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Effectiveness of Six Text Classifiers for Predicting SET Stock Price Direction

  • Ponrudee NetisopakulEmail author
  • Woranun Saewong
Conference paper
  • 7 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1149)

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

Stock trends Decision tree Neural network Deep learning Naive Bayes Random forest SVM Stock news Text classification 

Notes

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.

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Knowledge Management and Knowledge Engineering Laboratory, Faculty of Information TechnologyKing Mongkut’s Institute of TechnologyBangkokThailand

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