{"ID":2864535,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23062","arxiv_id":"2509.23062","title":"Data-Driven Long-Term Asset Allocation with Tsallis Entropy Regularization","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.23062","url_pdf":"https://arxiv.org/pdf/2509.23062v1","authors":"[\"Haoran Zhang\",\"Wenhao Zhang\",\"Xianping Wu\"]","published":"2025-09-27T02:34:05Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false}