French Five-factor Model Research Articles

The effects of Fama-French five factor and momentum factor on Islamic stock portfolio excess return listed in ISSI

Purpose – The purpose of this study is to examine the effect of Fama-French five-factor and momentum factor on Islamic stock portfolio excess returns listed in the Indonesia Sharia Stock Index (ISSI). Methodology – This study used return data from ISSI group, starting from January 2013 to December 2017, which are then formed into time series data with excess monthly stock portfolio. This study adapted the Fama and French (2015) methodology using 2x3 and 2x2 to form the portfolio and applied Ordinary Least Square (OLS) with monthly data frequency to test the relevance of the model to the expected stock return of 183 companies. Findings – The results showed that the risk premium, the book-to-market ratio which is proxied by High Minus Low (HML), the investment that is proxied by Conservative Minus Aggressive (CMA), and the momentum which is proxied by Up Minus Down (UMD) has a positive effect on the excess return of the company's stock portfolio registered in Indonesia Sharia Stock Index (ISSI) during the period. While, the size and profitability variable do not affect the expected stock return. Research limitations – The results of this study provides relevant information about the relationship between risk and stock return using Fama and French five-factor model and momentum. However, future researchers can expand the scale of the research by adding research periods and using daily return research data. It is intended that the results are more representative of the actual market conditions at the moment. Originality – Researches on the factors that influence the selection of Islamic stock portfolios based on excess return using Fama-French five-factor including the momentum factor are still limited. This study contributes to the asset pricing development by investigating factors influencing performance of ISSI’s portfolio excess return using five-factor model and momentum 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.

Emerging market currency risk exposure: evidence from South Africa

PurposeThe purpose of this paper is to examine the level of foreign exchange exposure of listed nonfinancial firms in South Africa. The study spans the period January 2002 and November 2015. Foreign exchange risk exposure is estimated in relation to the exchange rate of the South African Rand relative to the US$, the Euro, the British Pound and the trade-weighted exchange rate index.Design/methodology/approachThe study is based on the augmented-market model of Jorion (1990). The Jorion (1990) is a capital asset pricing model-inspired framework which models share returns as a function of the return on the market index and changes in the exchange rate factor. The market risk factor is meant to discount the effect of macroeconomic factors on share returns, thus isolating the foreign exchange risk factor. In addition, the study further added the size, value, momentum, investment and profitability risk factors in line with the Fama–French three-factor model, Carhart four-factor model and the Fama–French five-factor model to account for the fact that equity capital markets in countries such as South Africa are known to be partially segmented.FindingsForeign exchange risk exposure levels were estimated at more than 40% for all the proxy currencies on the basis of the standard augmented market model. However, after controlling for idiosyncratic factors, through the application of the Fama–French three-factor model, the Carhart four-factor model and the Fama–French five-factor model, exposure levels were found to range between 6.5 and 12%.Research limitations/implicationsThese results indicate the importance of controlling for the effects of idiosyncratic facto0rs in the estimation of foreign exchange risk exposure in the context of emerging markets of Sub-Saharan Africa (SSA).Originality/valueThis is the first study to apply the Fama–French three-factor model, Carhart four-factor model and the Fama–French five-factor model in the estimation of foreign exchange exposure of nonfinancial firms in the context of a SSA country. These results indicate the importance of controlling for the effects of idiosyncratic factors in the estimation of foreign exchange risk exposure in the context of emerging markets.

Presenting a Model for Portfolio Risk Premium Assessment: Evidence from the Tehran Stock Exchange

This study aimed to present a model for portfolio risk premium assessment of companies listed in Tehran Stock Exchange. In order to achieve this purpose, monthly data of 150 companies listed in Tehran Stock Exchange during 2007-2017 was used. In this study, the predictive powers of FamaFrench three-factor model [11], Carhart four-factor model [1], Fama - French five-factor model [24], Brousseau five-factor model [18] and Roy and Shijin six-factor model [44] have been evaluated using variables used in the mentioned models and then an optimal model has been developed for portfolio risk assessment using stepwise regression. Findings showed that the Carhart four-factor model has higher predictive ability (48.3%) than other mentioned models in the Tehran Stock Exchange. According to the results of stepwise regression, seven variables have been selected as effective variables on portfolio risk premium. The explanatory power and predictive ability of the model developed in the Tehran Stock Exchange was 55.7% indicating higher predictive ability respect to previous models on portfolio risk premium. Investigation of the coefficients of the developed model showed that market risk premium, size factor, value factor, momentum factor and accounting quality factor have positive and significant effects on portfolio risk premium while investment factor and liquidity risk factor have significant negative impacts on portfolio risk premium.

