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This article evaluates a large collection of systemic risk measures based on their ability to predict macroeconomic downturns. We evaluate 19 measures of systemic risk in the US and Europe spanning several decades. We propose dimension reduction estimators for constructing systemic risk indexes from the cross section of measures and prove their consistency in a factor model setting. Empirically, systemic risk indexes provide significant predictive information out-of-sample for the lower tail of future macroeconomic shocks.

Louis Federal Reserve, and Yale for helpful comments. We are grateful to Andreas Neuhierl for generously sharing data with us. A previous draft was circulated under the name "Some Characteristics Are Risk Exposures, and the Rest Are Irrelevant." AQR Capital Management is a global investment management firm, which may or may not apply similar investment techniques or methods of analysis as described herein. The views expressed here are those of the authors and not necessarily those of AQR or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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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.