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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
REILLY-COLIN-THESIS.pdf | 1.45 MB | Adobe PDF | Request a copy |
Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.