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Journal ArticleDOI

Market Expectations in the Cross‐Section of Present Values

Bryan T. Kelly, +1 more
- 01 Oct 2013 - 
- Vol. 68, Iss: 5, pp 1721-1756
TLDR
Kelly et al. as mentioned in this paper showed that relying on aggregate quantities drastically understates the degree of value ratios' predictive content for both returns and cash flow growth, and hence understate the volatility of investor expectations.
Abstract
Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross-section of book-tomarket ratios, we find an out-of-sample return forecasting R 2 of 13% at the annual frequency (0.9% monthly). We document similar out-of-sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios’ exposures to economic shocks are key to identifying predictability and are consistent with duration-based theories of the value premium. THE MOST COMMON APPROACH to measuring aggregate return and cash flow expectations is predictive regression. As suggested by the present value relationship between prices, discount rates, and future cash flows, research shows that the aggregate price-dividend ratio is among the most informative predictive variables. Typical in-sample estimates find that about 10% of annual return variation can be accounted for by forecasts based on the aggregate book-tomarket ratio, but find little or no out-of-sample predictive power. 1 In this paper we show that reliance on aggregate quantities drastically understates the degree of value ratios’ predictive content for both returns and cash flow growth, and hence understates the volatility of investor expectations. Our estimates suggest that as much as 13% of the out-of-sample variation in annual market returns (as much as 12% for dividend growth), and somewhat more of the insample variation, can be explained by the cross-section of past disaggregated value ratios. To harness disaggregated information we represent the cross-section of assetspecific book-to-market ratios as a dynamic latent factor model. We relate disaggregated value ratios to aggregate expected market returns and cash flow growth. Our model is based on the idea that the same dynamic state variables driving aggregate expectations also govern the dynamics of the entire panel ∗ Kelly is with Booth School of Business, University of Chicago, and Pruitt is with the Board of Governors of the Federal Reserve System. The view expressed here are those of the authors and do not necessarily reflect the views of the Federal Reserve System or its staff. 1 See Cochrane (2005) and Koijen and Van Nieuwerburgh (2011) for surveys of return and cash flow predictability evidence using the aggregate price-dividend ratio. Similar results obtain from forecasts based on the aggregate book-to-market ratio.

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Citations
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Monetary policy communication, policy slope, and the stock market

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Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets

TL;DR: In this paper, the authors proposed new jump indexes that are aligned with the jump information on the G7 stock markets to predict the U.S. stock market volatility, and the results are consistent across the direction-of-change test and a variety of robustness tests, showing that this new jump index can contain much more predictive information than jump itself or jump index based on the Principal Component Analysis (PCA).
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Chinese stock market volatility and the role of U.S. economic variables

TL;DR: In this paper, the authors investigated the effects of U.S. economic variables on the time variation of Chinese stock market volatility, and found that the predictability of these variables is statistically and economically significant.
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Industry Interdependencies and Cross-Industry Return Predictability

TL;DR: This paper used the adaptive LASSO from the statistical learning literature to identify economically connected industries in a general predictive regression framework and found extensive evidence that lagged returns of interdependent industries are significant predictors of individual industry returns.
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Financial time series forecasting using rough sets with time-weighted rule voting

TL;DR: The results showed that the rough sets approach with time weighted rule voting outperforms the classical rough sets and support vector machines decision systems and is profitable as compared to the buy and hold strategy.
References
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Journal ArticleDOI

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ReportDOI

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Whitney K. Newey, +1 more
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Journal ArticleDOI

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TL;DR: In this paper, the authors present a body of positive microeconomic theory dealing with conditions of risk, which can be used to predict the behavior of capital marcets under certain conditions.
Journal ArticleDOI

Risk, Return, and Equilibrium: Empirical Tests

TL;DR: In this article, the relationship between average return and risk for New York Stock Exchange common stocks was tested using a two-parameter portfolio model and models of market equilibrium derived from the two parameter portfolio model.
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