The Cross Section of Expected Returns and Amortized Spreads

2006 ◽  
Vol 09 (04) ◽  
pp. 597-638 ◽  
Author(s):  
Zhongzhi Lawrence He ◽  
Lawrence Kryzanowski
Keyword(s):  
Asset Pricing ◽  
Risk Premium ◽  
Overlapping Generations ◽  
Expected Returns ◽  
Relative Importance ◽  
Expected Return ◽  
Cross Sectional ◽  
Market Risk Premium ◽  
The Cross ◽  
Level Effect

The cross-sectional relationship between expected returns and amortized spreads is studied in an overlapping-generations economy with an average investor. The commonality in liquidity is directly incorporated into the asset-pricing relation. In a static equilibrium, the amortized spread of an asset is related to its expected return through four channels; namely: the equilibrium zero-beta rate, the market risk premium, a level effect, and an incremental sensitivity effect. Although both are present over the entire period, their relative importance shifts from a significant level to a significant sensitivity effect from the earlier to most recent sub-period in the Canadian stock market.

The Cross Section of Expected Returns with MIDAS Betas

2011 ◽  
Vol 47 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Mariano González ◽  
Juan Nave ◽  
Gonzalo Rubio
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Market Risk ◽  
Ordinary Least Squares ◽  
Mixed Data ◽  
Expected Returns ◽  
Data Sampling ◽  
Cross Sectional ◽  
Market Risk Premium ◽  
The Cross

AbstractThis paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

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.

scholarly journals Creative Destruction and Asset Prices

2016 ◽  
Vol 51 (6) ◽  
pp. 1739-1768 ◽  
Author(s):  
Joachim Grammig ◽  
Stephan Jank
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Creative Destruction ◽  
Expected Returns ◽  
Expected Return ◽  
Cross Sectional ◽  
Large Growth ◽  
Estimated Risk ◽  
Growth Stocks ◽  
Invention Activity

We relate Schumpeter’s notion of creative destruction to asset pricing, thereby offering a novel explanation of size and value premia. We argue that small-value firms must offer higher expected returns to compensate for the risk posed by serendipitous invention activity, whereas large-growth stocks provide protection against creative destruction and receive expected return discounts. A 2-factor model that accounts for creative-destruction risk effectively explains the cross-sectional return variation of size- and book-to-market-sorted portfolios. The estimated risk compensations associated with creative destruction are substantial and statistically significant, indicating their relevance for asset pricing.

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.

scholarly journals Predictability and the Cross-Section of Expected Returns: A Challenge for Asset Pricing Models

2021 ◽  
Author(s):  
Christian Schlag ◽  
Michael Semenischev ◽  
Julian Thimme
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Empirical Test ◽  
Expected Returns ◽  
State Variables ◽  
Expected Return ◽  
Long Run ◽  
The Cross ◽  
Long Run Risks ◽  
Macro Finance

Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples. This paper was accepted by David Simchi-Levi, finance.

The Price of Predictability: Estimating Inconsistency Premiums in Social Interactions

2021 ◽  
pp. 014616722199853
Author(s):  
Judith Gerten ◽  
Michael K. Zürn ◽  
Sascha Topolinski
Keyword(s):  
Risk Premium ◽  
Psychological Research ◽  
Expected Value ◽  
Decision Makers ◽  
Expected Returns ◽  
Expected Return ◽  
Total N ◽  
Financial Decision ◽  
Variance Risk ◽  
Risky Option

For financial decision-making, people trade off the expected value (return) and the variance (risk) of an option, preferring higher returns to lower ones and lower risks to higher ones. To make decision-makers indifferent between a risky and risk-free option, the expected value of the risky option must exceed the value of the risk-free option by a certain amount—the risk premium. Previous psychological research suggests that similar to risk aversion, people dislike inconsistency in an interaction partner’s behavior. In eight experiments (total N = 2,412) we pitted this inconsistency aversion against the expected returns from interacting with an inconsistent partner. We identified the additional expected return of interacting with an inconsistent partner that must be granted to make decision-makers prefer a more profitable, but inconsistent partner to a consistent, but less profitable one. We locate this inconsistency premium at around 31% of the expected value of the risk-free option.

Understanding the Determinants of Analyst Target Price Implied Returns

2020 ◽  
Vol 95 (6) ◽  
pp. 125-149
Author(s):  
Patricia M. Dechow ◽  
Haifeng You
Keyword(s):  
Stock Returns ◽  
Investment Decision ◽  
Optimistic Bias ◽  
Target Price ◽  
Expected Return ◽  
Cross Sectional ◽  
Empirical Risk ◽  
Investment Decision Making ◽  
The Cross ◽  
Jel Classifications

ABSTRACT We investigate the determinants of analysts' target price implied returns and the implication of our findings for investment decision-making. We identify four broad sets of factors that help explain the cross-sectional variation in target price implied returns: future realized stock returns, errors in forecasting fundamentals, errors in forecasting the expected return to risk, and biases relating to analysts' incentives. Our results suggest that all four sets help explain target price implied returns, with errors in forecasting the expected return to empirical risk proxies having the greatest impact. Collectively, these variables explain nearly a quarter of the cross-sectional variation in target price implied returns. We use our model to predict the optimistic bias in target price implied returns and evaluate whether investors correctly ignore the predictable bias. The results suggest that investors make similar valuation errors to analysts and/or do not perfectly back out the predicted bias in target prices. JEL Classifications: M40; M41; G14.

The Bias in Two-Pass Regression Tests of Asset-Pricing Models in Presence of Idiosyncratic Errors with Cross-Sectional Dependence

2019 ◽  
Vol 22 (02) ◽  
pp. 1950012
Author(s):  
Thomas Gramespacher ◽  
Armin Bänziger
Keyword(s):  
Asset Pricing ◽  
Risk Premium ◽  
Measurement Errors ◽  
Factor Model ◽  
Pricing Models ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Asset Pricing Models ◽  
Slope Estimator ◽  
Regression Tests

In two-pass regression-tests of asset-pricing models, cross-sectional correlations in the errors of the first-pass time-series regression lead to correlated measurement errors in the betas used as explanatory variables in the second-pass cross-sectional regression. The slope estimator of the second-pass regression is an estimate for the factor risk-premium and its significance is decisive for the validity of the pricing model. While it is well known that the slope estimator is downward biased in presence of uncorrelated measurement errors, we show in this paper that the correlations seen in empirical return data substantially suppress this bias. For the case of a single-factor model, we calculate the bias of the OLS slope estimator in the presence of correlated measurement errors with a first-order Taylor-approximation in the size of the errors. We show that the bias increases with the size of the errors, but decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical data commonly used in asset-pricing tests.

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