Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns

2014 ◽  
Author(s):  
Turan G. Bali ◽  
Stephen Brown ◽  
Yi Tang
Keyword(s):  
Stock Returns ◽  
Cross Sectional ◽  
Expected Stock Returns ◽  
Economic Forecasts

Volatility and Expected Option Returns

2019 ◽  
Vol 55 (3) ◽  
pp. 1025-1060 ◽  
Author(s):  
Guanglian Hu ◽  
Kris Jacobs
Keyword(s):  
Stock Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Stock Volatility ◽  
Option Returns ◽  
Put Option ◽  
Expected Stock Returns ◽  
Alternative Measures ◽  
Expected Option Returns ◽  
Equity Option

We analyze the relation between expected option returns and the volatility of the underlying securities. The expected return from holding a call (put) option is a decreasing (increasing) function of the volatility of the underlying. These predictions are supported by the data. In the cross section of equity option returns, returns on call (put) option portfolios decrease (increase) with underlying stock volatility. This finding is not due to cross-sectional variation in expected stock returns. It holds in various option samples with different maturities and moneyness, and is robust to alternative measures of underlying volatility and different weighting methods.

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)

Can the Cross-Sectional Variation in Expected Stock Returns Explain Momentum?

2009 ◽  
Vol 44 (4) ◽  
pp. 777-794 ◽  
Author(s):  
George Bulkley ◽  
Vivekanand Nawosah
Keyword(s):  
Stock Returns ◽  
Expected Returns ◽  
Sample Period ◽  
Expected Return ◽  
Cross Sectional ◽  
Holding Period ◽  
Expected Stock Returns ◽  
The Cross

AbstractIt has been hypothesized that momentum might be rationally explained as a consequence of the cross-sectional variation of unconditional expected returns. Stocks with relatively high unconditional expected returns will on average outperform in both the portfolio formation period and in the subsequent holding period. We evaluate this explanation by first removing unconditional expected returns for each stock from raw returns and then testing for momentum in the resulting series. We measure the unconditional expected return on each stock as its mean return in the whole sample period. We find momentum effects vanish in demeaned returns.

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 Equity Returns in Developed Markets

2021 ◽  
pp. 68-90
Author(s):  
A. Doruk Günaydin
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Developed Countries ◽  
Equity Returns ◽  
Market Returns ◽  
Short Term ◽  
Firm Level ◽  
Cross Sectional ◽  
Expected Stock Returns ◽  
The Cross

This chapter examines the relation between various firm-specific variables and the cross-section of equity returns in 26 developed countries. Univariate portfolio analyses using equal-weighted returns show that low beta, book-to-market equity, and momentum analysis are also priced in the cross-section of developed market returns, whereas short-term reversal and downside beta manifest themselves in the opposite direction. Univariate portfolio analysis based on value-weighted returns reveal that the predictive power of book-to-market equity and short-term reversal is driven by small stocks. Multivariate firm-level cross-sectional regression analysis document that momentum, short-term reversal, illiquidity, idiosyncratic volatility, hybrid tail risk, lower partial moment are related to expected stock returns. Overall, the most robust cross-sectional predictor in developed market is found to be return momentum.

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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