We provide the first systematic evidence on the link between long‐short anomaly portfolio
returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of …

After a natural experiment is first used, other researchers often reuse the setting, examining
different outcome variables. We use simulations based on real data to illustrate the multiple …

Using new data from US and global markets, we revisit market risk premium predictability by
equity anomalies. We apply a repertoire of machine-learning methods to forty-two countries …

In almost every area of empirical finance, researchers confront multiple tests. One high-
profile example is the identification of outperforming investment managers, many of whom …

In this paper, we empirically evaluate US market return predictability based on an aggregate
measure constructed from the bottom-up firm-level cash conversion cycle (CCC) for 1976 …

Short selling efficiency (SSE), measured each month by the slope coefficient of cross-
sectionally regressing abnormal short interest on a mispricing score, significantly and …

We compare the performance of the linear regression model, which is the current standard
in science and practice for cross-sectional stock return forecasting, with that of machine …

We provide strong empirical evidence for time-series predictability of the intraday return on
the aggregate market portfolio by exploiting lagged high-frequency cross-sectional returns …

Based on 10 commonly used factors, we construct a novel factor momentum strategy in the
Chinese stock market, which earns an annualized return of 9.91%, with a Sharpe ratio of …

I exploit the leveraged exchange-traded funds'(ETFs') primary market to measure aggregate,
uninformed, gambling-like demand, that is, speculation sentiment. The leveraged ETFs' …