Convex optimization solutions tend to be unstable, to the point of entirely offsetting the benefits of optimization. For example, in the context of financial applications, it is known that portfolios optimized in-sample often underperform the naïve (equal weights) allocation out-of-sample. This...

Traditionally, the development of investment strategies has required domain-specific knowledge as well as access to restricted datasets. This has meant that investment opportunities are not researched by the majority of data scientists, because they lack either or both of these requirements. In...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Economics (and by extension finance) is arguably one of the most mathematical fields of research. However, economists' choice of math may be inadequate to model the complexity of social institutions. In a constructive spirit, this note offers some advice on how students could increase their...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Traditionally, the development of investment strategies has required domain-specific knowledge and access to restricted datasets. These two barriers exist by design: (a) Financial knowledge is hoarded by firms, and protected as trade secrets, and (b) Financial data is expensive, making it...

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 consistent with the observed sample, they justify their chosen model specification in associational terms, and...

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 propose a hierarchy of empirical evidence, recognizing that not all types of observations have the same scientific...

In a number of applications, data may be anonymized, obfuscated, or highly noisy. In such cases, it is difficult to use domain knowledge or low-dimensional visualizations to engineer the features for tasks such as machine learning, instead, we explore dimensionality reduction (DR) as a...

In this article, I advocate for the use of causal graphs to modernize the field of factor investing, and set it on a logically-coherent foundation. In order to do that, first I must introduce the concepts of association and causations. Second, I explain the use of causal graphs and the real...