Building Cross-Sectional Trading Strategies via Geometric Semantic Genetic Programming

EasyChair Preprint 15909

Abstract

Cross-sectional trading strategies involves constructing portfolios by comparing expected performance of assets within a group, typically using predicted returns. In this study, we frame the estimation of cross-sectional expected returns as a symbolic regression problem, and investigate the predictive capabilities of geometric semantic genetic programming in developing cross-sectional trading strategies in the U.S. stock market. We employ standard genetic programming and other common methods used for studying cross-sectional returns as baselines for comparison. Our findings indicate that geometric semantic genetic programming provides better forecast accuracy, portfolio performance, and ranking accuracy than standard genetic programming. Furthermore, we show the limitations of errors-based metrics as performance measurement in cross-sectional trading strategies.

Keyphrases: Geometric Semantic Genetic Programming, Stock Returns Prediction, portfolio construction, stock selection

@booklet{EasyChair:15909,
  author    = {Kritpol Bunjerdtaweeporn and Alberto Moraglio},
  title     = {Building Cross-Sectional Trading Strategies via Geometric Semantic Genetic Programming},
  howpublished = {EasyChair Preprint 15909},
  year      = {EasyChair, 2025}}