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 Testing Beta-Pricing Models Using Large Cross-Sections

2019 ◽  
Vol 33 (6) ◽  
pp. 2796-2842 ◽  
Author(s):  
Valentina Raponi ◽  
Cesare Robotti ◽  
Paolo Zaffaroni
Keyword(s):  
Cross Sections ◽  
Risk Premia ◽  
Expected Returns ◽  
Firm Characteristics ◽  
Pricing Models ◽  
Cross Sectional ◽  
Misspecified Models ◽  
Oxford University ◽  
Fama French Factors ◽  
Published Paper

Abstract We propose a methodology for estimating and testing beta-pricing models when a large number of assets is available for investment but the number of time-series observations is fixed. We first consider the case of correctly specified models with constant risk premia, and then extend our framework to deal with time-varying risk premia, potentially misspecified models, firm characteristics, and unbalanced panels. We show that our large cross-sectional framework poses a serious challenge to common empirical findings regarding the validity of beta-pricing models. In the context of pricing models with Fama-French factors, firm characteristics are found to explain a much larger proportion of variation in estimated expected returns than betas. 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.

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.

scholarly journals Volatility Markets Underreacted to the Early Stages of the COVID-19 Pandemic

2020 ◽  
Vol 10 (4) ◽  
pp. 635-668 ◽  
Author(s):  
Ing-Haw Cheng
Keyword(s):  
Equity Market ◽  
Trading Strategies ◽  
Futures Prices ◽  
Pricing Models ◽  
Subsequent Increase ◽  
Oxford University ◽  
Price Premiums ◽  
Asset Pricing Models ◽  
Vix Futures ◽  
Published Paper

Abstract VIX futures prices rose slowly in late February and early March 2020 as the COVID-19 pandemic took hold. Futures price premiums, defined as futures prices minus real-time statistical forecasts of future VIX values, turned sharply negative and remained negative until mid-April. Trading strategies based on estimated premiums profited from the subsequent increase in market volatility and equity market crash. The underreaction of futures prices to growing pandemic risks poses a puzzle for standard 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.

Factor Timing

2020 ◽  
Vol 33 (5) ◽  
pp. 1980-2018 ◽  
Author(s):  
Valentin Haddad ◽  
Serhiy Kozak ◽  
Shrihari Santosh
Keyword(s):  
Stock Returns ◽  
Substantial Improvement ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Portfolio Performance ◽  
Oxford University ◽  
Static Factor ◽  
Cyclical Behavior ◽  
New Challenges ◽  
Published Paper

Abstract The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. 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.

Resiliency and Stock Returns

2019 ◽  
Vol 33 (2) ◽  
pp. 747-782
Author(s):  
Jian Hua ◽  
Lin Peng ◽  
Robert A Schwartz ◽  
Nazli Sila Alan
Keyword(s):  
Stock Returns ◽  
Web Site ◽  
Trading Volume ◽  
Expected Returns ◽  
Control Variables ◽  
Oxford University ◽  
And Control ◽  
Published Paper ◽  
Extensive List ◽  
Oxford University Press

Abstract We present resiliency as a measure of liquidity and assess its relationship to expected returns. We establish a covariance-based measure, RES, that captures opening period resiliency, and use it to find a significant nonresiliency premium that ranges from 33 to 57 basis points per month. The premium persists after accounting for an extensive list of other liquidity-related measures and control variables. The results are significant for both value-weighted and equal-weighted returns, when micro-cap stocks are excluded, and for a sample of large cap stocks. The premium is particularly pronounced when trading volume is high. 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 Stock Volatility and the Great Depression

2018 ◽  
Vol 32 (9) ◽  
pp. 3544-3570 ◽  
Author(s):  
Gustavo S Cortes ◽  
Marc D Weidenmier
Keyword(s):  
Stock Returns ◽  
Great Depression ◽  
Stock Volatility ◽  
Stock Return Volatility ◽  
Building Permits ◽  
Oxford University ◽  
The Great Depression ◽  
Editorial Decision ◽  
High Level ◽  
Published Paper

Abstract Stock return volatility during the Great Depression has been labeled a “volatility puzzle” because the standard deviation of stock returns was 2 to 3 times higher than any other period in American history. We investigate this puzzle using a new series of building permits and leverage. Our results suggest that volatility in building permit growth and financial leverage largely explain the high level of stock volatility during the Great Depression. Markets factored in the possibility of a forthcoming economic disaster. Received September 30, 2017; editorial decision August 27, 2018 by Editor Philip E. Strahan. 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

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