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A Hybrid Genetic Algorithm with Learning-to-Rank-to-Optimization for US Equity Portfolio Construction

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  • Ferdinantos Kottas

    (School of Business, National University of Ireland Maynooth, W23 F2H6 Maynooth, Ireland
    School of Engineering, Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
    Department of Computer Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

This study develops and evaluates an equity selection pipeline that converts quarterly fundamentals into a monthly frequency, constructs profitability, leverage, liquidity, and growth characteristics, and learns a linear ranking model via a genetic algorithm (GA). The GA is trained to maximize either (i) mean monthly NDCG@30 using 12-tile relevance labels or (ii) mean monthly Spearman information coefficient (IC). The learned ranker is tested out-of-sample using monthly forward returns, benchmarked against the S&P 500, with different types of allocation weights, and further evaluated under sector concentration limits. In the last layer, the monthly-selected stock universe is used in a daily dynamic allocation which is solved by the penalized Max-Sharpe or Min-Variance optimization problems under only long positions and transaction fees. Performance is examined across Pre-COVID, COVID, Post-COVID (Train), and Final Test regimes, demonstrating how ranking objectives and diversification constraints impact performance and stability. Results show that TTM-based accounting signals, when optimized through genetic learning and disciplined allocation, yield economically meaningful stock selection and robust portfolio performance across market regimes.

Suggested Citation

  • Ferdinantos Kottas, 2026. "A Hybrid Genetic Algorithm with Learning-to-Rank-to-Optimization for US Equity Portfolio Construction," IJFS, MDPI, vol. 14(4), pages 1-46, April.
    • Handle: RePEc:gam:jijfss:v:14:y:2026:i:4:p:95-:d:1914133
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