Forthcoming Articles

Is There Smart Money? How Information in the Commodity Futures Market Is Priced into the Cross-Section of Stock Returns with Delay

Steven Wei Ho and Alexandre R. Lauwers

We document a new empirical phenomenon in which the aggregate positions of money managers, who are sophisticated speculators in the commodity futures market, as disclosed by the Disaggregated Commitments of Traders reports, can predict the cross-section of commodity producers’ stock returns in the subsequent week. We employ a number of cross-sectional methods, including calendar-time regression analysis, single-sort, double-sort, and Fama–MacBeth regressions, to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry. We thus add more empirical evidence to the literature on costly information processing, which leads to gradual information diffusion across asset markets.

Media Sentiment and Currency Reversals

Ilias Filippou, Mark P. Taylor, and Zigan Wang

Analyzing 48 foreign exchange (FX) rates and 1.2 million FX-related news articles over a 35-year period, using digital textual analysis, we find that a currency reversal investment strategy that buys (sells) currencies with low (high) media sentiment offers strong positive and statistically significant returns and Sharpe ratios. The results are robust and the strategy adds value over other currency premia determinants. Analysts’ forecasts systematically mispredict the reversal strategy. This is the first paper to show that price reversals based on media sentiment are a well-defined feature of the foreign exchange market.

Anomaly Discovery and Arbitrage Trading

Xi Dong, Qi Liu, Lei Lu, Bo Sun, and Hongjun Yan

We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.

Business Cycles, Regime Shifts, and Return Predictability

Wei Yang

Consistent with the empirical properties of the consumption data, I develop a model in which consumption and dividend growth follow regime-switching dynamics. I show that regime-shift risk is priced in the model. Regime-shift risk exhibits dominant influence on asset prices: It generates a high equity premium and also induces time-varying risk premiums. The model explains major business cycle dependent asset market phenomena and, in particular, the stronger predictability of stock returns during recessions.

Heterogeneity of Beliefs and Trading Behavior: A Reexamination

Sascha Füllbrunn, Christoph Huber, Catherine Eckel, and Utz Weitzel

Combining experimental data sets from seven individual studies, including 255 asset markets with 2,031 participants, and 36,326 short-term price forecasts, we analyze the role of heterogeneity of beliefs in the organization of trading behavior by reproducing and reconsidering earlier experimental findings. Our results confirm prior evidence that price expectations affect trading behavior. However, heterogeneity in beliefs does not seem to drive overpricing and asset market bubbles, as suggested by earlier studies, and we find no indication of short-term beliefs being better determinants of trading behavior than longer-term beliefs.

Inferring Aggregate Market Expectations from the Cross-Section of Stock Prices

Turan Bali, Craig Nichols, and David Weinbaum

We introduce a new approach to estimating long-term aggregate discount rates using the crosssection of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of historical market return variation; in contrast, shocks to other discount rate measures account for no more than two percent. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets.

One Vol to Rule Them All: Common Volatility Dynamics in Factor Returns

Nishad Kapadia, Matthew Linn, and Bradley Paye

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.

The Only Constant Is Change: Non-Constant Volatility and Implied Volatility Spreads

Timothy Colin Campbell and Alex Petkevich

We examine the predictability of stock returns using implied volatility spreads (VS) from individual (non-index) options. Volatility spreads can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms’ sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms’ sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity.