The predictive ability of stock market factors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Mohammed Elgammal ◽  
Fatma Ehab Ahmed ◽  
David Gordon McMillan
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Five Factor Model ◽  
Trading Strategy ◽  
Expected Returns ◽  
Time Varying ◽  
Content Type ◽  
Varying Coefficients ◽  
One Step ◽  
Market Factors

Purpose This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions. Design/methodology/approach Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns. Findings Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns. Research limitations/implications The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy. Practical implications The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement. Originality/value The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

scholarly journals The information content of US stock market factors

2020 ◽  
Vol 37 (2) ◽  
pp. 323-346
Author(s):  
Mohammed M. Elgammal ◽  
Fatma Ehab Ahmed ◽  
David G. McMillan
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Information Content ◽  
Predictive Power ◽  
Output Growth ◽  
Economic Conditions ◽  
Content Type ◽  
Economic Information ◽  
Market Factors ◽  
The Impact

Purpose The purpose of this paper is to consider the economic information content within several popular stock market factors and to the extent to which their movements are both explained by economic variables and can explain future output growth. Design/methodology/approach Using US stock portfolios from 1964 to 2019, the authors undertake three related exercises: whether a set of common factors contain independent predictive ability for stock returns, what economic and market variables explain movements in the factors and whether stock market factors have predictive power for future output growth. Findings The results show that several of the considered factors do not contain independent information for stock returns. Further, most of these factors are neither explained by economic conditions nor they provide any predictive power for future output growth. Thus, they appear to contain very little economic content. However, the results suggest that the impact of these factors is more prominent with higher macroeconomic risk (contractionary regime). Research limitations/implications The stock market factors are more likely to reflect existing market conditions and exhibit a weaker relation with economic conditions and do not act as a window on future behavior. Practical implications Fama and French three-factor model still have better explanations for stock returns and economic information more than any other models. Originality/value This paper contributes to the literature by examining whether a selection of factors provides unique information when modelling stock returns data. It also investigates what variables can predict movements in the stock market factors. Third, it examines whether the factors exhibit a link with subsequent economic output. This should establish whether the stock market factors contain useful information for stock returns and the macroeconomy or whether the significance of the factor is a result of chance. The results in this paper should advance our understanding of asset price movement and the links between the macroeconomy and financial markets and, thus, be of interest to academics, investors and policy-makers.

Investor sentiment, customer satisfaction and stock returns

2015 ◽  
Vol 49 (5/6) ◽  
pp. 827-850 ◽  
Author(s):  
Chi-Lu Peng ◽  
Kuan-Ling Lai ◽  
Maio-Ling Chen ◽  
An-Pin Wei
Keyword(s):  
Customer Satisfaction ◽  
Stock Market ◽  
Stock Returns ◽  
Abnormal Returns ◽  
Investor Sentiment ◽  
Intangible Asset ◽  
Time Varying ◽  
Emotional States ◽  
Content Type ◽  
Intangible Information

Purpose – This study aims to investigate whether and how different sentiments affect the stock market’s reaction to the American Customer Satisfaction Index (ACSI) information. Design/methodology/approach – The portfolio approach, with time-varying risk factor loadings and the asset-pricing models, is borrowed from the finance literature to investigate the ACSI-performance relationship. A direct sentiment index is used to examine how investors’ optimistic, neutral and pessimistic sentiments affect the aforementioned relation. Findings – This paper finds that customer satisfaction is a valuable intangible asset that generates positive abnormal returns. On average, investing in the Strong-ACSI Portfolio is superior to investing in the market index. Even when the stock market holds pessimistic beliefs, investors can beat the market by investing in firms that score well on customer satisfaction. The out-performance of our zero-cost, long–short ACSI strategy also confirms the mispricing of ACSI information in pessimistic periods. Research limitations/implications – Findings are limited to firms covered by the ACSI data. Practical implications – Finance research has further documented evidence of the stock market under-reacting to intangible information. For example, firms with higher research and development expenditures, advertising, patent citations and employee satisfaction all earn superior returns. Literature also proves that investors efficiently react to tangible information, whereas they undervalue intangible information. In summary, combining our results and those reported in the literature, customer satisfaction is value-relevant for both investors and firm management, particularly in pessimistic periods. Originality/value – This study is the first to investigate how sentiment affects the positive ACSI-performance relationship, while considering the time-varying property of risk factors. This study is also the first to show that ACSI plays a more important role during pessimistic periods. This study contributes to the growing literature on the marketing–finance interface by providing better understanding of how investor emotional states affect their perceptions and valuations of customer satisfaction.