A better stock pricing model: A systematic literature review

Background: The Fama and French five-factor model (FF5M) is one of the stock valuation model that is on the cutting edge of finance research. Results from the empirical tests from various stock markets were the FFM5 has been tested since its launch in 2014 are mixed. Hence, it is important that empirical evidence testing the FF5M in comparison with lower models such as the Fama and French three-factor model (FF3M) and the Capital Asset Pricing Model (CAPM) is documented. Aim: To identify a research gap that still remains to be filled relating to stock valuation models in the field of finance. Conclusion: The results of the empirical evidence have revealed that although the FF5M is a great milestone in stock pricing models, it has also left room for better models to be further developed from it in future.

MODELO DE CINCO-FATORES DE FAMA E FRENCH E O RISCO DE INCERTEZA ECONÔMICA NO MERCADO ACIONÁRIO BRASILEIRO

O objetivo deste artigo é propor e testar um modelo de avaliação da influência da incerteza em relação à política econômica sobre o mercado acionário brasileiro a partir do arcabouço teórico do Modelo de Cinco-Fatores de Fama e French (FF, 2015), com a inserção de fator adicional representativo do risco de incerteza, em duas versões: com base no Índice de Incerteza Econômica (IIE-Br/Fundação Getúlio Vargas) e no Economic Policy Uncertainty – EPU (Baker et al., 2016). O modelo proposto, em ambas versões, demonstrou maior poder explicativo dos retornos dos 32 portifólios avaliados em relação ao modelo FF (2015), no período e amostra analisados. Verificou-se que os coeficientes dos fatores de incerteza (EPU e IIE-Br) mostraram-se significativos a até 0,05 em cerca de 75% dos portifólios, indicando a possibilidade de uso dessa metodologia como mecanismo para melhor compreensão do efeitos da incerteza no mercado brasileiro.

Analysis of the Spanish IBEX-35 Companies’ Returns Using Extensions of the Fama and French Factor Models

This paper studies in depth the sensitivity of Spanish companies’ returns to changes in several risk factors between January 2000 and December 2018 using the quantile regression approach. Concretely, this research applies extensions of the Fama and French three- and five-factor models (1993 and 2015), according to González and Jareño (2019), adding relevant explanatory factors, such as nominal interest rates, the Carhart (1997) risk factor for momentum and for momentum reversal and the Pastor and Stambaugh (2003) traded liquidity factor. Additionally, for robustness, this paper splits the entire sample period into three sub-sample periods (pre-crisis, crisis and post-crisis) to analyse the results according to the economic cycle. The main conclusions of this paper are fourfold: First, these two models have the greatest explanatory power in the extreme quantiles of the return distribution (0.1 and 0.9) and more specifically in the lowest quantile 0.1. Second, the second model, based on the Fama and French five-factor model, shows the highest explanatory power not only in the full period but also in the three sub-periods. Third, the bank BBVA is the company that shows the highest sensitivity to changes in the explanatory factors in most periods because its adjusted R2 is the highest. Fourth, the stage of the economy with the highest explanatory power is the crisis subperiod. Thus, the final conclusion of this paper is that the second model explains best variations in Spanish companies’ returns in crisis stages and low quantiles.

Have the New Factor Models Improved on the Theory Front? Evidence from Decomposing Asset Growth

Contrary to the theoretical foundations of the asset-growth factor in both the Fama and French (2015) five-factor model and the q-factor model of Hou, Xue, and Zhang (2015), this study finds that investment level per se is not the reason asset growth is negatively associated with future stock returns. Instead, the source of the negative return predictability is the structure of the investments–specifically, the division of total investments between those that are carried on the balance sheet, versus those that are off the balance sheet, and is endogenously linked with uncertainty because accounting standards prohibit highly uncertain investments from appearing on the balance sheet. This study also sheds new light as to why these different types of investments are related to future stock returns in opposite directions: the fundamental difference is not a function of their tangible versus intangible nature; rather, it is their placement in the financial statements. As such, the opposite directionality represents two sides of the same coin.

