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.

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.

A Bound on Expected Stock Returns

2019 ◽  
Vol 33 (4) ◽  
pp. 1565-1617 ◽  
Author(s):  
Ohad Kadan ◽  
Xiaoxiao Tang
Keyword(s):  
Lower Bound ◽  
Stock Returns ◽  
Expected Returns ◽  
Sufficient Condition ◽  
Cross Sectional ◽  
Oxford University ◽  
Expected Stock Returns ◽  
Book To Market Ratio ◽  
Published Paper ◽  
Frequency Basis

Abstract We present a sufficient condition under which the prices of options written on a particular stock can be aggregated to calculate a lower bound on the expected returns of that stock. The sufficient condition imposes a restriction on a combination of the stock’s systematic and idiosyncratic risk. The lower bound is forward-looking and can be calculated on a high-frequency basis. We estimate the bound empirically and study its cross-sectional properties. We find that the bound increases with beta and book-to-market ratio and decreases with size and momentum. The bound provides an economically meaningful signal about future stock returns. (JEL G11, G12) 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.

Beta Risk in the Cross-Section of Equities

2019 ◽  
Vol 33 (9) ◽  
pp. 4318-4366
Author(s):  
Ali Boloorforoosh ◽  
Peter Christoffersen ◽  
Mathieu Fournier ◽  
Christian Gouriéroux
Keyword(s):  
Expected Returns ◽  
Capital Asset Pricing ◽  
Stochastic Discount Factor ◽  
Security Market ◽  
Expected Return ◽  
Asset Pricing Model ◽  
Oxford University ◽  
Beta Risk ◽  
High Beta ◽  
Published Paper

Abstract We develop a conditional capital asset pricing model in continuous time that allows for stochastic beta exposure. When beta comoves with market variance and the stochastic discount factor (SDF), beta risk is priced, and the expected return on a stock deviates from the security market line. The model predicts that low-beta stocks earn high returns, because their beta positively comoves with market variance and the SDF. The opposite is true for high-beta stocks. Estimating the model on equity and option data, we find that beta risk explains expected returns on low- and high-beta stocks, resolving the “betting against beta” anomaly. Authors have furnished code and an Internet Appendix, which are available on the Oxford University Press Web site next to the link to the final published paper online.

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.

Using Stocks or Portfolios in Tests of Factor Models

2019 ◽  
Vol 55 (3) ◽  
pp. 709-750 ◽  
Author(s):  
Andrew Ang ◽  
Jun Liu ◽  
Krista Schwarz
Keyword(s):  
Asset Pricing ◽  
Risk Premia ◽  
Factor Models ◽  
Idiosyncratic Volatility ◽  
Standard Errors ◽  
Coefficient Estimates ◽  
Pricing Models ◽  
Cross Sectional ◽  
Factor Loadings ◽  
Asset Pricing Models

We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.

Financial Crises and Risk Premia*

2017 ◽  
Vol 132 (2) ◽  
pp. 765-809 ◽  
Author(s):  
Tyler Muir
Keyword(s):  
Financial Crises ◽  
Asset Prices ◽  
Risk Premia ◽  
Expected Returns ◽  
Behavioral Models ◽  
Pricing Models ◽  
Aggregate Consumption ◽  
Consumption Volatility ◽  
International Panel ◽  
Asset Pricing Models

Abstract I analyze the behavior of risk premia in financial crises, wars, and recessions in an international panel spanning over 140 years and 14 countries. I document that expected returns, or risk premia, increase substantially in financial crises, but not in the other episodes. Asset prices decline in all episodes, but the decline in financial crises is substantially larger than the decline in fundamentals so that expected returns going forward are large. However, drops in consumption and consumption volatility are fairly similar across financial crises and recessions and are largest during wars, so asset pricing models based on aggregate consumption have trouble matching these facts. Comparing crises to “deep” recessions strengthens these findings further. By disentangling financial crises from other bad macroeconomic times, the results suggest that financial crises are particularly important to understanding why risk premia vary. I discuss implications for theory more broadly and discuss both rational and behavioral models that are consistent with the facts. Theories where asset prices are related to the health of the financial sector appear particularly promising.

scholarly journals The Cross Section of Foreign Currency Risk Premia and Consumption Growth Risk: Comment

2011 ◽  
Vol 101 (7) ◽  
pp. 3456-3476 ◽  
Author(s):  
Craig Burnside
Keyword(s):  
Foreign Currency ◽  
Risk Premia ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Forward Premium Puzzle ◽  
Cross Sectional ◽  
Consumption Growth ◽  
Forward Premium ◽  
The Cross ◽  
Premium Puzzle

Lustig and Verdelhan (2007) argue that the excess returns to borrowing US dollars and lending in foreign currency “compensate US investors for taking on more US consumption growth risk,” yet the stochastic discount factor corresponding to their benchmark model is approximately uncorrelated with the returns they study. Hence, one cannot reject the null hypothesis that their model explains none of the cross sectional variation of the expected returns. Given this finding, and other evidence, I argue that the forward premium puzzle remains a puzzle. JEL: C58, E21, F31, G11, G12

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)

scholarly journals Does Partisanship Shape Investor Beliefs? Evidence from the COVID-19 Pandemic

2020 ◽  
Vol 10 (4) ◽  
pp. 863-893 ◽  
Author(s):  
J Anthony Cookson ◽  
Joseph E Engelberg ◽  
William Mullins
Keyword(s):  
Web Site ◽  
Difference In Difference ◽  
Cross Sectional ◽  
Oxford University ◽  
Published Paper ◽  
Oxford University Press

Abstract We use party-identifying language—like “liberal media” and “MAGA”—to identify Republican users on the investor social platform StockTwits. Using a difference-in-difference design, we find that partisan Republicans remain relatively unfazed in their beliefs about equities during the COVID-19 pandemic, while other users become considerably more pessimistic. In cross-sectional tests, we find Republicans become relatively more optimistic about stocks that suffered the most during the COVID-19 crisis, but more pessimistic about Chinese stocks. Finally, stocks with the greatest partisan disagreement on StockTwits have significantly more trading in the broader market, explaining 28% of the increase in stock turnover during the pandemic. Authors have furnished data and an Internet Appendix, which are available on the Oxford University Press Web site next to the link to the final published paper online.

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