High Minus Low Research Articles

Portfolio ownership in the excess return model at Indonesian stock exchange

<p>This research is to analyze and answer the phenomena of Foreign Portfolio Investment (FK), Global Risk (Rwf), Small minus Big (SmB), High minus Low (HmL), Win minus Lose (WmL) as determinants of Foreign Ownership (FO) which have implications for Excess Return (ER). The formula is to maximize the value of Excess Return through Portfolio Ownership, the object of research for companies on the Indonesia Stock Exchange. First results; an increase in FK can explain the impact on an increase in FO, these results confirm the prevailing theory. Other results are not like the existing theory, namely a decrease in SmB can explain the impact on a decrease in FO, while Rwf, HmL, and WmL cannot explain its effect on FO. Second result; the increase in HmL, WmL and FO can explain the impact of the increase in ER. Another thing FO can also explain its function as an intervening variable. Rwf can also explain the impact but it is not in line with the negative correlation theory. The FK has no impact on ER. These results are expected to help as a guide for foreign investors in the Indonesia Stock Exchange to obtain maximum excess returns.</p>

Using the Capital Asset Pricing Model and the Fama–French Three-Factor and Five-Factor Models to Manage Stock and Bond Portfolios: Evidence from Timor-Leste

Timor-Leste is a new country still in the process of economic development and does not yet have a capital market for stock and bond investments. These two asset classes have been invested in international capital markets such as the US, the UK, Japan, and Europe. We examine the performance of the capital asset pricing model (CAPM) and the Fama–French three-factor and five-factor models on the excess returns of Timor-Leste’s equity and bond investments in the international market over the period 2006 to 2019. Our empirical results show that the market factor (MKT) is positively and significantly associated with the excess returns of the CAPM and the Fama–French three-factor and five-factor models. Moreover, the two variables Small Minus Big (SMB) as a size factor and High Minus Low (HML) as a value factor have a negative and significant effect on the excess returns in the Fama–French three-factor model and five-factor model. Further analysis revealed that the explanatory power of the Fama–French five-factor model is that the Robust Minus Weak (RMW) factor as a profitability factor is positively and significantly associated with excess returns, while the Conservative Minus Aggressive (CMA) factor as an investment factor is insignificant.

How do climate risk and clean energy spillovers, and uncertainty affect U.S. stock markets?

In this study, we attempt to revisit how dependent the US stock market returns are on climate change related risks (CCRR). In this regard, we use a spillover and connectedness network analysis to assess the strength of the causal effect and transmission pathway of CCRR proxies (green index, carbon price, general and climate uncertainty) on US Small Minus Big (SMB) and High Minus Low (HML) factors. Statically, our findings reveal that the indexes of the clean energy and new energy innovation industry (green companies) and climate policy uncertainty are drivers of the transmission spillover network, especially in extreme market scenarios. Dynamically, CCRR proxies act as net contributors and/or net receipts of shock spillovers over time and across the quantiles of the joint distribution. We further find that the spillover connectedness network is very sensitive to market states and that the strength of the effects of CCRR proxies are more pronounced under bust and boom markets. Our findings are beneficial for environmental investors and policymakers responsible for portfolio diversification strategies.

Market sentiments and firm-level equity returns: panel evidence of Malaysia

This study focuses on the impact of market sentiment on firm-level equity returns in Malaysia by hypothesising that market sentiment is a relevant risk factor. Understanding how the market sentiment reflects the equity return is crucial to market participants managing their portfolio investment risks. In modelling for firm-level equity return determinants using augmented Fama and French (1992, 1996) three-factor model, this study used data from a sample of 608 publicly listed firms for 2010–2019 and the dynamic panel GMM estimation technique. The findings revealed that market sentiment indices, namely Business Conditions Index (BCI) and Consumer Sentiments Index (CSI), strongly and positively influenced firms equity returns. Excellent market sentiment encouraged a bullish strategy, increasing share prices and, consequently, stock returns. In addition to market sentiment, other related variables, namely domestic market returns, international market returns, small minus big (SMB), high minus low (HML), and firms’ liquidity ratio, are also found to be statistically significant in influencing firms equity returns. The policy implication provides a vital strategy to market participants, particularly fund managers and investors, to accordingly manage their risks and returns on their portfolio investment.

