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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01xd07gw89b
Title: Portfolio Management Via Deep Reinforcement Learning
Authors: Reilly, Colin
Advisors: Lee, Jason
Department: Electrical and Computer Engineering
Class Year: 2022
Abstract: The stock market is a data filled environment that cannot physically be iterated through by a human in order to find the best opportunities. Therefore, this paper investigates the strategies that utilize deep reinforcement learning, specifically Deep Q Networks (DQN), to not only optimally trade one stock but all of the constituents of the S&P 100 index for just under a five year period (January 6, 2017 to November 29,2021). The models for single stock analysis were capable of having a higher return than that of the stock they followed, returning over 100% regardless of the starting hyper-parameter, and the combined model was able to beat the S&P 100's growth with an unbounded model, returning 362.23%, and compete with the S&P 100 when limitations were placed upon it, returning 89.55%.
URI: http://arks.princeton.edu/ark:/88435/dsp01xd07gw89b
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Electrical and Computer Engineering, 1932-2023

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