Predicting stock market by sentiment analysis and deep learning

• Authors: ÖZÖĞÜR AKYÜZ Süreyya , ATAŞ Pınar Karadayı , BENKHALDOUN Aymane
• Languages of publication: EN

Abstracts

EN

The stock market may be unpredictable; understanding when to purchase and sell can greatly assist businesses and individuals in maximizing profits and minimizing losses. Many companies have previously modified time-series analysis, a data mining technique, to forecast stock price movement. The idea of textual data mining has recently come up in debates about stock market forecasts. In this study, five of the largest firms’ historical stock prices were used to train two deep learning models—long short-term memory (LSTM) and one-dimensional convolutional neural network (1D CNN), then the results of all the models were compared. To connect price value fluctuations with the general public, sentiment scores were offered in addition to stock price values by employing natural language processing techniques (TextBlob) to tweets.

Keywords

EN

stock market; Twitter; deep learning; sentiment analysis

• Publisher: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
• Journal: Operations Research and Decisions
• Year: 2024
• Volume: 34
• Issue: 2
• Pages: 85-107

Contributors

author

ÖZÖĞÜR AKYÜZ Süreyya

• Department of Mathematics, Faculty of Engineering and Natural Sciences, Bahçe¸sehir University, Istanbul, Turkey

author

ATAŞ Pınar Karadayı

• Department of Mathematics, Faculty of Engineering and Natural Sciences, Bahçe¸sehir University, Istanbul, Turkey

author

BENKHALDOUN Aymane

• Department of Computer Engineering, Faculty of Engineering and Architecture, Arel University, Istanbul, Turkey

