2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 178-189, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
John Ranjith 1 , Kumar Chandar S 2 *
Doi: https://doi.org/10.54216/FPA.160211
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes.
Explainable artificial intelligence , Machine learning , Stock market prediction , Multi-source data , Sentiment analysis , Technical indicators
[1] Lee, M. C., Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896-10904, 2009.
[2] Kara, Y., Acar Boyacioglu, M., & Baykan, Ö. K., Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311-5319, 2011.
[3] Ni, L. P., Ni, Z. W., & Gao, Y. Z., Stock trend prediction based on fractal feature selection and support vector machine. Expert Systems with Applications, 38(5), 5569-5576, 2011.
[4] Lei, L., Wavelet Neural Network Prediction Method of Stock Price Trend Based on Rough Set Attribute Reduction. Applied Soft Computing, 62, 923-932, 2018.
[5] Thakur, M., & Kumar, D., A hybrid financial trading support system using multi-category classifiers and random forest. Applied Soft Computing, 67, 337-349, 2018.
[6] Qiu, M., & Song, Y. (Eds.). (2016). Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. Public Library of Science (PLoS).
[7] Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E., Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? IEEE Access, 6, 48625-48633, 2018.
[8] Long, W., Lu, Z., & Cui, L., Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163-173, 2019.
[9] Billah, M., Waheed, S., & Hanifa, A., Stock market prediction using an improved training algorithm of neural network. International Conference on Electrical, Computer & Telecommunication Engineering, 2016.
[10] Khedr, A. E., Salama, S.E., & Yaseen, N., Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis. International Journal of Intelligent Systems and Applications, 9(7), 22-30, 2017.
[11] Mehta, P., Pandya, S., & Kotecha, K., Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science, 7, e476, 2021.
[12] Vargas, M. R., Dos Anjos, C. E. M., Bichara, G. L. G., & Evsukoff, A. G., Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles. International Joint Conference on Neural Networks, 2018.
[13] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S., Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13(7), 3433-3456, 2020.
[14] Adadi, A., & Berrada, M., Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160.
[15] Carta, S. M., Consoli, S., Piras, L., Podda, A. S., & Recupero, D. R., Explainable Machine Learning Exploiting News and Domain-Specific Lexicon for Stock Market Forecasting. IEEE Access, 9, 30193-30205, 2021.
[16] Kasey, P. Gartner, Top 10 strategic technology trends for 2020, available at http:// www.Gartner.Com/smarterwithgarther/gartner_top_10_strategic_technology_trends_for_2020.
[17] Team NLTK, NLTK Is a Suite of Libraries and Programs for Symbolic and Statistical Natural Language Processing for English Written in the Python Programming Language, 2001.
[18] Loper, E., & Bird, S., NLTK: The Natural Language Toolkit (Version 1). arXiv, 2001.
[19] Wang, Z., Yan, W., & Oates, T., Time series classification from scratch with deep neural networks: A strong baseline. International Joint Conference on Neural Networks, 2017.
[20] Tran, K. L., Le, H. A., Nguyen, T. H., & Nguyen, D. T., Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam. Data, 7(11), 160, 2022.
[21] Khaleghi B., The how of explainable AI: post-modelling explainability. Towards Media Science, 2019.
[22] Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viegas, F., & Wilson, J., The What-If Tool: Interactive Probing of Machine Learning Models. IEEE Transactions on Visualization and Computer Graphics, 2019.
[23] Messalas, A., Kanellopoulos, Y., & Makris, C., Model-Agnostic Interpretability with Shapley Values. International Conference on Information, Intelligence, Systems and Applications, 2019.
[24] Breiman, L. (Eds.). (2001). Machine Learning. Springer Science and Business Media LLC.
[25] Friedman, J. H. (Eds.). (2002). Stochastic gradient boosting. In Computational Statistics & Data Analysis. Elsevier BV.
[26] Ruck, D. W., Rogers, S. K., Kabrisky, M., Oxley, M. E., & Suter, B. W., The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks, 1(4), 296-298, 1990.
[27] Kabbani, T., & Usta, F. E., Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark (Version 1). arXiv, 2022.
[28] Xiao, Q., & Ihnaini, B., Stock trend prediction using sentiment analysis. PeerJ Computer Science, 9, e1293, 2023.
[29] Li, X., Wang, J., Tan, J., Ji, S., & Jia, H., A graph neural network-based stock forecasting method utilizing multi-source heterogeneous data fusion. Multimedia Tools and Applications, 81(30), 43753-43775, 2022.
[30] Theissler, A., Spinnato, F., Schlegel, U., & Guidotti, R., Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions. IEEE Access, 10, 100700-100724, 2022.
[31] Maqbool, J., Aggarwal, P., Kaur, R., Mittal, A., & Ganaie, I. A., Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach. Procedia Computer Science, 218, 1067-1078, 2023.
[32] Koukaras, P., Nousi, C., & Tjortjis, C., Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning. Telecom, 3(2), 358-378, 2022.
[33] Chen, X., Hirota, K., Dai, Y., & Jia, Z., A model fusion method based on multi-source heterogeneous data for stock trading signal prediction. Soft Computing, 27(10), 6587-6611, 2022.
[34] Li, A., Wei, Q., Shi, Y., & Liu, Z., Research on stock price prediction from a data fusion perspective. Data Science in Finance and Economics, 3(3), 230-250, 2023.
[35] Ma, Y., Mao, R., Lin, Q., Wu, P., & Cambria, E., Multi-source aggregated classification for stock price movement prediction. Information Fusion, 91, 515-528, 2023.
[36] Mohamed, L., Soliman, G., & Nasser, A., A Survey on Sentiment Analysis Algorithms and Techniques for Arabic Textual Data. Journal of Fusion: Practice and Applications, 2(2), 74-87, 2020.
[37] Shaymaa, A. A., & Amer, R., Detection and Classification of Alcoholics Using Electroencephalogram Signal and Support Vector Machine. Journal of Fusion: Practice and Applications, 2(1), 14-21, 2020.
[38] Abdelmonem, A. N., Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection. Journal of Fusion: Practice and Applications, 3(1), 54-69, 2021.
