the six-factor model has efficient and better outcomes. Furthermore, the liquidity factor has a strong role in improving the performance of the asset pricing model.

Analysis of the five-factor asset pricing model with wavelet multiscaling approach

We study the relationship between average returns and risk factors through wavelet multiscaling approach which enables us to investigate the risk-return relationship based on different time scales. The data for the period July 1963–February 2018 are gathered from the Kenneth French website. Each time series in the dataset is decomposed into five time scales. In order to make a comparison, the five-factor model is estimated based on both the scale basis and raw data. There are several key implications from our estimation results: i) The effects of risk factors on average returns vary over the time scales by their coefficient magnitudes and statistical significance. ii) Gibbons, Ross, and Shanken (1989) test results show that the intercepts of scale basis models are close to zero. iii) There is a period of unexpectedly higher cash flow for big value portfolios for long-term investments. iv) There is a minimum (maximum) risk level for aggressive (conservative) portfolios at different time horizons. Finally, we identify the risk factors in our five-factor model that have a significant effect on returns, and our model can capture the variations in average returns for different investment horizons.

Turkish currency crisis – Spillover effects on European banks

We analyze stock price reactions of the largest listed banks across 9 countries in Europe to the currency crisis in Turkey, on August 10, 2018. We find a statistically significant two-day cumulative abnormal return of −2.0% for the European banks, excluding banks of Turkey, using the Fama-French five-factor asset pricing model. We identify more severe stock responses in banks that have a recent increase in leverage or a decrease in liquidity or profitability. Results indicate that banks of Turkey, Greece, Netherlands, Italy, Spain, Germany and France are significantly influenced by possible loan defaults. German, French and Dutch banks could be vulnerable to the extent that their economies will support the weaker EU banks. UK and Switzerland should be immune to these pressures and therefore their banks are found relatively stable.

Dynamic Shrinkage Processes

SummaryWe propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute-by-minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five-factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods.

Weighing asset pricing factors: a least squares model averaging approach

Empirical evidence has demonstrated that certain factors in asset pricing models are more important than others for explaining specific portfolio returns. We propose a technique that evaluates the factors included in popular linear asset pricing models. Our method has the advantage of simultaneously ranking the relative importance of those pricing factors through comparing their model weights. As an empirical verification, we apply our method to portfolios formed following Fama and French [A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22] and demonstrate that models accommodated to our factor rankings do improve their explanatory power in both in-sample and out-of-sample analyses.

How many factors are important in U.K. stock returns?

ABSTRACTI use the sequential approach of Harvey and Liu ([2018]. Lucky factors (Working Paper). Duke University) to build linear factor models in U.K. stock returns among a set of 13 candidate factors using individual stocks and three groups of test portfolios between July 1983 and December 2017. My study finds that the Market factor is the dominant factor in reducing mispricing in individual stocks and test portfolios regardless of the pricing error metric used. The Market factor has a bigger impact when using a value weighting pricing error metric. Whether a second factor is used or not depends upon which metric is used for mispricing and the time period examined. My study finds support for a two-factor model for the whole sample period of the Market factor and the Conservative Minus Aggressive (CMA) factor of Fama and French ([2015]. “A five-factor asset pricing model.” Journal of Financial Economics 116: 1–22) when giving greater weight to the mispricing of larger companies.

Practical Applications of Five Concerns with the Five-Factor Model

Practical Applications Summary In Five Concerns with the Five-Factor Model, from the 2018 Quantitative Special Issue of the Journal of Portfolio Management, David Blitz, Matthias X. Hanauer, Milan Vidojevic, and Pim van Vliet (all of Robeco Asset Management) argue that Fama and French’s new five-factor asset pricing model is not a satisfactory improvement on the three-factor model, due to significant shortcomings that have not been sufficiently addressed. The authors list five concerns with the five-factor model: (1) the retainment of the CAPM relation between market beta and return as a starting point, (2) the absence of the momentum premium, (3) the robustness of the two new factors, (4) the economic rationale of the model, and (5) the inadequacy of the model to conclusively answer ongoing asset pricing discussions. The authors also briefly discuss the implicatio.s of(their findings and offer several questions for researchers to explore. TOPIC:Factor-based models

Is Human Capital the Sixth Factor? Evidence from US Data

Problem/Relevance: Measuring the risk of an asset and the economic forces driving the price of the risk is a challengingtask that preoccupied the asset pricing literature for decades. However, there exists no consensus on the integrated asset pricing framework among the financial economists in the contemporaneous asset pricing literature. Thus, we consider and study this research problem that has greater relevance in pricing the risks of an asset. In this backdrop, we develop an integrated equilibrium asset pricing model in an intertemporal (ICAPM) framework. Research Objective/Questions: Broadly we have two research objectives. First, we examine the joint dynamics of the human capital component and common factors in approximating the variation in asset return predictability. Second, we test whether the human capital component is the unaccounted and the sixth pricing factor of FF five-factor asset pricing model. Additionally, we assess the economic and statistical significance of the equilibrium six-factor asset pricing model. Methodology: The human capital component, market portfolio, size, value, profitability, and investment are the pricing factors of the equilibrium six-factor asset pricing model. We use Fama-French (FF) portfolios of 2  3, 5  5, 10  10 sorts, 2  4  4 sorts, and the Industry portfolios to examine the equilibrium six-factor asset pricing model. The Generalized method of moments (GMM) estimation is used to estimate the parameters of variant asset pricing models and Gibbons-Ross-Shanken test is employed to evaluate the performance of the variant asset pricing frameworks. Major Findings: Our approaches led to three conclusions. First, the GMM estimation result infers that the human capital component of the six-factor asset pricing model significantly priced the variation in excess return on FF portfolios of variant sorts and the Industry portfolios. Further, the sensitivity to human capital component priced separately in the presence of the market portfolios and the common factors. Second, the six-factor asset pricing model outperforms the CAPM, FF three-factor model, and FF five-factor model, which indicates that the human capital component is a significant pricing factor in asset return predictability. Third, we argue that the human capital component is the unaccounted asset pricing factor and equally the sixth-factor of the FF five-factor asset pricing model. The additional robustness test result confirms that the parameter estimation of the six-factor asset pricing model is robust to the alternative definitions of the human capital component. Implications: The empirical results and findings equally pose the more significant effects for the decision-making process of the rational investor, institutional managers, portfolio managers, and fund managers in formulating the better investment strategies, which can help in diversifying the aggregate risks.