Dead Alphas as Risk Factors

TL;DR

This paper presents an algorithm and source code to extract risk factors from dead alphas, helping to identify unprofitable or volatile directions in stock return space to enhance the performance of tradable alphas.

Contribution

It introduces a novel method for leveraging dead alphas to improve alpha performance by identifying non-profitable or volatile directions in stock returns.

Findings

Dead alphas can be used to extract meaningful risk factors.

The method improves the robustness of tradable alphas.

Care is needed when the number of dead alphas exceeds the number of stocks.

Abstract

We give an explicit algorithm and source code for extracting equity risk factors from dead (a.k.a. "flatlined" or "hockey-stick") alphas and using them to improve performance characteristics of good (tradable) alphas. In a nutshell, we use dead alphas to extract directions in the space of stock returns along which there is no money to be made (and/or those bets are too volatile). In practice the number of dead alphas can be large compared with the number of underlying stocks and care is required in identifying the aforesaid directions.