Stochastic Pathways of Long-Term Investing: A Control, Learning, and Search Perspective

Author(s): Shenggang Li

Originally published on Towards AI.

Recasting Incremental Portfolio Strategies through Reinforcement Learning, Inverse Reward Inference, and Monte Carlo Search

Investing has always been marked by fragmentation. Decisions are often made in pieces — one stock today, another tomorrow — guided by scattered news, personal intuition, or market rumors. While such step-by-step reasoning seems natural, it risks becoming reactive and unstructured, making it difficult to see how small daily moves align with a coherent long-term strategy.

This article presents a comprehensive view of how incremental investing can be optimized using principles from reinforcement learning, inverse reward inference, and Monte Carlo search. It explains the fragmentation in traditional investment strategies and introduces a cohesive framework for decision-making that utilizes stochastic control and simulation methods to guide investors in making disciplined, long-term financial choices.

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Published via Towards AI