Intangibles: The Missing Ingredient in Book Value

The author closely examines the impact of adding intangibles to traditional book equity as a more meaningful value measure. This intangibles-adjusted value metric subsumes the traditional book-to-price metric in explaining cross-sectional equity returns and improves value factor performance across subsample periods and geographic regions. The author finds that knowledge capital (capitalized research and development expenditures) plays a more important role than organization capital (capitalized partial selling, general, and administrative expenditures). The improved value premium comes from both the long and short sides of intangibles-adjusted high-minus-low (HML), which is good news for investors under a long-only constraint and provides useful information for investors who choose to short or underweight certain names.

Is the Long Memory Factor Important for Extending the Fama and French Five-Factor Model: Evidence from China

This paper proposes a new factor model, which is built upon the marriage of the Fama and French five-factor model and a long memory factor based on the monthly data of the A-share market in the Chinese stock market from January 2010 to July 2020. We first examine the explanatory power of the Fama and French five-factor model. We find strong market factor return of market (RM), size factor small minus big (SMB), and value factor high minus low (HML) but weak factor robust minus weak (RMW) and investment factor conservative minus aggressive (CMA). Then, both the Hurst exponent and the momentum factors (MOM) are added to the model to test the improvement of the explanatory power of these two new factors. We find that both the momentum factor and the Hurst exponent factor can effectively improve the explanatory power of the model. The momentum factor captures the short-term trend, but it cannot completely replace the Hurst exponent, which reflects the long memory effect.

Research on the application of Fama-French 5-factor model in the steel industry during COVID-19

Based on the Fama-French 5-factor model (FF5), this paper analyzes the market changes of the American steel industry before and after the epidemic. In this paper, the Data from Kennethr French-Data Library were used to make multiple regression analysis of the U.S. steel industry before and after the epidemic. The Fama-French 5-factor model was also used for analysis. According to the results of multiple linear regression, due to the influence of COVID-19, the coefficient of market risk (Betam) decreased; Robust minus Week (RMW) changed from significant to insignificant; Small minus Big (SMB) and High minus Low (HML) were significant and the change of epidemic situation was not significant, while Conservative minus Aggressive (CMA) was not significant. The impact of the outbreak on the U.S. steel industry has been dramatic and has led to a huge decline in the entire industry.

Long Run Risks in FX Markets: Are They There?

This paper documents a tight connection between long run consumption risks (LRRs), currency excess returns and traditional global currency risk factors. We adopt a novel identification strategy that estimates country level LRRs using asset market data alone. With this identification strategy in hand, we find that: (1) currencies that suffer a bad relative LRR shock appreciate on impact before depreciating over the long run, (2) the High-Minus-Low (HML) carry trade sorts currencies on the basis of global LRR exposures, (3) the dollar carry trade outperforms on impact before underperforming over the long run in response to positive US relative LRR shocks, (4) US relative LRR shocks drive global currency risk factors. We interpret these facts as evidence in favour of an international LRR model where US LRRs drive global shocks to the world economy.

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.

Different in Nature, Common in Style: View Commonality of Single Hedge Funds and Funds of Hedge Funds

Using style analysis, the authors show the common factor exposures of single hedge funds and funds of hedge funds (FoHFs). Despite the different nature and characteristics of the two categories, there are significant style similarities through the different business cycles. The authors argue that in general, FoHFs use substantially more leverage than single hedge fund strategies and found a positive trade-off between leverage and style exposure for the former and a negative one for the latter. Furthermore, the style in both category changes according to the business cycle, with single hedge fund strategies having more flexibility in allocation and aggressive style drifts. However, the similarities in style exposure increase in times of financial turmoil. Additionally, the exposure in equity and bond-related factors are significant and identify style drifts from the past. Finally, the authors found that both categories engage most in equity markets related factors—Betting Against Beta factors (BAB), High minus Low (HML) portfolios sorted by book-to-market to indicate value, Small minus Big (SMB) portfolios sorted by market-cap to indicate size, and Conservative minus Aggressive (CMA) portfolios. <b>TOPICS:</b>Real assets/alternative investments/private equity, portfolio construction, factor-based models, performance measurement <b>Key Findings</b> • In spite of their different natures, single funds and FoHFs show striking style similarity with significant exposure to the equity markets. Their style is largely determined by dynamically shifting between long and short exposure to the factors HML, SMB, and BAB. • FoHFs have much stronger absolute factor exposure, which is partly determined by the large amounts of leverage used. • The similar style exposure spikes in times of financial stress. However, single strategies show larger advantages in exploiting business cycles, flexible factor allocation, and excess returns.

