LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction

arXiv cs.LG / 5/4/2026

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Key Points

The paper introduces LambdaRankIC, a learning-to-rank method designed to directly optimize Rank IC (Spearman rank correlation between predictions and realized returns) for financial prediction tasks.
It derives closed-form lambda gradients from pairwise rank swaps to avoid the non-differentiability of the ranking operator, enabling efficient gradient-based training within the LambdaRank framework.
The authors implement LambdaRankIC as a custom objective in XGBoost and provide a theoretical result showing it optimizes an upper bound on Rank IC.
Experiments on both simulated and real financial datasets indicate LambdaRankIC outperforms regression-based and NDCG-oriented ranking approaches, improving Rank IC and related finance metrics such as ICIR, monthly return, and Sharpe ratio.
Overall, the results suggest that when full-order ranking quality is the main objective, training directly for Rank IC can deliver substantial gains over conventional loss functions.

Abstract

In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the model predictions and the realized asset returns. Despite its wide adoption, most existing models are trained using regression losses or ranking objectives that may not align with Rank IC. We propose LambdaRankIC, a novel learning-to-rank approach that directly optimizes Rank IC. We circumvent the non-differentiability of the ranking operator by deriving the closed-form expression for the lambda gradients induced by the pairwise rank swaps, which enables efficient gradient-based optimization within the LambdaRank framework. We implement LambdaRankIC as a custom objective in XGBoost. Theoretically, we show that our approach optimizes an upper bound on Rank IC. We evaluate the proposed approach on both simulated and real-world financial data. In simulation studies, LambdaRankIC accurately recovers the true ranking structure in noiseless settings and consistently outperforms regression-based and NDCG-oriented ranking methods under low signal-to-noise ratios and heavy-tailed noise regimes. In empirical experiments using real market data, LambdaRankIC achieves the best out-of-sample performance on evaluation metrics commonly used in finance, including Rank IC, ICIR, monthly return, and Sharpe ratio. These results show that directly optimizing Rank IC can yield substantial improvements over conventional learning objectives in financial predictions when the full-order ranking quality is the primary goal.