Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

• Autorzy: Sridhar Sashank , Sanagavarapu Sowmya

Identyfikatory

DOI

10.15439/2021F79

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

Stock market price prediction models have remained a prominent challenge for the investors owing to their volatile nature. The impact of macroeconomic events such as news headlines is studied here using a standard dataset with closing stock price rates for a chosen period by performing sentiment analysis using a Random Forest classifier. A Bi-LSTM time-series forecasting model is constructed to predict the stock prices by using the polarity of the news headlines. It is observed that Random Forest Classifiers predict the polarity of news articles with an accuracy of 84.92%.

Słowa kluczowe

EN

forecasting theory; macroeconomics; pricing; random forests; sentiment analysis; stock market; time series

PL

teoria prognozowania; makroekonomia; cennik; las losowy; analiza sentymentu; giełda; szereg czasowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 147--150
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Sridhar Sashank

• College of Engineering Guindy, Anna University, Chennai, India

autor

Sanagavarapu Sowmya

• College of Engineering Guindy, Anna University, Chennai, India

Bibliografia

• 1. J.-J. Ohana, S. Ohana, E. Benhamou, D. Saltiel, and B. Guez, “Explainable AI Models of Stock Crashes: A Machine-Learning Explanation of the Covid March 2020 Equity Meltdown,” SSRN Electronic Journal, 2021, http://dx.doi.org/http://dx.doi.org/10.2139/ssrn.3809308.
• 2. S. P. Fraiberger, D. Lee, D. Puy, and R. Rancière, “Media Sentiment and International Asset Prices,” NBER Working Papers 25353, National Bureau of Economic Research, Inc., 2018.
• 3. M. Skuza and A. Romanowski, “Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction,” in 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1349–1354, 2015, http://dx.doi.org/10.15439/2015F230.
• 4. D. Ruta, L. Cen and Q. H. Vu, "Deep Bi-Directional LSTM Networks for Device Workload Forecasting," in 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 115-118, 2020, http://dx.doi.org/10.15439/2020F213.
• 5. D. Daiya and C. Lin, “Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3305–3309, 2021, http://dx.doi.org/10.1109/ICASSP39728.2021.9414893.
• 6. H. Bourezk, A. Raji, N. Acha, and H. Barka, “Analyzing Moroccan Stock Market using Machine Learning and Sentiment Analysis,” in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–5, 2020, http://dx.doi.org/10.1109/IRASET48871.2020.9092304.
• 7. M. V. D. H. P. Malawana and R. M. K. T. Rathnayaka, “The Public Sentiment analysis within Big data Distributed system for Stock market prediction– A case study on Colombo Stock Exchange,” in 2020 5th International Conference on Information Technology Research (ICITR), pp. 1–6, 2020, http://dx.doi.org/10.1109/ICITR51448.2020.9310871.
• 8. J. Sun, “Daily News for Stock Market Prediction, Version 1,” kaggle.com, 2016. https://www.kaggle.com/aaron7sun/stocknews (accessed May 23, 2021).
• 9. J. A. Reyes-Ortiz, M. Bravo, and H. Pablo, “Web Services Ontology Population through Text Classification,” in 2016 Federated Conference on Computer Science and Information Systems, pp. 491–495, 2016, http://dx.doi.org/10.15439/2016F332.
• 10. A. Gillioz, J. Casas, E. Mugellini and O. A. Khaled, "Overview of the Transformer-based Models for NLP Tasks," in 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 179-183, 2020, http://dx.doi.org/10.15439/2020F20.
• 11. P. Maciąg, “Efficient Discovery of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data,” in 2018 Federated Conference on Computer Science and Information Systems, pp. 47–56, 2018, http://dx.doi.org/10.15439/2018F19.
• 12. J. Lindén, S. Forsström, and T. Zhang, “Evaluating Combinations of Classification Algorithms and Paragraph Vectors for News Article Classification,” in 2018 Federated Conference on Computer Science and Information Systems, pp. 489–495, 2018, http://dx.doi.org/10.15439/2018F110.

Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Short Paper