Virtually all journal articles in the factor investing literature make associational claims, in
denial of the causal content of factor models. Authors do not identify the causal graph …

In this paper we address the problem of selection bias under multiple testing in the context of
investment strategies. We introduce an unsupervised learning algorithm that determines the …

The emerging or at least threatening “significance test crisis” in accounting has been
prompted by a chorus across multiple physical and social sciences of dissatisfaction with …

Recent progress in causal inference has opened a path, however difficult, for advancing
financial economics beyond its current phenomenological stage. This article proposes a …

Most discoveries in empirical finance are false, as a consequence of selection bias under
multiple testing. In this paper, we present a real example of how multiple testing information …

Virtually all journal articles in the factor investing literature make associational claims,
instead of causal claims. Authors do not identify the causal graph consistent with the …

Machine Learning and AI in Finance Page 1 i Machine Learning and AI in Finance The
significant amount of information available in any field requires a systematic and analytical …

Recent progress in causal inference has opened a path, however difficult, for advancing
financial economics beyond its current phenomenological stage. The goal of this article is to …

Goal: The paper examines the fundamental theoretical prerequisites for the use of machine
learning in business valuation. Methods: The study demonstrates that the use of statistical …

Alternatives to traditional cap-weighted indexes have been growing in popularity as
investors strive to add diversification using a transparent, mechanical, and low-cost …