Abstract: Rather than merely measuring the timing of cash-flows, equity duration is also driven by stock-specific discount rates. We find that established empirical measures of equity duration predict returns mechanically because they use market prices, i.e. functions of the stock's true discount rate (that may reflect mispricing). We propose new measures of cash-flow timing that are not susceptible to this critique. These discount-rate free measures are better predictors of cash-flow timing but - in contrast to established, discount-rate contaminated measures - indicate an unconditionally flat relationship between cash-flow timing and average returns. However, in recessions (expansion episodes), there is a negative (positive) relation between cash-flow timing and average stock returns. These timing premia can be explained by the joint cross-section of profitability, investment, market capitalization, and beta.
Abstract: I analyze the cash flows of 174 anomalies, categorized into accounting and non-accounting, to explore the reasons behind their abnormal returns. Tracking their cash flow growth at both the firm and anomaly levels reveals distinct patterns: accounting anomalies show procyclical growth, aligning with risk-based models. In contrast, non-accounting anomalies exhibit countercyclical growth and thus provide a hedge against economic downturns. These findings suggest diverse causes for anomalies, indicating the need for diļ¬erent explanations for each category.
Discussant: Christian Opp, University of Rochester
Abstract: We develop a transparent Bayesian framework to measure uncertainty in asset pricing models. Our framework quantifies the tradeoff between mean-variance efficiency and parsimony for models to attain high posterior probabilities. Model uncertainty is defined as the entropy of these posterior probabilities, which is consistently interpretable even under misspecification due to omitted factors. Empirically, model uncertainty accumulates during major market events, carrying a significantly negative risk premium of approximately half the magnitude of the market. Positive shocks to model uncertainty predict persistent outflows from US equity funds and inflows to Treasury funds.
Abstract: We extend traditional tests of factor models to incorporate nonlinearities. The metric for model evaluation becomes the Sharpe ratio of the mimicking portfolio of a nonlinear stochastic discount factor (SDF) pricing the model factors. Empirically, we investigate the implications of an economically meaningful family of nonlinear SDFs for evaluating popular factor models. We find that, relative to the linear case, introducing nonlinearities substantially improves pricing performance and changes rankings among competing models. The preferred model depends on the test assets: unlike the linear approach, test assets are relevant for model comparison as they are needed to mimic nonlinearities in the factors.
Discussant: Mirela Sandulescu, University of Michigan