References

• Abu-Taleb, S. K., and Nilsson, F. Impact of social media on investment decision: A quantitative study which considers information online, online community behaviour, and firm image. Bachelor’s thesis, Umeå University, 2021.
• Baresa, S., Bogdan, S., and Ivanovic, Z. Strategy of stock valuation by fundamental analysis. UTMS Journal of Economics 4, 1 (2013), 45–51.
• Breaban, A., and Noussair, C. N. Emotional state and market behavior. Review of Finance 22, 1 (2018), 279–309.
• Cakra, Y. E., and Trisedya, B. D. Stock price prediction using linear regression based on sentiment analysis. In 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (Depok, Indonesia, 2015), IEEE, pp. 147–154.
• Catelli, R., Fujita, H., De Pietro, G., and Esposito, M. Deceptive reviews and sentiment polarity: Effective link by exploiting BERT. Expert Systems with Applications 209 (2022), 118290.
• Chauhan, P., Sharma, N., and Sikka, G. The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing 12, 2 (2021), 2601–2627.
• Costola, M., Hinz, O., Nofer, M., and Pelizzon, L. Machine learning sentiment analysis, Covid-19 news and stock market reactions. Research in International Business and Finance 64 (2023), 101881.
• Deng, L., and Yu, D. Deep learning: methods and applications. Foundations and Trends® in Signal Processing 7, 3–4 (2014), 197–387.
• Dhanasekaren, K., Aluri, S. T., Karthikeyan, N., Baskaran, S. H., and Selvanambi, R. A study on the impact of sentiment analysis on stock market prediction. Recent Advances in Computer Science and Communications 16 (formerly: Recent Patents on Computer Science), 1 (2023), 73–93.
• Ding, X., Zhang, Y., Liu, T., and Duan, J. Using structured events to predict stock price movement: An empirical investigation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014), pp. 1415–1425.
• Doğan, M., Metin, Ö., Tek, E., Yumuşak, S., and Öztoprak, K. Speculator and influencer evaluation in stock market by using social media. In 2020 IEEE International Conference on Big Data (Big Data) (Atlanta, GA, USA, 2020), IEEE, pp. 4559–4566.
• Fabien, M. (3 November 2019). Natural language processing in French (TAL/NLP) (accessed on 29 November 2019) (in French).
• Ghanem, D., and Rosvall, D. Major world events impact on stock market prices: An event study. Bachelor’s thesis, Department of Business Studies, Uppsalla University, 2014.
• Gilbert, E., and Karahalios, K. Widespread worry and the stock market. In Proceedings of the Fourth International AAAI Conference on web and Social Media, Vol. 4, No. 1 (2010) pp. 58-65.
• Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning, The MIT Press, 2016.
• Goutte, S., Liu, F., Le, H. V., and von Mettenheim, H.-J. (5 January 2023). ESG investing: A sentiment analysis approach (accessed on 28 December 2023).
• Hasselgren, B., Chrysoulas, C., Pitropakis, N., and Buchanan, W. J. Using social media & sentiment analysis to make investment decisions. Future Internet 15, 1 (2023), 5.
• Hochreiter, S., and Schmidhuber, J. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
• Jabeen, A., Afzal, S., Maqsood, M., Mehmood, I., Yasmin, S., Niaz, M. T., and Nam, Y. An LSTM based forecasting for major stock sectors using Covid sentiment. Computers, Materials and Continua 67, 1 (2021), 1191–1206.
• Jena, P. R., and Majhi, R. Are Twitter sentiments during Covid-19 pandemic a critical determinant to predict stock market movements? A machine learning approach. Scientific African 19 (2023), e01480.
• Karlemstrand, R., and Leckström, E. Using Twitter attribute information to predict stock prices, 2021. Working paper version available from arXiv: https://doi.org/10.48550/arXiv.2105.01402.
• Khatri, S. K., and Srivastava, A. Using sentimental analysis in prediction of stock market investment. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (Noida, India, 2016), IEEE, pp. 566–569.
• Li, J., Bu, H., and Wu, J. Sentiment-aware stock market prediction: A deep learning method. In International Conference on Service Systems and Service Management (Dalian, 2017), IEEE, pp. 1–6.
• Li, X., Xie, H., Chen, L., Wang, J., and Deng, X. News impact on stock price return via sentiment analysis. Knowledge-Based Systems 69 (2014), 14–23.
• Liu, H. Leveraging financial news for stock trend prediction with attention-based recurrent neural network, 2018. Working paper version available from arXiv: https://doi.org/10.48550/arXiv.1811.06173.
• Long, W., Song, L., and Tian, Y. A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarity. Expert Systems with Applications 118 (2019), 411–424.
• Martin, V. Predicting the French stock market using social media analysis. In 2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization (Bayonne, France, 2013), IEEE, pp. 3–7.
• Mukherjee, S., Sadhukhan, B., Sarkar, N., Roy, D., and De, S. Stock market prediction using deep learning algorithms. CAAI Transactions on Intelligence Technology 8, 1 (2023), 82–94.
• Nguyen, T. H., Shirai, K., and Velcin, J. Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications 42, 24 (2015), 9603–9611.
• Pathak, A. R., Pandey, M., and Rautaray, S. Topic-level sentiment analysis of social media data using deep learning. Applied Soft Computing 108 (2021), 107440.
• Picasso, A., Merello, S., Ma, Y., Oneto, L., and Cambria, E. Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications 135 (2019), 60–70.
• Porshnev, A., Redkin, I., and Shevchenko, A. Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis. In 2013 IEEE 13th International Conference on Data Mining Workshops (Dallas, TX, USA, 2013), IEEE, pp. 440–444.
• Ragab, M. G., Abdulkadir, S. J., Aziz, N., Al-Tashi, Q., Alyousifi, Y., Alhussian, H., and Alqushaibi, A. A novel one-dimensional CNN with exponential adaptive gradients for air pollution index prediction. Sustainability 12, 23 (2020), 10090.
• Salkind, N. J. Encyclopedia of Research Design, vol. 1. Sage, 2010.
• Schmitz, H. C., Lutz, B., Wolff, D., and Neumann, D. When machines trade on corporate disclosures: Using text analytics for investment strategies. Decision Support Systems 165 (2023), 113892.
• Shang, L., Xi, H., Hua, J., Tang, H., and Zhou, J. A lexicon enhanced collaborative network for targeted financial sentiment analysis. Information Processing & Management 60, 2 (2023), 103187.
• Shastri, M., Roy, S., and Mittal, M. Stock price prediction using artificial neural model: an application of big data. EAI Endorsed Transactions on Scalable Information Systems 6, 20 (2019), e1.
• Shruthi, J., and Swamy, S. A prior case study of natural language processing on different domain. International Journal of Electrical and Computer Engineering 10, 5 (2020), 4928–4936.
• Singh, T., Bhisikar, S. B., and Kumar, M. Stock market prediction using ensemble learning and sentimental analysis. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021 (Singapore, 2023), R. Doriya, B. Soni, A. Shukla and X.-Z. Gao, Eds., vol. 946 of Lecture Notes in Electrical Engineering, Springer, pp. 429–441.
• Turri, A. M., Smith, K. H., and Kemp, E. Developing affective brand commitment through social media. Journal of Electronic Commerce Research 14, 3 (2013), 201–214.
• Vargas, M. R., de Lima, B. S. L. P., and Evsukoff, A. G. Deep learning for stock market prediction from financial news articles. In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) ( Annecy, France, 2017), IEEE, pp. 60–65.
• Vu, T. T., Chang, S., Ha, Q. T., and Collier, N. An experiment in integrating sentiment features for tech stock prediction in Twitter. In Proceedings of the Workshop on Information Extraction and Entity Analytics on Social Media Data (Mumbai, India, 2012), The COLING 2012 Organizing Committee, pp. 23–38.
• Wehrmann, J., Becker, W., Cagnini, H. E., and Barros, R. C. A character-based convolutional neural network for language-agnostic Twitter sentiment analysis. In 2017 International Joint Conference on Neural Networks (IJCNN) (Anchorage, AK, USA, 2017), IEEE, pp. 2384–2391.
• Wilson, A. C., Roelofs, R., Stern, M., Srebro, N., and Recht, B. The marginal value of adaptive gradient methods in machine learning. In Advances in Neural Information Processing Systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017) (Long Beach, CA, USA, 2017), U. von Luxburg, S. Bengio, R. Fergus, R. Garnett, I. Guyon, H. Wallach and S.V.N. Vishwanathan, Eds., Curran Associates, Inc., pp. 4149–4159.
• Xie, Y., and Jiang, H. Stock market forecasting based on text mining technology: A support vector machine method, 2019. Working paper version available from arXiv: https://doi.org/10.48550/arXiv.1909.12789.
• Xu, F., and Keelj, V. Collective sentiment mining of microblogs in 24-hour stock price movement prediction. In 2014 IEEE 16th Conference on Business Informatics (Geneva, Switzerland, 2014), vol. 2, IEEE, pp. 60–67.
• Yang, X., Loua, M. A., Wu, M., Huang, L., and Gao, Q. Multi-granularity stock prediction with sequential three-way decisions. Information Sciences 621 (2023), 524–544.
• Zhang, X., Qu, S., Huang, J., Fang, B., and Yu, P. Stock market prediction via multi-source multiple instance learning. IEEE Access 6 (2018), 50720–50728.
• Zhao, Y., and Yang, G. Deep learning-based integrated framework for stock price movement prediction. Applied Soft Computing 133 (2023), 109921.

Identifiers

DOI

10.37190/ord240206