The Effects of Environmental Regulation on the Singapore Stock Market

This study examines the impact of environmental regulation on the Singapore stock market using the event study methodology. Several asset pricing models are used to estimate sectoral abnormal returns. Additionally, we estimate the change in systematic risk after the introduction of the carbon tax and related regulation. We conduct various robustness tests, including the Corrado non-parametric ranking test, the Chesney non-parametric conditional distribution approach, a representation of market integration, and Fama–French five-factor model. We find evidence showing that the environmental regulations tend to achieve their desired effects in Singapore in which several big polluters (including industrial metals and mining, forestry and papers, and electrical equipment and services) were negatively affected by the announcements of environmental regulations and carbon tax. In addition, our results indicate that the electricity sector, one of the biggest polluters, was negatively affected by the announcement of environmental regulations and carbon tax. We also find that environmental regulations seem to boost the performance of environmentally-friendly sectors whereby we find the alternative energy industry (focusing on new renewable energy technologies) experienced a sizeable positive reaction following the announcements of these regulations.

Do Fama–French common risk-factor portfolio investors herd on a daily basis? Implications for common risk-factor regressions

Purpose The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe. Design/methodology/approach To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model. Findings The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion. Research limitations/implications Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios. Originality/value This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

Choosing factors: Australian evidence

Using a factor-pricing approach, this paper investigates the extent to which the factors in the Fama–French five-factor model, including momentum, explain Australian equity returns. A comparison of the United States and Australia suggests common components in asset returns. All the factors examined are useful in pricing Australian equities, whereas the HML factor is redundant for the United States. The findings suggest that the Fama–French five-factor model should be at least considered as a benchmark model in asset pricing research.

Which is the best: A comparison of asset pricing factor models in Chinese mutual fund industry

Factor models are commonly used in estimating risk-adjusted fund performance. We compare the commonly used factor models in empirical asset pricing studies and find that Fama and French (2015) five-factor model outperforms other models in the Chinese mutual fund industry and in most fund segments. The factor models we tested are more effective in explaining the return of index funds than other types. Meanwhile, we also find that the capital asset pricing model (CAPM) better controls the estimated alpha dispersion than other models. Though most multifactor models including Carhart (1997) have higher R-squared than CAPM, the cross-sectional differences between them are not statistically significant.

Factors influencing SRI fund performance

Purpose The purpose of this paper is to examine socially responsible investment (SRI) fund performance and investigate the factors influencing fund performance. Design/methodology/approach The study uses return data from the Morningstar database for 152 SRI funds from January 1995 to May 2015. The initial analysis includes the use of various risk-adjusted performance measures, including Sharpe ratio, Treynor ratio, Information ratio, Sortino ratio and M2. The study also uses four factor models, including Jensen single-factor model, Fama–French three-factor model, Carhart four-factor model and Fama–French five-factor model to explain SRI fund returns. Finally, a cross-sectional regression analysis is applied to investigate the determinants of SRI fund returns. Findings The results show that, on average, the SRI funds provide comparable risk-adjusted returns relative to various benchmark market indices. Market factor is significant in explaining SRI fund returns. Examining each factor model, the results do not support Fama–French’s three-factor model as neither size nor value factor is significant. The author finds weak support for Carhart’s momentum factor along with the market factor. Finally, the Fama–French five-factor model shows market, size and operating profit factors explain SRI fund returns. The study also finds the fund performance is stronger for funds with the higher turnover ratio, the larger fund size and more managerial experience and lower for funds with higher expense ratio. Also, funds formed with negative screening perform better than positive or mixed screened funds. Originality/value SRI funds have received considerable attention from investors. This study contributes to the literature by examining SRI fund performance and investigating factors influencing their performance using multiple factor models and cross-sectional regression analysis. The findings are relevant for investors who demand responsible investment opportunities without sacrificing returns for nonfinancial screenings. Findings also suggest that investors should consider fund characteristics when selecting SRI funds.

The Fama-French five-factor model: Evidence from the Johannesburg Stock Exchange

ABSTRACTThis study tests the effectiveness of the Fama and French (2015) five-factor model in explaining returns on the Johannesburg Securities Exchange (JSE). The five-factor model is compared to the traditional Fama-French three-factor model as well as other factor combinations. The results show that the size-value and size-profitability three-factor models best describe time-series returns when comparing models. The five-factor model best explains the cross-section of returns and, overall, the results identify a significant inverse size premium and negative relationship between beta and returns but find a significant value premium. The additional factors of profitability and investment contribute to explaining the returns on the JSE; however, profitability is more consistent than investment.

