Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

• Published in: Electronics (Basel) Vol. 13; no. 9; p. 1629
• Main Authors: Papageorgiou, George, Gkaimanis, Dimitrios, Tjortjis, Christos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accuracy; Algorithms; Behavioral economics; Computational linguistics; Data integration; Data mining; Datasets; Economic forecasting; Effectiveness; Efficient markets; Finance; Financial analysis; Forecasting; Indicators; Innovations; Investments; Language processing; Machine learning; Mathematical models; Methods; Natural language interfaces; Prices and rates; Profitability; Program trading; Random walk theory; Securities markets; Securities trading; Sentiment analysis; Social media; Social networks; Stock exchanges; Stock markets; Stocks; Time series; Trends; User generated content; Volatility
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13091629

Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.