Portfolio Optimization Using Reinforcement Learning

Owuor, Hezi; Davis, Jason; Schwarzrock, Erich

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Portfolio Optimization Using Reinforcement Learning

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In this report, we analyze the effect of data complexity on the performance of financial reinforcement learning models. We created six models which were identical except for the complexity of their learning data. The goal for each was to make as much money as possible by investing in only a single stock. We trained these models on daily index fund data, intraday index fund data, and daily foreign exchange data. We then analyzed the effect that the different data complexities had on both the training and testing returns. Simpler models cannot learn anything and will perform poorly, while if a model is too complex, the agents will overfit the training data and perform poorly on testing data. State spaces with moderate complexity tend to perform the best.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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