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.

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.

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.

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.

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.

scholarly journals The cross-section of expected stock returns and components of idiosyncratic volatility

2021 ◽  
Author(s):  
◽  
Seyed Reza Tabatabaei Poudeh
Keyword(s):  
Risk Factors ◽  
Stock Returns ◽  
Cross Section ◽  
Idiosyncratic Volatility ◽  
Firm Level ◽  
Cross Sectional ◽  
Expected Stock Returns ◽  
Book To Market Ratio ◽  
Alpha Risk ◽  
The Relationship

We examine the relationship between stock returns and components of idiosyncratic volatility—two volatility and two covariance terms— derived from the decomposition of stock returns variance. The portfolio analysis result shows that volatility terms are negatively related to expected stock returns. On the contrary, covariance terms have positive relationships with expected stock returns at the portfolio level. These relationships are robust to controlling for risk factors such as size, book-to-market ratio, momentum, volume, and turnover. Furthermore, the results of Fama-MacBeth cross-sectional regression show that only alpha risk can explain variations in stock returns at the firm level. Another finding is that when volatility and covariance terms are excluded from idiosyncratic volatility, the relation between idiosyncratic volatility and stock returns becomes weak at the portfolio level and disappears at the firm level.

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.

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.

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.

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