Using historical data that spans almost 150 years, we examine whether there is a long-run equilibrium relationship between the stock's earnings and bond yields. The novelty of our econometric methodology consists in using a vector error correction model where we allow multiple structural breaks...

Most retirement withdrawal rate studies are either based on historical data or use a particular assumption about portfolio returns unique to the study in question. But planners may have their own capital market expectations for future returns from stocks, bonds, and other assets they deem...

We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of additional and independent data. Our database contains data on stock prices, dividends and hand-collected market capitalizations for 1,488 major stocks between 1866-1926. Results...

This paper provides a model to interpret the relative behavior of expected returns of high- and low-resilience assets from the time of the COVID-19 pandemic, including a novel definition of disaster based on COVID-19 intensity. The setup allows us to disentangle the probability of disaster and...

The paper investigates the determinants of the idiosyncratic volatility puzzle by allowing linkages across asset returns. The first contribution of the paper is to show that portfolios sorted by increasing indegree computed on the network based on Granger causality test have lower expected...

We examine the connection between tail risk — as measured in Kelly and Jiang (2014) — and the cross-section of expected returns. In conditional predictive regression systems and vector-autoregressions of the market portfolio and the long- and shoresides of the Fama-French factor portfolios,...

We outline a framework in which accounting “valuation anchors" could be connected to expected stock returns. Under two general conditions, expected log returns is a log- linear function of a valuation (market value-to-accounting) multiple and the expected growth in the valuation anchor. We...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

Cross-predictability denotes the fact that some assets can predict other assets' returns. I propose a novel performance-based measure that disentangles the economic value of cross-predictability into two components: the predictive power of one asset's signal for other assets' returns...