We provide some new tools to evaluate trading strategies. When it is known that many strategies and combinations of strategies have been tried, we need to adjust our evaluation method for these multiple tests. Sharpe Ratios and other statistics will be overstated. Our methods are simple to...

Hundreds of papers and hundreds of factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make any economic or statistical sense to use the usual significance criteria for a newly discovered factor, e.g., a t-ratio greater than 2.0....

The multiple testing problem plagues many important issues in finance such as fund and factor selection. Many look good purely by luck. There are a number of statistical techniques to control for multiplicity that reduce Type I errors - but it is unknown by how much. We propose a new way to...

Identifying the factors that drive the cross-section of expected returns is challenging for at least three reasons. First, the choice of testing approach (time-series versus cross-sectional) will deliver different sets of factors. Second, varying test portfolio sorts changes the importance of...

When evaluating a trading strategy, it is routine to discount the Sharpe ratio from a historical backtest. The reason is simple: there is inevitable data mining by both the researcher and by other researchers in the past. Our paper provides a statistical framework that systematically accounts...

We propose a new way to conduct multiple hypothesis testing in economics research. Our framework allows for correlation among tests and incomplete data, both of which are prevalent in economic meta-analysis. Our simulations show that that our method is able to produce the correct p-value cutoff...

Harvey, Liu, and Zhu (2016) argue that a large proportion of published asset-pricing factors are likely false. Researchers may try many variables and report only the significant ones, so-called p-hacking. Some recent work challenges the prevalence of p-hacking and argues that the amount of...

We contrast the time-series and cross-sectional performance of three popular investment strategies: carry, momentum and value. While considerable research has examined the performance of these strategies in either a directional or cross-asset settings, we offer some insights on the market...

In the late stages of long bull markets, a popular question arises: What steps can an investor take to mitigate the impact of the inevitable large equity correction? However, hedging equity portfolios is notoriously difficult and expensive. We analyze the performance of different tools that...

Stock characteristics have two sources of predictive power. First, a characteristic might be valuable in identifying high or low expected returns across industries. Second, a characteristic might be useful in identifying individual stock expected returns within an industry. Past studies...