We measure the loss potential of Hedge Funds by combining three market risk measures: VaR, Draw-Down and Time Under-The-Water. Calculations are carried out considering three different frameworks regarding Hedge Fund returns: i) Normality and time-independence, ii) Non-normality and...

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...

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become...

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...

The purpose of our work is to show that, in the near future, Quantum Computing algorithms may solve many currently intractable financial problems, and render obsolete some existing mathematical approaches

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...

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 observed phenomenon, they justify their chosen model specification in terms of correlations, and they do not propose...