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 …

Non-standard errors capture variation due to differences in research design choices. We
document large variation in design choices in the context of asset pricing factor models and …

In statistics, samples are drawn from a population in a data‐generating process (DGP).
Standard errors measure the uncertainty in estimates of population parameters. In science …

We employ a repertoire of machine learning models to investigate the cross-sectional return
predictability in cryptocurrency markets. While all methods generate substantial economic …

Motivated by recent findings from the equity market, we investigate non-standard errors in
cryptocurrency research. We examine ten prevalent decisions related to data sources …

Is there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data
from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the …

This textbook shows how to bring theoretical concepts from finance and econometrics to the
data. Focusing on coding and data analysis with R, we show how to conduct research in …

We propose a theoretical framework that characterizes the diversity of outcomes in empirical
studies, depending on the nature and number of design choices that researchers make. We …

We argue that spanning large numbers of degrees of freedom in empirical analysis allows
better characterizations of effects and thus improves the trustworthiness of conclusions. Our …

In statistics, samples are drawn from a population in a data-generating process (DGP).
Standard errors measure the uncertainty in estimates of population parameters. In science …