New results on the predictive value of crude oil for US stock returns

2018 ◽  
Vol 35 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Matt Brigida
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
State Space ◽  
Risk Premia ◽  
Time Varying ◽  
Content Type ◽  
Varying Coefficients ◽  
The Us ◽  
The Relationship ◽  
Predictive Relationship

Purpose The purpose of this study is to clarify the nature of the predictive relationship between crude oil and the US stock market, with particular attention to whether this relationship is driven by time-varying risk premia. Design/methodology/approach The authors formulate the predictive regression as a state-space model and estimate the time-varying coefficients via the Kalman filter and prediction-error decomposition. Findings The authors find that the nature of the predictive relationship between crude oil and the US stock market changed in the latter half of 2008. After mid-2008, the predictive relationship switched signs and exhibited characteristics which make it much more likely that the predictive relationship is due to time-varying risk premia rather than a market inefficiency. Originality/value The authors apply a state-space approach to modeling the predictive relationship. This allows one to watch the evolution of the predictive relationship over time. In particular, the authors identify a dramatic shift in the relationship around August 2008. Prior research has not been able to identify shifts in the relationship.

Cross section of stock returns on Shari’ah-compliant stocks: evidence from Pakistan

2019 ◽  
Vol 12 (2) ◽  
pp. 282-302 ◽  
Author(s):  
Salman Ahmed Shaikh ◽  
Mohd Adib Ismail ◽  
Abdul Ghafar Ismail ◽  
Shahida Shahimi ◽  
Muhammad Hakimi Mohd. Shafiai
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Cross Section ◽  
Factor Model ◽  
Five Factor Model ◽  
Value Premium ◽  
Expected Returns ◽  
Content Type ◽  
The Cross ◽  
Three Factor

Purpose This paper aims to study the cross section of expected returns on Shari’ah-compliant stocks in Pakistan by using single- and multi-factor asset pricing models. Design/methodology/approach To estimate cross section of expected returns of Shari’ah-compliant stocks, the study uses capital asset pricing model (CAPM), Fama-French three-factor model and Fama-French five-factor model. Data for the period 2001-2015 on 217 companies are used. For the market portfolio, PSX-100 and Dow Jones Islamic Index for Pakistan are used. Findings The study could not find empirical support for CAPM using Lintner (1965), Black et al. (1972) and Fama and Macbeth (1973) approach. Nonetheless, the relation between beta and returns is positive in up-market and negative in down-market. The results of Fama-French three-factor and five-factor models suggest that size premium is positive and significant for explaining the cross section of stock returns of small size stocks, whereas value premium is positive and significant for explaining the cross section of returns of high value stocks. Practical implications The results suggest that fund managers can use Shari’ah-compliant stocks for portfolio diversification and for offering specialized investments given the positive market excess returns and the existence of size and value premium on Shari’ah-compliant stocks. Originality/value This is the first study on Fama-French (2015) five-factor model for Islamic capital markets in Pakistan.

scholarly journals Predicting stock market returns in the US: evidence from an average correlation approach

2020 ◽  
Vol 33 (2) ◽  
pp. 411-433
Author(s):  
Xiyang Li ◽  
Bin Li ◽  
Tarlok Singh ◽  
Kan Shi
Keyword(s):  
Stock Market ◽  
Business Cycles ◽  
Stock Returns ◽  
Expected Returns ◽  
Market Returns ◽  
Average Correlation ◽  
Stock Market Returns ◽  
Content Type ◽  
Portfolio Returns ◽  
The Usa

Purpose This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA. Design/methodology/approach This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s). Findings The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner. Research limitations/implications The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models. Practical implications Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession. Social implications The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets. Originality/value The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

A quantile regression approach to evaluate the relative impact of global and local factors on the MENA stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Slah Bahloul ◽  
Nawel Ben Amor
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Stock Markets ◽  
Financial Integration ◽  
Ordinary Least Squares ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Impact

