Common risk metrics reported in academia include volatility, skewness, and factor exposures. The maximum drawdown statistic is rarely calculated, perhaps because it is path dependent and estimated with greater uncertainty. In practice, however, asset managers and fiduciaries routinely use the...

Impact costs occur when large buy or sell orders move market prices. The measurement of these costs is crucial for the evaluation of potential trading strategies as well as the successful execution of systematic investment strategies. However, common approaches suffer from a type of myopia:...

We now know that research findings favorable to the sponsor of the research should be discounted on the grounds of conflict of interest (e.g., tobacco companies or pharma companies). Incentives distort research findings. Is the same true in the field of finance? I argue that economic incentives...

Incentives distort research findings. We now know that research findings favorable to the sponsor of the research should be discounted on the grounds of conflict of interest (e.g., tobacco companies or pharma companies). Is the same true in the field of finance? I argue that economic incentives...

Both Kosowski et al. (2006) and Fama and French (2010) evaluate whether mutual funds outperform, but their conclusions are very different. We reconcile their findings. We show that the Fama and French method suffers from an undersampling problem that leads to a failure to reject the null...

We provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 21% share of total crypto trading volume in 2021. We discuss a wide variety of tokens,...

Gold objects have existed for thousands of years but for many investors gold has only recently become a tradable investment opportunity. Gold has been described as an inflation hedge, a “golden constant”, with a long run real return of zero. Yet over 1, 5, 10, 15 and 20 year investment...

Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, the danger of misapplying these techniques can lead to disappointment. One crucial limitation involves data availability. Many of machine...

We apply state-of-the-art Bayesian machine learning to test whether we can extract valuable information from analysts' recommendations of stock performance. We use a probabilistic model for independent Bayesian classifier combination that has been successfully applied in both the physical and...