Maximizing Returns with Reinforcement Learning: A Paradigm Shift in Stock Market Portfolio Management

Abstract

This report introduces an innovative stock market portfolio management approach, utilizing reinforcement learning and sentiment analysis techniques. Unlike traditional methods, which rely on time-consuming fundamental and technical analysis susceptible to human biases, our proposed approach leverages machine learning advancements such as DQN, DDQN, and Dueling DQN algorithms, combined with sentiment analysis to automate and enhance portfolio decisions. This study introduces a novel framework that combines reinforcement learning with multiple methods and sentiment analysis for stock market portfolio management. We have used Sensex NSE stock data comprising of historical data of open, low, high, close prices from 2010 to 2023. Experimental results demonstrate that our approach outperforms traditional portfolio management methods regarding returns and risk management.