Do Arbitrage Pricing Models Explain the Predictability of Stock Returns?

1995 ◽  
Vol 68 (3) ◽  
pp. 309 ◽  
Author(s):  
Wayne E. Ferson ◽  
Robert A. Korajczyk
Keyword(s):  
Stock Returns ◽  
Arbitrage Pricing ◽  
Pricing Models

A Transaction-Cost Perspective on the Multitude of Firm Characteristics

2020 ◽  
Vol 33 (5) ◽  
pp. 2180-2222 ◽  
Author(s):  
Victor DeMiguel ◽  
Alberto Martín-Utrera ◽  
Francisco J Nogales ◽  
Raman Uppal
Keyword(s):  
Transaction Costs ◽  
Stock Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Oxford University ◽  
Asset Pricing Models ◽  
Economic Rationale ◽  
Underlying Stocks ◽  
Different Characteristics ◽  
Published Paper

Abstract We investigate how transaction costs change the number of characteristics that are jointly significant for an investor’s optimal portfolio and, hence, how they change the dimension of the cross-section of stock returns. We find that transaction costs increase the number of significant characteristics from 6 to 15. The explanation is that, as we show theoretically and empirically, combining characteristics reduces transaction costs because the trades in the underlying stocks required to rebalance different characteristics often cancel out. Thus, transaction costs provide an economic rationale for considering a larger number of characteristics than that in prominent asset-pricing models. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

scholarly journals Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 394
Author(s):  
Adeel Nasir ◽  
Kanwal Iqbal Khan ◽  
Mário Nuno Mata ◽  
Pedro Neves Mata ◽  
Jéssica Nunes Martins
Keyword(s):  
At Risk ◽  
Asset Pricing ◽  
Stock Returns ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Time Varying ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Market Beta

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

An extensile method on the arbitrage pricing theory based on downside risk (D-APT)

2014 ◽  
Vol 10 (1) ◽  
pp. 54-72 ◽  
Author(s):  
Mohammad Reza Tavakoli Baghdadabad ◽  
Paskalis Glabadanidis
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Arbitrage Pricing Theory ◽  
Arbitrage Pricing ◽  
Content Type ◽  
Corporate Managers ◽  
Fund Managers ◽  
Financial Variables ◽  
Expected Stock Returns ◽  
Pricing Theory

Purpose – The purpose of this paper is to propose a new and improved version of arbitrage pricing theory (APT), namely, downside APT (D-APT) using the concepts of factors’ downside beta and semi-variance. Design/methodology/approach – This study includes 163 stocks traded on the Malaysian stock market and uses eight macroeconomic variables as the dependent and independent variables to investigate the relationship between the adjusted returns and the downside factors’ betas over the whole period 1990-2010, and sub-periods 1990-1998 and 1999-2010. It proposes a new version of the APT, namely, the D-APT to replace two deficient measures of factor's beta and variance with more efficient measures of factors’ downside betas and semi-variance to improve and dispel the APT deficiency. Findings – The paper finds that the pricing restrictions of the D-APT, in the context of an unrestricted linear factor model, cannot be rejected over the sample period. This means that all of the identified factors are able to price stock returns in the D-APT model. The robustness control model supports the results reported for the D-APT as well. In addition, all of the empirical tests provide support the D-APT as a new asset pricing model, especially during a crisis. Research limitations/implications – It may be worthwhile explaining the autocorrelation limitation between variables when applying the D-APT. Practical implications – The framework can be useful to investors, portfolio managers, and economists in predicting expected stock returns driven by macroeconomic and financial variables. Moreover, the results are important to corporate managers who undertake the cost of capital computations, fund managers who make investment decisions and, investors who assess the performance of managed funds. Originality/value – This paper is the first study to apply the concepts of semi-variance and downside beta in the conventional APT model to propose a new model, namely, the D-APT.

Firm Characteristics, Cross-Sectional Regression Estimates, and Asset Pricing Tests

2019 ◽  
Vol 10 (2) ◽  
pp. 290-334 ◽  
Author(s):  
Chris Kirby
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Nonlinear Effects ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Portfolio Returns ◽  
Asset Pricing Models ◽  
Formidable Challenge ◽  
Expected Stock Returns

Abstract I test a number of well-known asset pricing models using regression-based managed portfolios that capture nonlinearity in the cross-sectional relation between firm characteristics and expected stock returns. Although the average portfolio returns point to substantial nonlinearity in the data, none of the asset pricing models successfully explain the estimated nonlinear effects. Indeed, the estimated expected returns produced by the models display almost no variation across portfolios. Because the tests soundly reject every model considered, it is apparent that nonlinearity in the relation between firm characteristics and expected stock returns poses a formidable challenge to asset pricing theory. (JEL G12, C58)

Arbitrage Portfolios

2020 ◽  
Author(s):  
Soohun Kim ◽  
Robert A Korajczyk ◽  
Andreas Neuhierl
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Predictive Power ◽  
Abnormal Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Maximal Weight ◽  
Large Panel ◽  
Sharpe Ratios

Abstract We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.

Can Asset Pricing Models Price Idiosyncratic Risk in U.K. Stock Returns?

2007 ◽  
Vol 42 (4) ◽  
pp. 507-535 ◽  
Author(s):  
Jonathan Fletcher
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Idiosyncratic Risk ◽  
Pricing Models ◽  
Asset Pricing Models

Funds, Factors, and Diversification in Arbitrage Pricing Models

Econometrica ◽  
1983 ◽  
Vol 51 (5) ◽  
pp. 1305 ◽  
Author(s):  
Gary Chamberlain
Keyword(s):  
Arbitrage Pricing ◽  
Pricing Models

scholarly journals Pricing Spread Options with Stochastic Interest Rates

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yunguo Jin ◽  
Shouming Zhong
Keyword(s):  
Option Pricing ◽  
Interest Rates ◽  
Stock Returns ◽  
Grid Method ◽  
Pricing Models ◽  
Stochastic Interest Rates ◽  
Spread Options ◽  
Asset Pricing Models ◽  
Stochastic Interest ◽  
Spread Option

Although spread options have been extensively studied in the literature, few papers deal with the problem of pricing spread options with stochastic interest rates. This study presents three novel spread option pricing models that permit the interest rates to be random. The paper not only presents a good approach to formulate spread option pricing models with stochastic interest rates but also offers a new test bed to understand the dynamics of option pricing with interest rates in a variety of asset pricing models. We discuss the merits of the models and techniques presented by us in some asset pricing models. Finally, we use regular grid method to the calculation of the formula when underlying stock returns are continuous and a mixture of both the regular grid method and a Monte Carlo method to the one when underlying stock returns are discontinuous, and sensitivity analyses are presented.

Sign in / Sign up

Export Citation Format

Share Document