Data-Driven Long-Term Asset Allocation with Tsallis Entropy Regularization

Haoran Zhang; Wenhao Zhang; Xianping Wu

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arXiv · 2509.23062

Data-Driven Long-Term Asset Allocation with Tsallis Entropy Regularization

Haoran Zhang·Wenhao Zhang·Xianping Wu

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Abstract

This paper addresses the problem of dynamic asset allocation under uncertainty, which can be formulated as a linear quadratic (LQ) control problem with multiplicative noise. To handle exploration exploitation trade offs and induce sparse control actions, we introduce Tsallis entropy as a regularization term. We develop an entropy regularized policy iteration scheme and provide theoretical guarantees for its convergence. For cases where system dynamics are unknown, we further propose a fully data driven algorithm that estimates Q functions using an instrumental variable least squares approach, allowing efficient and stable policy updates. Our framework connects entropy-regularized stochastic control with model free reinforcement learning, offering new tools for intelligent decision making in finance and automation.

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BibTeX RIS

Haoran Zhang, Wenhao Zhang, Xianping Wu. 2025-09-27. Data-Driven Long-Term Asset Allocation with Tsallis Entropy Regularization. https://arxiv.org/abs/2509.23062

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