Revisiting Fama–French’s asset pricing model with an MCB volatility risk factor

PurposeSince the introduction of VIX to measure the spot volatility in the stock market, VIX and its futures have been widely considered to be the standard of underlying investor sentiment. This study aims to examine how the magnitude of contango or backwardation (MCB volatility risk factor) derived from VIX and VIX3M may affect the pricing of assets.Design/methodology/approachThis paper focuses on the statistical inference of three defined MCB risk factors when cross-examined with Fama–French’s five factors: the market factor Rm–Rf, the size factor SMB (small minus big), the value factor HML (high minus low B/M), the profitability factor RMW (robust minus weak) and the investing factor CMA (conservative minus aggressive). Robustness checks are performed with the revised HML-Dev factor, as well as with daily data sets.FindingsThe inclusions of the MCB volatility risk factor, either defined as a spread of monthly VIX3M/VIX and its monthly MA(20), or as a monthly net return of VIX3M/VIX, generally enhance the explanatory power of all factors in the Fama and French’s model, in particular the market factor Rm–Rf and the value factor HML, and the investing factor CMA also displays a significant and positive correlation with the MCB risk factor. When the more in-time adjusted HML-Dev factor, suggested by Asness (2014), replaces the original HML factor, results are generally better and more intuitive, with a higher R2 for the market factor and more explanatory power with HML-Dev.Originality/valueThis paper introduces the term structure of VIX to Fama–French’s asset pricing model. The MCB risk factor identifies underlying configurations of investor sentiment. The sensitivities to this timing indicator will significantly relate to returns across individual stocks or portfolios.

A Comparison of New Factor Models in the Korean Stock Market

AbstractWe compare the empirical performance of the Fama and French (2015) five‐factor model, the Hou et al. (2015) q‐factor model, and their variations in the Korean stock market. Among the models considered, we demonstrate that the adjusted five‐factor model, which includes the quarterly‐ rather than the yearly‐based profitability factor, best explains the size‐, value‐, investment‐, and profitability‐sorted portfolio returns. We also document supporting evidence that high‐minus‐low (HML) may not be a redundant factor in the existence of q‐factors. The adjusted five‐factor model outperforms the other factor models in digesting various anomalies in the Korean market.

Income Hedging, Dynamic Style Preferences, and Return Predictability

ABSTRACTWe propose a theoretical measure of income hedging demand and show that it affects asset prices. We focus on the value factor and first demonstrate that our demand estimates are correlated with the actual demands of retail and mutual fund investors. We then show that the aggregate high‐minus‐low (HML) demand predicts HML returns. Exploiting the state‐level variation in income risk, we demonstrate that state‐level hedging demands predict state‐level HML returns. A long‐short portfolio that exploits this hedging‐induced predictability earns an annualized risk‐adjusted return of 6%.

Evaluation of Long Term Performance for Initial Public Offerings (IPO) in Malaysia: A Fama-French Method

The objective of this study is to evaluate performance of initial public offerings for 16 sharia compliant companies listed on the Malaysia Stock Exchange. Data of stock price are collected from Thomson Reuters Datastream for 36 months starting from January 2016 until December 2018. The method implemented is using Fama-French approach that evaluates expected return with three factors namely Market Risk Premium (MRP), Small Minus Big (SMB) and High Minus Low (HML). Result shows the abnormal return for 36 months is -3.399 which indicates negative value. The negative value of y-intercept gives interpretation that market (FTSE Bursa Malaysia KLCI) performed better than portfolio. The findings of this study will help investors to understand the financial dynamic behavior in Malaysia Stock Exchange. In addition, it will help investors to select appropriate financial assets for investment portfolio in gaining better return.

Factor investing: get your exposures right!

This paper is devoted to the question of optimal portfolio construction for equity factor investing. The first part focuses on how to make sure that a given equity portfolio has the targeted factor exposures, even before imposing any constraints. We show that such portfolios can be derived from mean–variance optimization using expected stock returns as inputs, provided these are built in a robust way from information about the factors. We propose a framework to build those robust expected stock returns and show that the targeted factor exposures are retained by the portfolios both before and after applying realistic constraints, eg, long only. Other, more simplistic, approaches fail. The second part illustrates the application of the framework to a practical case, where the objectives are, first, to decide on the risk-budget allocation to factors in some pragmatic way and, second, to construct a long-only constrained portfolio that retains the targeted exposures to four factors from well-known asset-pricing equity models, namely high-minus-low (HML), robust-minus-weak (RMW), conservative-minus-aggressive (CMA) and momentum (MOM).

