Alpha Mining and Enhancing via Warm Start Genetic Programming for Quantitative Investment

TL;DR

This paper introduces a novel genetic programming framework with warm start initialization and structural constraints to improve stock alpha discovery, resulting in better prediction accuracy and higher returns in Chinese stock markets.

Contribution

It presents a new GP method that enhances search efficiency and interpretability for alpha factor discovery, outperforming traditional approaches.

Findings

Superior out-of-sample prediction results

Higher portfolio returns than benchmarks

Effective focus on promising search regions

Abstract

Traditional genetic programming (GP) often struggles in stock alpha factor discovery due to its vast search space, overwhelming computational burden, and sporadic effective alphas. We find that GP performs better when focusing on promising regions rather than random searching. This paper proposes a new GP framework with carefully chosen initialization and structural constraints to enhance search performance and improve the interpretability of the alpha factors. This approach is motivated by and mimics the alpha searching practice and aims to boost the efficiency of such a process. Analysis of 2020-2024 Chinese stock market data shows that our method yields superior out-of-sample prediction results and higher portfolio returns than the benchmark.