Abstract
Six text classification methods were compared to find the best model for predicting Stock Exchange of Thailand stock prices. News headlines, on individual stocks, were classified as causing “change” and “no-change” based on a preset change threshold, 2.5%. The training dataset was collected by matching stock news in 2018 with stock names and filling in stock price changes. 258 news were associated with a “change” and 636 news with “no-change”. The Thai text news items were preprocessed and converted to TF-IDF vector representation. Six machine learning text classification methods are applied to create six text classifier models and create a confusion matrix, then compared with actual changes to obtain accuracy scores. We found that a deep learning classifier (with 85.6% accuracy) scored better than other classifiers for one day price movement to assist short-term investments.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
The Stock Exchange of Thailand: TSD’s Statistical Highlights (As of December). https://www.set.or.th/tsd/en/download/statistic.html. Accessed 20 Jan 2019
Cheng, S.H.: Forecasting the change of intraday stock price by using text mining news of stock. In: Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, pp. 2605–2609. IEEE, Qingdao (2010)
Ichinose, K., Shimada, K.: Stock market prediction from news on the web and a new evaluation approach in trading. In: Proceedings of 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 77–81. IEEE, Kumamoto (2016)
Yetis, Y., Kaplan, H., Jamshidi, M.: Stock market prediction by using artificial neural network. In: Proceedings of world Automation Congress (WAC), pp. 1–5. IEEE, Waikoloa, HI, USA (2014)
Kumar, I., Dogra, K., Utreja, K., Yadav, P.: A comparative study of supervised machine learning algorithm stock market trend prediction. In: Proceedings of 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1003–1007. IEEE, Coimbatore (2018)
Loon, R.V.: Naive Bayes classifier with example simplilearn channel. https://youtu.be/l3dZ6ZNFjo0. Accessed 03 Aug 2019
Pedregosa, F., et al.: scikit-learn: sklearn.naive_bayes.MultinomialNB. https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html. Accessed 11 Aug 2019
Pedregosa, F., et al.: scikit-learn: learn.feature_extraction.text.TfidfVectorizer. https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html. Accessed 11 Aug 2019
Pedregosa, F., et al.: scikit-learn: machine learning in Python. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html. Accessed 20 July 2019
Chairattanamanokorn, N., et al.: HoonSmart Breaking News. https://www.hoonsmart.com. Accessed 20 Jan 2019
The Stock Exchange of Thailand.: Companies/Securities in Focus Historical Trading. https://www.setsmart.com. Accessed 05 May 2019
Viriyayudhakorn, K.: Thai Natural Language Processing (Thai NLP) Resource. https://github.com/kobkrit/nlp_thai_resources. Accessed 12 Aug 2019
Acknowledgement
We would like to thank the Thai NLP Group for sharing their knowledge and resources, King Mongkut’s Institute of Technology Ladkrabang (KMITL) for the research funding and KMITL KRIS for advice on technical English.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Netisopakul, P., Saewong, W. (2020). Effectiveness of Six Text Classifiers for Predicting SET Stock Price Direction. In: Meesad, P., Sodsee, S. (eds) Recent Advances in Information and Communication Technology 2020. IC2IT 2020. Advances in Intelligent Systems and Computing, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-030-44044-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-44044-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44043-5
Online ISBN: 978-3-030-44044-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)