In recent years, Machine Learning (ML) has been able to master tasks that until now only a few human experts could perform.Some of the most successful hedge funds in history apply ML every day. However, myths about Financial ML have proliferated:a) The Sisyphus paradigm is applicable to ML,b) ML...

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...

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...

Several features of financial research make it particularly prone to the occurrence of false discoveries. First, the probability of finding a positive (profitable investment strategy) is very low, due to intense competition. Second, true findings are mostly short-lived, as a result of the...

Financial systems rarely allow experimentation. For example, we cannot reproduce the flash crash of 2010 while controlling for environmental conditions. As a result, much financial research relies on the statistical analysis of finite (historical) datasets, where: (a) Time series datasets are...

In this article, the authors present a conceptual framework named 'Adaptive Seriational Risk Parity' (ASRP) to extend Hierarchical Risk Parity (HRP) as an asset allocation heuristic. The first step of HRP (quasi-diagonalization) determining the hierarchy of assets is required for the actual...

In this paper, the authors construct a pipeline to benchmark Hierarchical Risk Parity (HRP) relative to Equal Risk Contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage ("volatility target"). The authors use...

In the field of mathematical finance, a “backtest” is the usage of historical market data to assess the performance of a proposed trading strategy. It is a relatively simple matter for a present-day computer system to explore thousands, millions or even billions of variations of a proposed...

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