We develop a detailed epidemiological multi-factor model, the K-Susceptible-Exposed-Infected-Removed (K-SEIR) model, as well as several simpler sub-models, as its building blocks. The general model enables us to account for all the relevant COVID-19 features, its disparate impact on different...

We investigate the use of machine learning (ML) and other robust-estimation techniques in event studies conducted on single securities for the purpose of securities litigation. Single-firm event studies are widely used in civil litigation, with billions of dollars in settlements hinging on the...

We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform...

We introduce two online backtest overfitting tools: BODT simulates the overfitting of seasonal strategies (typical of technical analysis), and TMST simulates the overfitting of econometric strategies (typical of academic journals). We show that econometric methods lend themselves to extreme...

Selection bias under multiple testing is a serious problem. From a practitioner's perspective, failure to disclose the impact of multiple tests of a proposed investment strategy to clients and senior management can lead to the adoption of a false discovery. Clients will lose money, senior...

I have divided this testimony into four sections, which discuss: (1) several types of automation currently being deployed in capital markets and the financial sector, and how they affect decision-making; (2) how machine learning (ML) and automation can help and hurt workers by disruption of the...

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...

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...

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...