Integrating Time Series Analysis and Sentiment Analysis for Indian Stock Market Price Movement Prediction
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Abstract
The Indian stock market, which includes the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE), has developed into one of the world's most vibrant and data-rich financial ecosystems. The challenge of forecasting price movements in such a setting lies at the nexus of natural language processing, machine learning, and econometrics. This review article offers a thorough synthesis of research on two popular methodological streams, sentiment analysis and time series forecasting, and looks at how combining them results in noticeably better predictive performance for Indian stocks. We examine over twenty groundbreaking and current studies, covering sentiment extraction methods used in financial news, social media, and regulatory disclosures, deep learning architectures (LSTM, GRU, Transformer), and traditional statistical models (ARIMA, GARCH). In India-specific issues like multilingual data, market microstructure, regulatory events, and the impact of retail investor sentiment amplified through platforms like Money control, Twitter/X, and NSE forums are given particular attention. In this review important research gaps are highlighted, especially in the areas of explainable AI and real-time hybrid pipelines and suggests a research agenda for the Indian market.
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1. Thakkar, A., & Chaudhari, K. (2021). Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions. Information Fusion, 65, 100–109.
2. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). John Wiley & Sons.
3. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
4. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
5. Banerjee, A., & Sarkar, S. (2006). Modelling daily volatility of the Indian stock market using intra-day data. Working Paper, IIM Calcutta.
6. Panda, C., & Narasimhan, V. (2007). Forecasting daily volatility of stock index using GARCH models. Asian Academy of Management Journal of Accounting and Finance, 3(1), 1–24.
7. Choudhary, K., & Choudhary, S. (2010). Testing capital asset pricing model: Empirical evidence from Indian equity market. Eurasian Journal of Business and Economics, 3(6), 127–138.
8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
9. Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
10. Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748–1764.
11. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65.
12. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
13. Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
14. Mondal, P., Shit, L., & Goswami, S. (2014). A study of time series model for predicting jute yarn demand: Box-Jenkins methodology. American Journal of Modeling and Optimization, 2(3), 103–107.
15. Kumar, M., & Anand, M. (2014). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Studies in Business and Economics, 9(1), 81–94.
16. Pawar, K., Jalem, R. S., & Tiwari, V. (2019). Stock market price prediction using LSTM RNN. In Emerging Trends in Expert Applications and Security (pp. 493–503). Springer.
17. Mittal, A., & Goel, A. (2012). Stock prediction using Twitter sentiment analysis. Stanford University Technical Report, CS229.
18. Nagar, A., & Hahsler, M. (2012). Using text and data mining techniques to extract stock market sentiment from live news streams. IBER Proceedings.
19. Joshi, K., Bharathi, H. N., & Rao, J. (2016). Stock trend prediction using news sentiment analysis. International Journal of Computer Science and Information Technology, 8(3).
20. Gupta, U., & Kamble, V. (2020). Multilingual sentiment analysis of Indian financial social media using BERT-based models. Proceedings of EMNLP 2020 Workshop on Noisy Text.
21. Rao, T., & Srivastava, S. (2014). Analyzing stock market movements using Twitter sentiment analysis. Proceedings of the 2014 International Conference on Advances in Social Networks Analysis and Mining.
22. Gundecha, P., & Liu, H. (2012). Mining social media: A brief introduction. New Directions in Informatics, Optimization, Logistics, and Production, 1–17.
23. Mankar, T., Hotchandani, T., Madhwani, M., Chidrawar, A., & Lifna, C. S. (2018). Stock market prediction based on social sentiments using machine learning. Proceedings of IEEE ICSCCC 2018.
24. Tripathi, A., Agrawal, S., & Rao, S. (2022). Comparison of VADER and BERT sentiment scores for financial news. Procedia Computer Science, 204, 422–428.
25. Dey, S., & Bhattacharyya, S. (2019). Deep learning for event-driven stock market prediction using SEBI disclosure data. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(6), 534–545.
26. Sehgal, S., & Pandey, A. (2015). Equity valuation using price multiples: Evidence from India. Asian Academy of Management Journal of Accounting and Finance, 6(1), 89–108.
27. Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153–163.
28. Srivastava, T., Gupta, P., & Bansode, A. (2021). Deep learning-based hybrid model for Indian stock prediction with cross-market sentiment analysis. Journal of Financial Data Science, 3(2), 88–107.
29. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
30. Agrawal, M., Khan, A. U., & Shukla, P. K. (2019). Stock price prediction using technical, fundamental, and textual information—A hybrid deep learning-based approach for Indian markets. International Journal of Advanced Computer Science and Applications, 10(3), 185–194.