How Much Stock Return Predictability Can We Expect From an Asset Pricing Model?

2009 ◽  
Author(s):  
Guofu Zhou
Keyword(s):  
Asset Pricing ◽  
Stock Return ◽  
Return Predictability ◽  
Pricing Model ◽  
Stock Return Predictability ◽  
Asset Pricing Model

scholarly journals Is Human Capital the Sixth Factor? Evidence from US Data

2019 ◽  
Vol 8 (1) ◽  
pp. 21-55 ◽  
Author(s):  
Rahul Roy ◽  
Santhakumar Shijin
Keyword(s):  
Human Capital ◽  
Asset Pricing ◽  
Factor Model ◽  
Generalized Method Of Moments ◽  
Common Factors ◽  
Return Predictability ◽  
Pricing Model ◽  
Gmm Estimation ◽  
Asset Return ◽  
Asset Pricing Model

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.

scholarly journals PENGARUH HUMAN CAPITAL (LABOR INCOME) TERHADAP EXPECTED STOCK RETURNS

2018 ◽  
Vol 12 (1) ◽  
pp. 58-79
Author(s):  
Caecilia Atmini Susilandari
Keyword(s):  
Human Capital ◽  
Asset Pricing ◽  
Stock Returns ◽  
Stock Return ◽  
Idiosyncratic Risk ◽  
Labor Income ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Industry Level ◽  
Expected Stock Returns

This research intended to analyse the use of premium as the proxy of human capital (labor income) in the industry level as one of the factors to measure the expected stock returns other than market, smb, hml, umdand liquidity variable that can be applied in Indonesia.The analysis coveres the human capital (labor income) in the industry level to cross section of stock return and the effect of human capital (labor income) to idiosyncratic risk in the asset pricing model. It usesincome percapita to measure the premium variabel in the period of 2001 – 2011 and 30 stocks portfolio chosen based on the biggest market capitalization value in six sector in the period of 2001 – 2011

scholarly journals Perbandingan Keakuratan Metode Capital Asset Pricing Model dan Arbitrage Pricing Theory dalam Memprediksi Return Saham (Studi pada Perusahaan Sektor Barang Konsumsi dan Sektor Pertambangan yang Terdaftar di Indeks Saham Syariah Indonesia (ISSI) Peri

2018 ◽  
Vol 1 (2) ◽  
pp. 233-240
Author(s):  
Yetti Afrida Indra
Keyword(s):  
Asset Pricing ◽  
Stock Return ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Arbitrage Pricing Theory ◽  
Pricing Model ◽  
Capital Asset ◽  
Arbitrage Pricing ◽  
Asset Pricing Model ◽  
Pricing Theory

CAPM is a balance model that can determine the risks and returns that investors will gain. Under the CAPM, the level of risk and the appropriate rate of return has a positive and linear relationship. The measure of risk that is an indicator affecting stock in CAPM is indicated by the variable β (beta). The bigger the β of a stock, the greater the risk it contains. This model links the expectation return rate of a risky asset with the risk of the asset in a balanced market condition. The population in this study is the stock price data of companies in the consumption sector and the mining sector listed on the Indonesia Sharia Sharia Index (ISSI) period 2013-2016. Based on the results of research and statistical tests, a more accurate model in predicting future ISSI stock returns is more accurate than the Arbitrage Pricing Theory (APT) model, because MADCAPM (0.0835) value MADAPT (0,5070). Furthermore, based on data processing with MannWhitney test shows that H0 is rejected, in the sense that there is a significant difference of accuracy between Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) in predicting ISSI stock return. This is evidenced by the significance value (Sig) (0.002) smaller than (α) 0.05.Keywords: Comparison, Accuracy, Capital Asset Pricing Model (Capm), Arbitrage Pricing Theory (Apt), Stock Return.

scholarly journals THE RISK OF BEING UNSYSTEMATIC AND STOCK RETURNS: THE EMPIRICAL TEST ON CAPITAL VALIDITY OF CAPITAL ASSET PRICING MODEL

2014 ◽  
Vol 13 (2) ◽  
Author(s):  
Arfiana Rachel
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Stock Return ◽  
Ordinary Least Squares ◽  
Capital Asset Pricing Model ◽  
Series Data ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Asset Pricing Model

