A Reinforcement Learning Framework for Portfolio Management Problem Leveraging Stocks Historical Data And Their Correlation

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Taherkhani, Hamed | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 56219 (44)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Fazli, Mohammad Amin
Abstract:
Over the past few years, deep reinforcement learning(DRL) has been given a lot of attention in finance for portfolio management. With the help of experts’ signals and historical price data, we have developed a new reinforcement learning(RL) method. The use of experts’ signals in tandem with DRL has been used before in finance, but we believe this is the first time this method has been used to solve the financial portfolio management problem. As our agent, we used the Proximal Policy Optimization(PPO) algorithm to process the reward and take actions in the environment. Our framework comprises a convolutional network to aggregate signals, a convolutional network for historical price data, and a vanilla network. The results suggested that, on average, our framework could gain 90 percent of the profit earned by the best expert.
Keywords:
Reinforcement Learning ; Portfolio Management ; Deep Reinforcement Learning ; Financial Market ; Deep Learning ; Optimal Fiscal Strategy