A Multi Headed Artificial Intelligence Approach for Stock Market Trading
Main Article Content
Keywords
Stock Market Analysis, Deep Learning, Sentiment Analysis, Algorithmic trading, Ensemble Model
Abstract
Stock market prediction remains a challenging task due to market volatility and the complex interplay of multiple factors affecting price movements. While traditional technical analysis and modern machine learning/deep learning approaches have shown promise, they often fall short when used in isolation. This paper presents a novel three-layer approach that combines traditional technical analysis, deep learning, and sentiment analysis for more accurate stock price prediction. The first layer employs technical indicators to capture price trends from historical data. The second layer utilizes deep learning models to process comprehensive market data and identify complex patterns. In this second layer, predicted prices are also plotted with historical data, and the buy or sell decision is based on the chart classification. The third layer incorporates real-time sentiment analysis of news and social media to capture market sentiment impact. We evaluate our approach using historical data from major stock exchanges spanning three years (2021-2023). Results demonstrate that our integrated approach significantly outperforms existing methods, achieving lower Mean Absolute Error (MAE) and Mean Squared Error (MSE) scores while maintaining a higher R². These findings suggest that combining multiple analytical perspectives through a layered architecture can provide more reliable stock market predictions than single-method approaches.
References
Roscoe, P. (2023). How to build a stock exchange: The past, present and future of finance. Policy Press. https://doi:10.1332/policypress/9781529224313.001.0001
Idowu, E. (2024). 2. Advancements in Financial Market Predictions Using Machine Learning Techniques. https://doi: 10.20944/preprints202407.1075.v1
Paul, M. K., & Das, P. (2023). A comparative study of deep learning algorithms for forecasting Indian stock market trends. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2023.0141098
Awad, A. L., Elkaffas, S. M., & Fakhr, M. W. (2023). Stock market prediction using deep reinforcement learning. Applied System Innovation. https://doi.org/10.3390/ASI6060106
Sahu, S., Mokhade, A., & Bokde, N. (2023). An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: Recent progress and challenges. Applied Sciences. https://doi.org/10.3390/APP13031956
Fan., C., Zhang., X. (2024). Stock Price Nowcasting and Forecasting with Deep Learning. https://doi: 10.21203/rs.3.rs-4757746/v1
Kurniawan, A., & Yusuf, M. (2024, July). Stock Price Prediction Using Technical Data and Sentiment Score. In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) (pp. 360-366). IEEE.
Cohen, N., Balch, T., & Veloso, M. (2020). Trading via image classification. In Proceedings of the first ACM international conference on AI in finance (pp. 1-6).
Siva, N., Sivaiah, B. V., Nikkam, P. V., Volliboina, V., Sai, D. H., & Pushpalatha, K. (2024). Improving Predictions of Stock Price with Ensemble Learning. In International Conference on Computational Innovations and Emerging Trends (ICCIET-2024) (pp. 548-558). Atlantis Press.
Liapis, C. M., Karanikola, A., & Kotsiantis, S. (2023). Investigating deep stock market forecasting with sentiment analysis. Entropy, 25(2), 219. https://doi.org/10.3390/e25020219
Zhang, C., Sjarif, N. N. A., & Ibrahim, R. (2024). 1D-CapsNet-LSTM: A deep learning-based model for multi-step stock index forecasting. Journal of King Saud University-Computer and Information Sciences, 36(2), 101959. https://arxiv.org/pdf/2310.02090
Nejad, F. S., & Ebadzadeh, M. M. (2024). Stock market forecasting using DRAGAN and feature matching. Expert Systems with Applications, 244, 122952.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Wilder, J. W. (1978). New concepts in technical trading systems. Greensboro, NC.
Appel, G. (2005). Technical analysis: power tools for active investors. FT Press.
Lane, G. C. (1984). Lane’s stochastics. Technical Analysis of Stocks and Commodities, 2(3), 80.
Bollinger, J. (2002). Bollinger on Bollinger bands (p. 0). New York: McGraw-Hill.
Borovykh, A., Bohte, S., & Oosterlee, C. W. (2017). Conditional Time Series Forecasting with Convolutional Neural Networks. stat, 1050, 16. https://doi.org/10.48550/arXiv.1703.04691
Chen, R. T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. (2018). Neural ordinary differential equations. Advances in neural information processing systems, 31.
Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). TensorFlow: a system for Large-Scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).
Jia, C., Wang, D., Liu, J., & Deng, W. (2024). Performance Optimization and Application Research of YOLOv8 Model in Object Detection. Academic Journal of Science and Technology, 10(1), 325-329.
Araci, D. (2019). FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv e-prints, arXiv-1908.