We evaluate the probability that an estimated Sharpe ratio exceeds a given threshold in presence of non-Normal returns. We show that this new uncertainty-adjusted investment skill metric (called Probabilistic Sharpe ratio, or PSR) has a number of important applications: First, it allows us to...

Portfolio optimization is one of the problems most frequently encountered by financial practitioners. To our knowledge, the Critical Line Algorithm (CLA) is the only algorithm specifically designed for inequality-constrained portfolio optimization problems, which guarantees that the exact...

* It has been estimated that the current size of the asset management industry is approximately US$58 trillion.* Portfolio optimization is one of the problems most frequently encountered by financial practitioners. It appears in various forms in the context of Trading, Risk Management and...

Because the Sharpe ratio only takes into account the first two moments, it wrongly “translates” skewness and excess kurtosis into standard deviation.As a result: It deflates the skill measured on “well-behaved” investments (positive skewness, negative excess kurtosis). It inflates the...

The problem of capital allocation to a set of strategies could be partially avoided or at least greatly simplified with an appropriate strategy approval decision process. This paper proposes such a procedure.We begin by splitting the capital allocation problem into two sequential stages:...

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...

Correlation matrices are ubiquitous in finance. Some key applications include portfolio construction, risk management, and factor/style analysis. Correlation matrices are usually estimated from historical empirical observations or derived from historically estimated factors. It is widely...

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...

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...