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In the quantitative hedge funds industry, alphas are the foundation of trading strategies. They are trading signals, mostly market or factor neutrals, as opposed to beta (or smart beta) strategies that have directional exposure. While the construction of those alphas is kept secret, the process of building trading strategies often relies on combining them. Indeed the main idea behind been factor neutral is to reduce the correlation among the strategies and benefit from diversification.

First, let's get rid of terminology. In this article, alphas are the exposure we are willing to take on one universe of tradable assets to generate…

Careful, It is all about prior

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Quantitative finance is all about finding trading strategies using past data to generate returns in the future. In a machine learning dialect, futures returns are by definition out-of-sample so any trading strategy that (out)performs out-of-sample is the Graal.

A few years ago a paper with a new portfolio technic has been released, Building Diversified Portfolios that Outperform Out-of-Sample. The paper has been downloaded thousands of times and is ranked in the top 300 most viewed papers on SSRN.

The author introduces the Hierarchical Risk Parity (HRP) portfolio and claims its superiority to minimum variance portfolios in producing better out-of-sample returns…

James Rafco

A random quantitative researcher.

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