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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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Journal ArticleDOI

Dissecting Market Expectations in the Cross-Section of Book-to-Market Ratios

TL;DR: This article found no evidence that partial least squares based on disaggregated book-to-market ratios produces a model of market premiums with persistently positive out-of-sample R2, as originally documented for market returns.
Journal ArticleDOI

Expectation-Driven Term Structure of Equity and Bond Yields

TL;DR: In this article, a new equilibrium model of subjective expectations is presented to explain the joint historical dynamics of equity and bond yields (and their yield spreads) and their movements are mainly driven by subjective dividend/GDP growth expectations.
Journal ArticleDOI

Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation

TL;DR: In this paper , two new techniques are introduced to improve the forecasting of electricity market prices by incorporating the characteristics of electricity prices that are discretely affected by the fuel cost per unit, the unit generation cost of the large-scale generators, and the demand.
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Journal ArticleDOI

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