The objective of this research is to analyze the effect of idiosyncratic risk to stock return on Indonesia Stock Exchange. To test these variables, the study applied two pass regression with time series data of stock return LQ45 and stock price index from January 2014 - December 2014. The estimation method used in the first pass regression was selected by characteristics of the return data, that is EGARCH (1,1) method for heterokedasticity data and Ordinary Least Squares for constant variance data. Specifications on the second pass regression models using cross section data, that is month by month cross sectional regression of 30 stock portfolios, which aim to identify unsystematic risk role in explaining the behavior of the return from stock portfolio. The findings of this study indicate that unsystematic risk has insignificant effect on stock return. These findings support the statement postulated in Capital Asset Pricing Model (CAPM), that the only relevant risk in explaining the return of stock only systematic risk, so there is no statistical evidence is strong enough to declare that the unsystematic risk can play a role in explaining the movement of stock return.

VOLUME–VOLATILITY DYNAMICS IN AN INTERTEMPORAL ASSET PRICING MODEL

2000 ◽  
Vol 4 (4) ◽  
pp. 506-533 ◽  
Author(s):  
Chiente Hsu
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Stock Return ◽  
Dynamic Equilibrium ◽  
Empirical Studies ◽  
Simulated Data ◽  
Empirical Literature ◽  
Time Varying ◽  
Pricing Model ◽  
Asset Pricing Model

This paper embeds time-varying volatility into a dynamic equilibrium model of returns and trading. The model allows us to ask how time-varying volatility might affect the relation among return autocorrelation, volatility, and trading volume, as opposed to the pairwise relations that have been studied previously. It is shown analytically that, with time-varying volatility, the relationship between volume and stock return autocorrelation is ambiguous even if agents have symmetric information, which may explain the contradictory findings in the empirical literature. In the numerical exercise, the model is simulated in a way that mimics the persistent volatility of high-frequency stock data documented in numerous empirical studies. Specially, the time-varying volatility of stock returns is approximated with a highly persistent chaotic tent map, which is known to have the same autocorrelation coefficients as an AR(1) process. The simulated data can approximate GARCH-type behavior very well. Whereas in the simulated data, no significant relation between volume and return autocorrelation can be found, there is a significantly positive relation between volume and one-step-ahead stock return volatility. The ambiguous volume–persistence and positive volume–volatility relations are confirmed empirically by using four heavily traded individual stocks. Therefore, the data simulated from the highly stylized asset pricing model with deterministic time-varying volatility can mimic well the volume–return dynamics revealed in the observed data in these two respects.

scholarly journals An Analysis of the Relationship between Risk and Expected Return in Malaysia Stock Market: Test of the CAPM

2018 ◽  
Vol 7 (3.21) ◽  
pp. 161
Author(s):  
Koh Xin Rui ◽  
Devinaga Rasiah ◽  
Yuen Yee Yen ◽  
Suganthi Ramasamy ◽  
Shalini Devi Pillay
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Strong Evidence ◽  
Stock Return ◽  
Stock Exchange ◽  
Pricing Model ◽  
Expected Return ◽  
Risk And Return ◽  
Asset Pricing Model ◽  
Capm Model

Investment theory describes the concept of relationship between risk and return. Capital Model Asset Pricing Model (CAPM) was based on the risk and return relationship. CAPM described that asset’s expected return that is above the risk free rate is directly related to the non-diversifiable risk that is measure by beta. Focus of this study is to identify the impacts of risk toward the stock return in Malaysia stock market during the year 2007 to 2015 by testing on the applicability of Capital Asset Pricing Model. The data is from monthly stock returns from 24 companies listed on the stock exchange for investigation. The analysis of monthly stock market closing indexes from using regression model was carried out on the standard CAPM model. When testing the CAPM model for the whole period, it has not showed strong evidence that support the validity of this model and in order to get better estimates, this study divided the whole sample into 3 sub periods of five years each. The study found high beta value does not related to higher level in stock return. The positive relationship between systematic risk and return does not have a strong evidence to support it. The research also identify that the securities market line has direct relationship between risk and return. The unsystematic risk does not have an effect on the return. It means that stock prices cannot be effectively predicted by CAPM and Malaysia Stock and the validity of CAPM does not exist in Malaysia Stock Exchange Market for the period 2007-2015 due to some limitations such as time frame, sample size and others. This paper suggest a different assets pricing model and takes into consideration of some related variables in predicting future stocks returns. This research provides important implication to investors, analysts, stock brokers, speculators, fund managers, practitioners, relevant authorities, and government.  

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