scholarly journals Publication Bias and the Cross-Section of Stock Returns

2019 ◽  
Vol 10 (2) ◽  
pp. 249-289 ◽  
Author(s):  
Andrew Y Chen ◽  
Tom Zimmermann
Keyword(s):  
Stock Returns ◽  
Publication Bias ◽  
Cross Section ◽  
Standard Error ◽  
P Value ◽  
Oxford University ◽  
Percentage Points ◽  
Sample Data ◽  
Out Of Sample ◽  
Published Paper

Abstract We develop an estimator for publication bias-adjusted returns and apply it to 156 published long-short portfolios. Our adjustment uses only in-sample data and provides sharper inferences than out-of-sample tests. Bias-adjusted returns are only 12.3% smaller than in-sample returns with a standard error of 1.7 percentage points. The small bias comes from the dispersion of returns across predictors, which is too large to be explained by data-mined noise. The bias is much smaller than post-publication decay (p-value ¡.0001), suggesting mispricing is important. Our results offer a different perspective about recent papers that find most published predictors are likely false. 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.

scholarly journals Contingent Capital Trigger Effects: Evidence from Liability Management Exercises

2019 ◽  
Vol 8 (2) ◽  
pp. 235-259 ◽  
Author(s):  
Boris Vallée
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Web Site ◽  
Regulatory Capital ◽  
Oxford University ◽  
Contingent Capital ◽  
Liability Management ◽  
Editorial Decision ◽  
Debt Holders ◽  
Published Paper

AbstractThis paper studies liability management exercises (LME) by banks, which have comparable regulatory capital effects than contingent capital triggers. LMEs are concentrated on low capitalization situations, both in the cross-section and in the time series and are frequently associated with equity issuances. These exercises prove effective at improving bank capitalization levels. The market reaction to LMEs is positive and mostly accrues to debt holders. These findings strengthen the case for innovative liabilities securities as a tool to improve bank resilience.Received February 8, 2019; editorial decision May 16, 2019 by Editor Andrew Ellul. 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.

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 Break Risk

2020 ◽  
Author(s):  
Simon C Smith ◽  
Allan Timmermann
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Prediction Models ◽  
Return Predictability ◽  
Present Value ◽  
New Approach ◽  
Present Value Model ◽  
Out Of Sample ◽  
Rate Processes ◽  
Value Model

Abstract We develop a new approach to modeling and predicting stock returns in the presence of breaks that simultaneously affect a large cross-section of stocks. Exploiting information in the cross-section enables us to detect breaks in return prediction models with little delay and to generate out-of-sample return forecasts that are significantly more accurate than those from existing approaches. To identify the economic sources of breaks, we explore the asset pricing restrictions implied by a present value model which links breaks in return predictability to breaks in the cash flow growth and discount rate processes.

Modeling the Cross Section of Stock Returns: A Model Pooling Approach

2012 ◽  
Vol 47 (6) ◽  
pp. 1331-1360 ◽  
Author(s):  
Michael O’Doherty ◽  
N. E. Savin ◽  
Ashish Tiwari
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Asset Allocation ◽  
Economic Stress ◽  
Predictive Score ◽  
Asset Pricing Model ◽  
Out Of Sample ◽  
Competing Models ◽  
The Cross ◽  
Allocation Decisions

AbstractModel selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.

scholarly journals Publication Bias and the Cross-Section of Stock Returns

2018 ◽  
Vol 2018 (033) ◽  
Author(s):  
Andrew Y. Chen ◽  
◽  
Tom Zimmermann ◽  
Keyword(s):  
Stock Returns ◽  
Publication Bias ◽  
Cross Section ◽  
The Cross

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

Factors That Fit the Time Series and Cross-Section of Stock Returns

2020 ◽  
Vol 33 (5) ◽  
pp. 2274-2325 ◽  
Author(s):  
Martin Lettau ◽  
Markus Pelger
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Cross Section ◽  
Principal Component ◽  
Expected Returns ◽  
Portfolio Returns ◽  
Pricing Errors ◽  
Out Of Sample ◽  
Pricing Error ◽  
Sharpe Ratios

Abstract We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample maximum Sharpe ratio of our factors is twice as large as with PCA with substantially smaller pricing errors. Our factors imply that a significant amount of characteristic information is redundant. 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.

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