There are three fundamental ways of testing the validity of an investment algorithm against historical evidence: a) the walk-forward method; b) the resampling method; and c) the Monte Carlo method. By far the most common approach followed among academics and practitioners is the walk-forward...

Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. In this paper we propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations...

Mean-Variance portfolios are optimal in-sample, however they tend to perform poorly out-of-sample (even worse than the 1/N naïve portfolio!) We introduce a new portfolio construction method that substantially improves the Out-Of-Sample performance of diversified portfolios.The full paper is...

Classical statistics (e.g., Econometrics) relies on assumptions that are often unrealistic in finance. Two critical assumptions are that the researcher has perfect knowledge about the model's specification, and that the researcher knows all the variables involved in a phenomenon (including all...

Many quantitative firms have suffered substantial losses as a result of the COVID-19 selloff. In this note, we highlight three lessons that quantitative researchers could learn from this crisis. First, researchers should develop more nowcasting methods, and pay less attention to forecasts....

Six weeks after becoming a pandemic, COVID-19 has caused over 150,000 deaths across 210 countries. Governments around the world have instituted universal lockdowns to curve the spread of this serious disease. While it is obvious that extended universal lockdowns have saved lives that otherwise...

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory, rather than on back-testing potential trading...

When used incorrectly, the risk of machine learning (ML) overfitting is extremely high. However, ML counts with sophisticated methods to prevent: (a) train set overfitting, and (b) test set overfitting.Thus, the popular belief that ML overfits is false. A more accurate statement would be that:...