Competition, Markups, and Predictable Returns

2020 ◽  
Vol 33 (12) ◽  
pp. 5906-5939 ◽  
Author(s):  
Alexandre Corhay ◽  
Howard Kung ◽  
Lukas Schmid
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Industry Concentration ◽  
Expected Returns ◽  
Oligopolistic Competition ◽  
Risk Premiums ◽  
Macroeconomic Volatility ◽  
Aggregate Risk ◽  
Secular Increase ◽  
Over Time

Abstract This paper jointly examines the link between competition and expected returns in the time series and in the cross-section. To this end, we build a general equilibrium model where markups vary because of firm entry with oligopolistic competition. When concentration is high, markups are more sensitive to entry risk. We find that higher markups are associated with higher expected returns over time and across industries, in line with the data. The model can also quantitatively account for the persistent rise in aggregate risk premiums and macroeconomic volatility associated with the secular increase trend industry concentration since the mid-1980s.

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.

Cross-Section of Expected Returns Based on Equity Duration

2019 ◽  
Vol 27 (3) ◽  
pp. 297-327
Author(s):  
Sungjeh Moon ◽  
Joonhyuk Song
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Factor Model ◽  
Explanatory Power ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Expected Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Risk Premiums

We analyze the cross-sectional expected return of KOSPI stocks using equity duration. From 1991 to 2018, we calculate equity durations for the KOSPI listed stocks (including de-listed stocks) and find that the shorter the equity duration, the higher the risk premium. Using the 4-factor model with equity duration added to the benchmark 3-factor model, the explanatory power of the 4-factor model is superior to that of the existing benchmark model in accounting for risk premiums. This is an unusual finding that is not readily explainable by the traditional CAPM or the Fama-French 3-factor model. This can be interpreted that the equity duration is a separate and significant risk factor dissociated from the HML of the 3-factor model.

Cross-Sectional PEG Ratios, Market Equity Premium, and Macroeconomic Activity

2018 ◽  
Vol 35 (3) ◽  
pp. 471-500
Author(s):  
Xiaoquan Jiang ◽  
Qiang Kang
Keyword(s):  
Cross Section ◽  
Expected Returns ◽  
Future Market ◽  
Cross Sectional ◽  
Risk Premiums ◽  
Econometric Methods ◽  
Out Of Sample ◽  
Macroeconomic Activity ◽  
Equity Premiums ◽  
Valuation Ratios

This article explores the information content of PEG ratios (price/earnings to growth ratios) for future aggregate returns and economic fundamentals. We first establish an analytic link between PEG ratios and time-varying expected returns of stocks. We then combine the link with empirical asset pricing models to extract market-wide information from cross-sectional PEG ratios. The resultant cross-section estimates of the risk premiums on stock betas serve as proxies for market-wide information. The proxies contain salient information about future market equity premiums and macroeconomic activity both in-sample and out-of-sample. Moreover, the proxies outperform aggregate PEG ratios and the cross-section beta-premium estimate based on conventional valuation ratios and retain incremental power in forecasting future market equity premiums. The results are robust to using various econometric methods for standard error adjustments.

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