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.

Idiosyncratic Volatility and the Cross Section of Expected Returns

2008 ◽  
Vol 43 (1) ◽  
pp. 29-58 ◽  
Author(s):  
Turan G. Bali ◽  
Nusret Cakici
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
Cross Sectional ◽  
Monthly Data ◽  
Portfolio Returns ◽  
Expected Stock Returns ◽  
The Cross ◽  
Data Frequency

AbstractThis paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that i) the data frequency used to estimate idiosyncratic volatility, ii) the weighting scheme used to compute average portfolio returns, iii) the breakpoints utilized to sort stocks into quintile portfolios, and iv) using a screen for size, price, and liquidity play critical roles in determining the existence and significance of a relation between idiosyncratic risk and the cross section of expected returns. Portfoliolevel analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse volatility-weighted), three breakpoints (CRSP, NYSE, equal market share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that no robustly significant relation exists between idiosyncratic volatility and expected returns.

Predicting US recessions with stock market illiquidity

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Cross Section ◽  
Market Liquidity ◽  
Credit Spreads ◽  
Stock Market Liquidity ◽  
Predictive Content ◽  
Dynamic Link

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”

scholarly journals Forecasting using cross-section average–augmented time series regressions

2020 ◽  
Author(s):  
Hande Karabiyik ◽  
Joakim Westerlund
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Regression Models ◽  
Common Factor ◽  
Principal Component ◽  
Small Sample ◽  
Interactive Effects ◽  
Cross Sectional ◽  
Second Development ◽  
The Cross

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

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 Information Content of Implicit Spot Prices Embedded in Single Stock Future Prices: Evidence from Indian Market

2017 ◽  
Vol 16 (2) ◽  
pp. 169-187 ◽  
Author(s):  
Rajesh Pathak ◽  
Thanos Verousis ◽  
Yogesh Chauhan
Keyword(s):  
Stock Returns ◽  
Information Content ◽  
Stock Exchange ◽  
Jel Classification ◽  
Spot Price ◽  
Spot Prices ◽  
Pricing Errors ◽  
Pricing Error ◽  
The Difference ◽  
Cost Of Carry

This study examines the information content of pricing error, measured by the difference between the implied price computed using the cost of carry model and the spot price of Single Stock Futures (SSFs), traded on National Stock Exchange (NSE), India. The returns of portfolios, based on ranking of such pricing errors, are investigated. The consistency of results is verified by controlling for established risk factors, that is, market, size, value and momentum premium, and idiosyncratic factors such as firm’s liquidity and size. Our study reveals that the pricing error is a priced risk factor that contains incremental information about stock returns of day t, and not beyond. We conclude that implied spot prices from stock futures market are useful for traders to profit in the spot market. JEL Classification: G120, G130

scholarly journals Illiquidity and Stock Returns Cross-Section and Time-Series Effects＊

2012 ◽  
pp. 110-136 ◽  
Author(s):  
Yakov Amihud ◽  
Yakov Amihud ◽  
Haim Mendelson ◽  
Lasse Heje Pedersen
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Cross Section
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