Xgboost Model Based Alpha Signal Prediction Using Microblogging Data from Social Media
Keywords:
Alpha Signals, XGBoost, Social Media Data, Machine Learning, Sentiment Analysis, Financial Markets.Abstract
This paper explores a novel approach to predicting alpha signals—indicators of potential stock price movements—by leveraging microblogging data from social media platforms such as Twitter. Traditional methods of alpha signal prediction often rely on historical financial data, which may not fully capture real-time market sentiments. To address this limitation, the study integrates social media data into financial analysis, offering an innovative perspective on understanding investor sentiment and market be haviour. The research employs the XGBoost (Extreme Gradient Boosting) model, a powerful machine learning algorithm, to process and analyse complex, unstructured data with high dimensionality. The model is trained on historical data and testedonout-of-sample data to evaluate its predictive accuracy. Results demonstrate that the XGBoost model effectively generates accurate alpha signals, providing valuable insights for traders and investors, and enhancing decision-making processes in the financial domain.
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References
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM. https:// doi.org/10.1145/2939672.2939785
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
Salton, G., & Mc Gill, M.J.(1983). Introduction to Modern Information Retrieval.McGraw-Hill.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
Zhang, H., & Zhang, H. (2020). Real-time sentiment analysis and its applications in stock market prediction. Journal of Financial Markets, 47, 100-120. https://doi.org/10.1016/j.finmar.2020.100120
Liu, Q., & Zhang, J. (2019). Deep learning for financial sentiment analysis: A comparative study. Proceedings of the 2019 International Conference on Computational Intelligence and Data Science (pp. 204-210). IEEE.
Venkata Sai Teja, D., & Bavankumar, S. (2024). XGBoost model-based alpha signal prediction nusing Micro blogging data from social media. St. Martin’s Engineering College, Secunderabad, Telangana, India.
Amareshwar, M., Shivani, K., Krishna Sai, B. V., & Nagaraj, U. (2024). XGBoost model-based alpha signal prediction using microblogging data from social media. Kommuri Pratap Reddy Institute of Technology, Ghatkesar, Hyderabad, Telangana, India.
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