Performances of liquidity factors in the stock market cycle: evidence from the Tokyo Stock Exchange

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Zhong
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Stock Exchange ◽  
Regression Method ◽  
Pricing Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
Long Run ◽  
Market Trends ◽  
Japanese Stock Market

PurposeThe purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.Design/methodology/approachSix liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.FindingsThe result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.Originality/valueIn the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.

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.  

Global risk factors in the returns of listed private equity

2018 ◽  
Vol 35 (2) ◽  
pp. 340-360 ◽  
Author(s):  
Jörg Döpke ◽  
Lars Tegtmeier
Keyword(s):  
Risk Factors ◽  
Asset Pricing ◽  
Stock Market ◽  
Cross Section ◽  
Private Equity ◽  
Pricing Model ◽  
Stock Market Returns ◽  
Content Type ◽  
Asset Pricing Model ◽  
Macroeconomic Risk

PurposeThe purpose of this paper is, to study macroeconomic risk factors driving the expected stock returns of listed private equity (LPE). The authors use LPE indices divided into different styles and regions from January 2004 to December 2016 and a set of country stock indices to estimate the macroeconomic risk profiles and corresponding risk premiums. Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock marketβs that are greater than 1. A one-factor asset pricing model using world stock market returns as the only possible risk factor is rejected on the basis of generalized method of moments (GMM) orthogonality conditions. In contrast, using the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of economic policy uncertainty as additional risk factors, this multifactor model is able to price a cross-section of expected LPE returns. The risk-return profile of LPE differs from country equity indices. Consequently, LPE should be treated as a separate asset class.Design/methodology/approachFollowing Ferson and Harvey (1994), the authors use an unconditional asset pricing model to capture the structure of returns across LPE. The authors use 11 LPE indices divided into different styles and regions from January 2004 to December 2016, and a set of country stock indices as spanning assets to estimate the macroeconomic risk profiles and corresponding risk premiums.FindingsUsing a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock marketßs that are greater than 1. The authors estimate a one-factor asset pricing model using world stock market returns as the only possible risk factor by GMM. This model is rejected on the basis of the GMM orthogonality conditions. By contrast, a multifactor model built on the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of global economic policy uncertainty as additional risk factors is able to price a cross-section of expected LPE returns.Research limitations/implicationsGiven data availability, the authors’ sample is strongly influenced by the financial crisis and its aftermath.Practical implicationsInformation about the risk profile of LPE is important for asset allocation decisions. In particular, it may help to optimally react to contemporaneous changes in economy-wide risk factors.Originality/valueTo the best of authors’ knowledge, this is the first LPE study which investigates whether a set of macroeconomic factors is actually priced and, therefore, associated with a non-zero risk premium in the cross-section of returns.

Application of CAPM in the Indian Stock Market A Comprehensive Reassessment

2005 ◽  
Vol 1 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Raj S. Dhankar ◽  
Rohini Singh
Keyword(s):  
Stock Market ◽  
Capital Market ◽  
Stock Exchange ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Asset Pricing Model ◽  
Conflicting Evidence ◽  
Empirical Tests ◽  
Indian Stock Market

There is conflicting evidence on the applicability of Capital Asset Pricing Model in the Indian stock market. Data for 158 stocks listed on the Bombay Stock Exchange was analyzed using a number of tests from 1991–2002, the period which roughly coincides with the period after liberalization and initiation of capital market reforms. Taken in aggregate the various empirical tests show that CAPM is not valid for the Indian stock market for the period studied.

scholarly journals Investment decision by using Capital Asset Method Pricing Model (CAPM) (Case studies on five automotive companies listed in stock exchange)

2021 ◽  
Vol 1 (2) ◽  
pp. 165-175
Author(s):  
Ahmad Musodik ◽  
Arrum Sari ◽  
Ida Nur Fitriani
Keyword(s):  
Asset Pricing ◽  
Automotive Industry ◽  
Stock Exchange ◽  
Investment Decision ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Expected Return ◽  
Asset Pricing Model

Investment is a tool for investors to get more profit than what has been invested. Investors must be able to predict the possibilities that occur when investing. Capital Asset Pricing Model is a tool to predict the development of investment in a particular company used to calculate and determine the Expected Return in minimizing risk investments. The authors conducted research using a sample of 5 companies in the automotive industry, namely PT Astra International Tbk, PT Indokordsa Tbk, PT Indomobil Sukses Internasional Tbk, PT Astra Otoparts Tbk, and PT Gajah Tunggal Tbk. This study uses a descriptive quantitative approach with Microsoft Excel 2016 analysis tools. This study aims to determine Portfolio Analysis with the Capital Asset Pricing Model (CAPM) approach which is used as the basis for making stock investment decisions in automotive industry sector companies listed on the Indonesia Stock Exchange. Use from the results of the analysis of the results by comparing the value of E(Ri) has a directly proportional relationship, meaning that the higher the value of, then the stock return (E(Ri)) will be high as well. Of the 5 companies, there are 2 companies that are in the Undervalued category and 3 companies that are in the overvalued category. This means that investors who will invest in companies engaged in the automotive industry can decide to buy shares of the companies PT Indomobil Sukses Internasional Tbk and PT Gajah Tunggal Tbk, because they are classified as undervalued. Meanwhile, investors who want to invest in shares are not advised to buy company shares that are in the overvalued category, but are advised to sell them to investors who already have shares in the company.

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

2020 ◽  
Vol 21 (3) ◽  
pp. 233-251
Author(s):  
Xiaoying Chen ◽  
Nicholas Ray-Wang Gao
Keyword(s):  
Risk Factor ◽  
Asset Pricing ◽  
Explanatory Power ◽  
Investor Sentiment ◽  
Size Factor ◽  
Pricing Model ◽  
Volatility Risk ◽  
Content Type ◽  
Asset Pricing Model ◽  
Market Factor

Purpose Since 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/approach This 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. Findings The 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/value This 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.

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