Factor Investing: Get Your Exposures Right!

This paper is devoted to the question of optimal portfolio construction for equity factor investing. The first part of the paper focusses on how to make sure that a given equity portfolio has the targeted factor exposures, even before imposing any constraints. We show that such portfolios can be derived from mean-variance optimization using stock expected returns as inputs provided these are built in a robust way from information about the factors. We propose a framework to build those robust stock expected returns and show that the targeted factor exposures are retained by the portfolios both before and after applying realistic constraints, e.g. long-only. Other more simplistic approaches fail. In the second part of the paper we illustrate the application of the framework to a practical case where the objectives are, first, to decide about the risk budget allocation to factors in some pragmatic way; and second, to construct a long-only constrained portfolio that retains the targeted exposures to four factors from well-known asset pricing equity models, namely High-minus-Low (HML), Robust-minus-Weak (RMW), Conservative-minus-Aggressive (CMA) and Momentum (MOM).

Size and value effects in high-tech industries: The role of R&D investment

We use monthly US stock data over 55 years from 1962 to 2017 to show that the R&D intensity at firms adds another important dimension to the size and value effects in describing stock returns, especially for small high-tech firms. A trading strategy that double sorts on R&D intensity and size or book-to-market ratio outperforms a simple small-minus-big (SMB) or high-minus-low (HML) strategy in producing higher and more significant portfolio returns. The most profitable schemes involve triple sorts by size, BM, and R&D intensity: the payoffs of buying high-BM/R&D-Active portfolio and selling low-BM/R&D-Inactive portfolio in the small-size/high-tech group and that of buying high-tech/high-BM and selling low-tech/low-BM in the small-size/R&D-active group generate a return of more than 2% on a monthly basis. Our results are robust to alternative classification method of assigning stocks in portfolios.

The Fama and French Five Factor Model: Evidence from an Emerging Market

This study tests the five-factor model that has recently developed by Fama and French (2015). We use daily data of 84 companies listed in Amman Stock Exchange (ASE) over the period (2011-2015). The results indicate that there is a statistically significant effect of the common risk factors, excess market return (Rm-Rf), small minus big (SMB), high minus low (HML), robust minus weak (RMW) and conservative minus aggressive (CMA) on the cross section of daily returns in ASE. However, the Fama and French five factor model fails to perfectly explain the cross section of stock returns in ASE over the study period. These results could be mainly justified by the fact that ASE is an emerging market in which many unexpected factors apart from fundamentals may interfere in affecting stock returns.

Fama–French in China: Size and Value Factors in Chinese Stock Returns

AbstractWe investigate the size and value factors in the cross‐section of returns for the Chinese stock market. We find a significant size effect but no robust value effect. A zero‐cost small‐minus‐big (SMB) portfolio earns an average premium of 0.61% per month, which is statistically significant with a t‐value of 2.89 and economically important. In contrast, neither the market portfolio nor the zero‐cost high‐minus‐low (HML) portfolio has average premiums that are statistically different from zero. In both time‐series regressions and Fama–MacBeth cross‐sectional tests, SMB represents the strongest factor in explaining the cross‐section of Chinese stock returns. Our results contradict several existing studies which document a value effect. We show that this difference comes from the extreme values in a few months in the early years of the market with a small number of stocks and high volatility. Their impact becomes insignificant with a longer sample and proper volatility adjustment.

Value Premium and Portfolio Return Regime: Evidence from European Equities

This paper examines the profitability of four European higher return stock portfolios and their linkages between the value premium factor return, high-minus-low (HML), developed by Fama and French. Our fundamental analyses and quantitative examinations using Markov switching models derive the following findings. First, in the four higher return stock portfolios in Europe, the smallest and the highest momentum portfolio shows the highest return. Second, the second smallest and the highest book-to-market (BM) portfolio, the second smallest and the highest operating profitability portfolio, and the second smallest and the second lowest investment portfolio for Europe also demonstrate higher excess returns than the overall stock market in Europe. Furthermore, we also clarify that for all the four European stock portfolios, there clearly exist two regimes: one is positively associated with the value premium factor return, HML, and the other is negatively associated with HML. We further reveal that recently, for all the four portfolios, the high value premium factor loading regimes shift to the other regimes that are uncorrelated with HML. This indicates that, in the recent periods, hedging and risk-diversification effects can be recognized in investing value stocks and the four higher return stock portfolios in Europe.