Explaining Equity Anomalies in Frontier Markets: A Horserace of Factor Pricing Models

ABSTRACT We are the first to compare the explanatory power of the major empirical asset pricing models over equity anomalies in the frontier markets. We replicate over 160 stock market anomalies in 23 frontier countries for years 1996–2017 and evaluate their performance with the factor models. The Carhart’s four-factor model outperforms both the recent Fama and French five-factor model and the q-model by Hou, Xue, and Zhan. Its superiority is driven by the ability to explain the momentum-related anomalies. Inclusion of additional profitability and investment factors lead to no further major improvement in the performance. Nonetheless, none of the models is able to fully explain the abnormal returns on all of the anomaly portfolios.

Do fundamental portfolios outperform in the MENA equity markets?

PurposeThis study aims to investigate the performance of fundamental weighted portfolios (using sales, cash flows, dividends, book values and a composite of all these variables), an equal weighted portfolio and a smoothed cap-weighted (CW) portfolio in Middle East and North Africa (MENA) markets. The performance of these portfolios is compared with that of a CW portfolio for the period 2005 to 2015.Design/methodology/approachThe portfolios are formed using different concentration levels, different construction schemes and different sub-regions. The performance is assessed using a large set of risk-adjusted performance measures, including more robust measures in the context of multi-factor models, such as the Fama and French (1993) three-factor model, the Fama and French (2015) five-factor model and a seven-factor model.FindingsThe results show that the fundamental portfolios, with the exception of the sales portfolio, underperform the CW portfolio using either the traditional or more robust risk-adjusted performance measures. The underperformance of the fundamental portfolios is found to be robust using different concentration levels, different construction schemes and different sub-regions. The results also show that the equal weighted portfolio outperforms the CW portfolio using traditional risk-adjusted measures. However, after controlling for additional risk factors, this outperformance disappears.Practical implicationsThe failure of fundamental indexation in the emerging markets could help the researchers and the academics to search for the best weighting method that could be used as an alternative to the CW indexation method.Originality/valueThe results of the study add evidence to the debatable propositions on the performance of fundamental portfolios in emerging markets. Furthermore, the findings may help domestic and international investors, practitioners and decision-makers to deepen their knowledge in terms of the best portfolio construction scheme in the MENA region.

Which model best explains the returns of large Australian stocks?

Equity markets outside the US are generally dominated by small-sized stocks that are outside the investable universe of institutional investors and professional money managers. In this paper, we compare the performance of a range of competing factor models in pricing large Australian stocks. By doing so, we shed light on the mixed findings in prior studies and the issue of national and international pricing of assets. Using a comprehensive sample spanning a period of 35 years, we document that the Fama and French (2015) five-factor model is superior despite a few close matches with some of the competing models. As the sample expands from the top 300 to the top 500 stocks, the superiority of the five-factor model becomes more apparent. There is also evidence that profitability and investment factors help to explain the cross-section of stock returns. Finally, although large Australian stocks are integrated with the US market, domestic factors are more important drivers of expected returns in Australia.

Return Predictability: Evidence from the US–China Supply Chain

In this article, by investigating return predictability across a transnational supply chain, the authors present new evidence supporting the hypothesis that value-relevant information diffuses gradually in financial markets because of limited attention from investors. They find that the corresponding trading strategy delivers superior abnormal return. Using a sample of supply chains between Chinese customers and US suppliers from 2009 to 2015 and corresponding financial and return data, the authors show that Chinese customer returns can predict US supplier future return at the firm level. A long–short portfolio strategy based on these findings yields significant abnormal monthly returns of 2.179% (equal-weighted portfolio) in the Fama–French five-factor model. The authors also employ Fama–MacBeth regression analysis and propensity score matching–matched sample analysis, and the conclusions continue to hold.

Time-Varying Coefficient Estimation in SURE Models. Application to Portfolio Management*

Abstract This paper provides a detailed analysis of the asymptotic properties of a kernel estimator for a seemingly unrelated regression equations model with time-varying coefficients (tv-SURE) under general conditions. Theoretical results together with a simulation study differentiate the cases for which the estimation of a tv-SURE outperforms the estimation of a single regression equations model with time-varying coefficients. The study shows that Zellner’s results cannot be straightforwardly extended to the time-varying case. The tv-SURE is applied to the Fama and French five-factor model using data from four different international markets. Finally, we provide the estimation under cross-restriction and discuss a testing procedure.

DO THE FAMA AND FRENCH FIVE-FACTOR MODEL FORECAST WELL USING ANN?

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.