Decoding Stock Market with Quant Alphas

TL;DR

This paper introduces a new algorithm for trading stock market alphas directly from expected returns, eliminating the need for combining alphas into weighted portfolios, thereby reducing costs and noise.

Contribution

The authors present a novel method for trading alphas without combining them into alpha combos, resulting in cost savings and reduced noise compared to traditional approaches.

Findings

Substantial cost savings by avoiding alpha combos

Reduced noise and suboptimality in trading strategies

Independent derivation of the algorithm from alpha risk models

Abstract

We give an explicit algorithm and source code for extracting expected returns for stocks from expected returns for alphas. Our algorithm altogether bypasses combining alphas with weights into "alpha combos". Simply put, we have developed a new method for trading alphas which does not involve combining them. This yields substantial cost savings as alpha combos cost hedge funds around 3% of the P&L, while alphas themselves cost around 10%. Also, the extra layer of alpha combos, which our new method avoids, adds noise and suboptimality. We also arrive at our algorithm independently by explicitly constructing alpha risk models based on position data.