This paper shows that exposure to aggregate distress risk is the underlying source of the premiums for the Fama-French size (SMB) and value (HML) factors. Using a unique data set of aggregate business failures of both private and public firms from 1926 to 1997, I build portfolios that track news about future firm failures. These tracking portfolios optimally hedge aggregate distress risk and earn a Capital Asset Pricing Model (CAPM) alpha of approximately −4% a year. Both HML and SMB predict changes in future failure rates. Small stocks have lower returns than large stocks and value stocks have lower returns than growth stocks when the market expects an increase in future failure rates. Finally, a two-factor model with the market and the tracking portfolio for aggregate distress as factors does as well as the Fama-French three-factor model in pricing the 25 size and book-to-market sorted portfolios.

We show that a simple, intuitive variable, Goliath versus David (GVD), reflects time variation in discount rates related to changes in aggregate business conditions. GVD is the annual change in the weight of the largest 250 firms in the aggregate stock market and is motivated by research that shows that small firms are more severely impacted than large firms by economic shocks due to differences in access to external finance. We find that GVD is the best single predictor of out-of-sample market returns among traditional predictors, predicting quarterly market returns with an out-of-sample R 2 of 6.3% in the 1976–2011 evaluation period.

We show that idiosyncratic jumps are a key determinant of mean stock returns from both an ex post and ex ante perspective. Ex post, the entire annual average return of a typical stock accrues on the four days on which its price jumps. Ex ante, idiosyncratic jump risk earns a premium: a value-weighted weekly long-short portfolio that buys (sells) stocks with high (low) predicted jump probabilities earns annualized mean returns of 9.4% and four-factor alphas of 8.1%. This strategy’s returns are larger when there are greater limits to arbitrage. These results are consistent with investor aversion to idiosyncratic jump risk.

Campbell, Hilscher, and Szilagyi (2008) show that firms with a high probability of default have abnormally low average future returns. We show that firms with a high potential for default (death) also tend to have a relatively high probability of extremely large (jackpot) payoffs. Consistent with an investor preference for skewed, lottery-like payoffs, stocks with high predicted probabilities for jackpot returns earn abnormally low average returns. Stocks with high death or jackpot probabilities have relatively low institutional ownership and the jackpot effect we find is much stronger in stocks with high limits to arbitrage.

We show that a common component governs volatility dynamics across a wide range of traded equity factors. This “common factor volatility” (CFV) exists even among orthogonal factors. CFV occurs in both cash-flow and discount-rate components of factor returns and derives from market responses to fundamental news rather than underlying commonality in news volatility. Incorporating CFV improves factor volatility forecasts relative to models that include only own-factor volatility. CFV allows us to characterize stochastic discount factor (SDF) volatility dynamics in a very general sense and we show that many popular models imply SDFs with time-varying volatility that correlates strongly with CFV.

We examine the ability of ratings and market-based measures to predict defaults. Although market-based measures are more accurate at horizons up to one year, ratings complement market-based measures and are not redundant in predicting defaults across horizons. Market-based measures differ from ratings in that they respond to both cash-flow and discount-rate news, while ratings respond primarily to cash-flow news, which is more informative of future defaults. Ratings ignore transitory shocks to credit risk, while market-based measures do not. Rating agencies respond to transitory shocks with watches rather than downgrades. Ratings are more informative during expansions and for speculative grade firms.

Stocks that hedge sustained market downturns should have low expected returns, but they do not. We use ex ante firm characteristics and covariances to construct a tradable safe minus risky (SMR) portfolio that hedges market downturns out of sample. Although downturns (peaks to troughs in market index levels at the business-cycle frequency) predict significant declines in gross domestic product growth, SMR has significant positive average returns and 4-factor alphas (both around 0.8% per month). Risk-based models do not explain SMR’s returns, but mispricing does. Risky stocks are overpriced when sentiment is high, resulting in subsequent returns of -0.9% per month.