PurposeThis paper investigates the relative importance of local macroeconomic and global factors in the explanation of twelve MENA (Middle East and North Africa) stock market returns across the different quantiles in order to determine their degree of international financial integration.Design/methodology/approachThe authors use both ordinary least squares and quantile regressions from January 2007 to January 2018. Quantile regression permits to know how the effects of explanatory variables vary across the different states of the market.FindingsThe results of this paper indicate that the impact of local macroeconomic and global factors differs across the quantiles and markets. Generally, there are wide ranges in degree of international integration and most of MENA stock markets appear to be weakly integrated. This reveals that the portfolio diversification within the stock markets in this region is still beneficial.Originality/valueThis paper is original for two reasons. First, it emphasizes, over a fairly long period, the impact of a large number of macroeconomic and global variables on the MENA stock market returns. Second, it examines if the relative effects of these factors on MENA stock returns vary or not across the market states and MENA countries.

Weather, investor psychology and stock returns: evidence from Fiji's stock market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janesh Sami
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Sunshine Duration ◽  
Market Returns ◽  
Modeling Framework ◽  
Stock Market Returns ◽  
Content Type ◽  
Asset Pricing Models ◽  
Weather Changes ◽  
Practical Implications

PurposeThis paper investigates whether weather affects stock market returns in Fiji's stock market.Design/methodology/approachThe author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.FindingsThe results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.Research limitations/implicationsIt is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).Practical implicationsInvestors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.Originality/valueThis is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

Internet gambling stock returns: empirical evidence from the UK

2017 ◽  
Vol 13 (1) ◽  
pp. 36-49
Author(s):  
Daniel Perez Liston
Keyword(s):  
Stock Returns ◽  
Multivariate Garch ◽  
Online Gambling ◽  
Future Research ◽  
Time Varying ◽  
Conditional Correlation ◽  
Socially Responsible ◽  
Content Type ◽  
Starting Point ◽  
The Uk

Purpose The purpose of this paper is to quantify beta for an online gambling portfolio in the UK and investigates whether it is time-varying. It also examines the dynamic correlations of the online gambling portfolio with both the market and socially responsible portfolios. In addition, this paper documents the effect of important UK gambling legislation on the betas and correlations of the online gambling portfolio. Design/methodology/approach This study uses static and time-varying models (e.g. rolling regressions, multivariate GARCH models) to estimate betas and correlations for a portfolio of UK online gambling stocks. Findings This study finds that beta for the online gambling portfolio is less than 1, indicative of defensiveness toward the market, a result that is consistent with prior literature for sin stocks. In addition, the conditional correlation between the market and online gambling portfolio is small when compared to the correlation of the market and socially responsible portfolios. Findings suggest that the adoption of the Gambling Act 2005 increases the conditional correlation between the market and online gambling portfolio and it also increases the conditional betas for the online gambling portfolio. Research limitations/implications This paper serves as a starting point for future research on online gambling stocks. Going forward, studies can focus on the financial performance or accounting performance of online gambling stocks. Originality/value This empirical investigation provides insight into the risk characteristics of publicly listed online gambling companies in the UK.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

scholarly journals ANALISIS PORTOFOLIO SEBAGAI DASAR PENGAMBILAN KEPUTUSAN INVESTASI SAHAM YANG TERDAFTAR DI BEI

IQTISHODUNA ◽  
2013 ◽  
Author(s):  
Sri Yati
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Analytical Method ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Portfolio Analysis ◽  
Rate Of Return ◽  
Expected Returns ◽  
Risk Level ◽  
Expected Return

This study aims to analyze rate of return and risk as the tools to form the portfolio analysis on 15 the most actives stocks listed in Indonesian Stock Exchange. Descriptive analytical method is used to describe the correlation between three variables: stock returns, expected returns of stock market, and beta in order to measure the risk of stocks to help the investors in making the investment decisions. The research materials are 15 the most actives stocks listed in Indonesian Stock Exchange during 2008-2009. The results show that PT. Astra International Tbk. has the highest average expected return of individual stock (Ri) of 308,3355685, while PT. Perusahaan Gas Negara Tbk. has the lowest of -477,0827847. The average expected return of stock market (Rm) is 0,00247163. PT. Astra International Tbk. has the highest systematic risk level of 20229,14205, while the lowest of -147,5793279 is PT. Kalbe Farma Tbk. Furthermore, the results also indicate that there are 9 stocks can be combined to form optimal portfolio because they have positive expected returns.

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