Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Pranjali Kasture; Kamini Shirsath

doi:10.17485/IJST/v17i18.466

Article

Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

VIEWS 118
PDF 55

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i18.466

Year: 2024, Volume: 17, Issue: 18, Pages: 1880-1888

Original Article

Enhancing Stock Market Prediction: A Hybrid RNN-LSTM Framework with Sentiment Analysis

Pranjali Kasture^1*, Kamini Shirsath²

¹Research Scholar, Department of Computer Engineering, K K Wagh Institute of Engineering Education and Research, Nashik, Savitribai Phule Pune University, Pune, Maharashtra, India
²Professor, Department of Computer Engineering, Sandeep Institute of Engineering & Management, Nashik, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:18 February 2024, Accepted Date:28 March 2024, Published Date:29 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Predicting stock prices with accuracy is a difficult but crucial endeavor for market participants. To increase the precision of stock market forecasts, this study suggests a novel method that blends sophisticated neural network algorithms with sentiment assessment. Methods: News data is pre-processed. Each text document received a sentiment score reflecting overall sentiment. These scores were integrated into the feature set, combining textual sentiment information with historical stock price data from BSE Sensex. The proposed model of hybrid RNN-LSTM is applied and compared with the Random Forest Regressor (RFR), and Support Vector Regressor (SVR). The LSTM model is also applied and tested on data without sentiment analysis scores. Findings: The proposed model yields promising results in stock market prediction accuracy. It significantly gives a low value for mean absolute error (0.036), mean squared error (0.021), and root mean square error (0.046) when compared with the SVR and RFR models. The R2 value is also compared with literature methods, and it shows a 0.40% to 5.5% enhancement in the scores. The results prove that the incorporation of sentiment analysis enriches the predictive capabilities of the model. Novelty: Sentiment analysis combined with the hybrid RNN-LSTM framework provides a new technique to increase the accuracy of stock market forecasts. Using sophisticated knowledge of market dynamics and sentiments, the proposed approach gives important results to market participants, investors, and analysts of financial markets.

Keywords: Stock Market, Sentiment Analysis, Evaluation Metric, Prediction

References

Agustin IN. The Integration of Fundamental and Technical Analysis in Predicting the Stock Price. Jurnal Manajemen Maranatha. 2019;18(2):93–102. Available from: https://doi.org/10.28932/jmm.v18i2.1611
Agrawal M, Shukla PK, Nair R, Nayyar A, Masud M. Stock Prediction Based on Technical Indicators Using Deep Learning Model. Computers, Materials & Continua. 2022;70(1):287–304. Available from: https://dx.doi.org/10.32604/cmc.2022.014637
Idrees SM, Alam MA, Agarwal P. A Prediction Approach for Stock Market Volatility Based on Time Series Data. IEEE Access. 2019;7:17287–17298. Available from: https://dx.doi.org/10.1109/access.2019.2895252
Huang X, You P, Gao X, Cheng D. Stock Price Prediction Based on ARIMA-GARCH and LSTM. In: Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023). (pp. 438-448) Atlantis Press International BV. 2023.
Sen J, Mehtab S. A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models. International Journal of Business Forecasting and Marketing Intelligence. 2020;6(4). Available from: https://dx.doi.org/10.1504/ijbfmi.2020.10038524
Burgess M, Javed F, NO, Robinson C. Stock Forecasts with LSTM and Web Sentiment. Smu Scholar Data Science Review. 2022;6(2):1–20. Available from: https://scholar.smu.edu/cgi/viewcontent.cgi?article=1219&context=datasciencereview
Gaur Y. Stock Market Price Prediction Using LSTM. International Journal for Research in Applied Science and Engineering Technology. 2023;11(12):1881–1887. Available from: https://dx.doi.org/10.22214/ijraset.2023.57673
Patel J, Patel M, Darji M. Stock Price Prediction Using RNN and LSTM. Journal Of Emerging Technologies and Innovative Research (JETIR). 2018;5(11):1069–1079. Available from: https://www.jetir.org/papers/JETIRK006164.pdf
Kasture P, Shirsath K. Recent Studies on Deep Learning Techniques for Stock Market Forecasting. In: 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS). IEEE. 2023.
Mitra M, Adhikary J, Nayek I, Baidya B, Ganguly K, Saha D. Stock Market Prediction Using Sentiment Analysis. International Journal of Research Publication and Reviews. 2023;4(6):2721–2729. Available from: https://ijrpr.com/uploads/V4ISSUE6/IJRPR14435.pdf
Olawale IO, Iworiso J, Amaunam IA. Predicting Stock Investments Based on Sentiment and Historical Price Data. International Journal of Data Envelopment Analysis and *Operations Research*. 2023;4(1):1–32. Available from: https://pubs.sciepub.com/ijdeaor/4/1/1/
Ebrahimian K, Abbasi E, Tabriz AA, Mohammadzadeh A. Stock Price Forecasting With Support Vector Regression Based On. International Journal Of Finance And Managerial Accounting. 2023;8(29):53–64. Available from: http://www.ijfma.ir/article_21124.html
Darapaneni N, Padur AR, Sharma H, Manjrekar M. Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets. 2022. Available from: https://doi.org/10.48550/arXiv.2204.05783
Chen Y. Stock Market Analysis and Prediction Using LSTM. BCP Business & Management. 2013;36:381–386. Available from: https://doi.org/10.54691/bcpbm.v36i.3489
Srijiranon K, Lertratanakham Y, Tanantong T. A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis. Applied Sciences. 2022;12(21):1–23. Available from: https://dx.doi.org/10.3390/app122110823
John A, Latha T. Stock market prediction based on deep hybrid RNN model and sentiment analysis. Automatika. 2023;64(4):981–995. Available from: https://dx.doi.org/10.1080/00051144.2023.2217602

Copyright

© 2024 Kasture & Shirsath. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)