A typical empirical study involves choosing a sample, a research design, and an analysis
path. Variation in such choices across studies leads to heterogeneity in results that introduce …

Data analysis is challenging as analysts must navigate nuanced decisions that may yield
divergent conclusions. AI assistants have the potential to support analysts in planning their …

We propose a framework for evaluating reproducibility and replicability in economics.
Reproducibility is defined as testing if the results of an original study can be reproduced …

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 …

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500
articles published in the journal Management Science before and after the introduction of a …

This study explores how researchers' analytical choices affect the reliability of scientific
findings. Most discussions of reliability problems in science focus on systematic biases. We …

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 …

Financial professionals play a key role in financial markets and the financial industry as a
whole. Researchers in experimental economics and finance have therefore started to …

We discuss a relatively new meta-scientific research design: many-analyst studies that
attempt to assess the replicability and credibility of research based on large-scale …

Many-analysts studies explore how well an empirical claim withstands plausible alternative
analyses of the same dataset by multiple, independent analysis teams. Conclusions from …