Can natural language processing accurately predict stock market movements based on Reddit World News headlines?

Research Article
Open access

Can natural language processing accurately predict stock market movements based on Reddit World News headlines?

Chen Cao 1 , Xuang Yu 2* , Caiyi Qian 3
  • 1 New York University    
  • 2 University of California Santa Barbara    
  • 3 University of San Francisco    
  • *corresponding author xuangyu@ucsb.edu
Published on 26 December 2023 | https://doi.org/10.54254/2755-2721/29/20230903
ACE Vol.29
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-259-6
ISBN (Online): 978-1-83558-260-2

Abstract

This research examines the application of machine learning and natural language processing (NLP) methods to stock market movement forecasting. Many NLP approaches were used to gather and preprocess Dow Jones Industrial Average (DJIA) data and Reddit Global News headlines. The preprocessed data were then used to train three machine learning algorithms (Random Forest, Logistic Regression, and Naive Bayes) to forecast the daily trend of the DJIA. According to the study, the Naive Bayes algorithm, along with Textblob, fared better than the other two models, obtaining an accuracy of 68.59%, which is an improvement above previous research. These findings show how NLP and machine learning may be used to forecast stock market patterns and offer ideas for further study to boost the precision of these models.

Keywords:

natural language processing, text opinion mining, stock market, DJIA, sentiment analysis, Reddit news, machine learning

Cao,C.;Yu,X.;Qian,C. (2023). Can natural language processing accurately predict stock market movements based on Reddit World News headlines?. Applied and Computational Engineering,29,84-91.
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References

[1]. N. Sharma and A. Juneja, "Combining of random forest estimates using LSboost for stock market index prediction," 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India, 2017, pp. 1199-1202, doi: 10.1109/I2CT.2017.8226316.

[2]. J. M. -T. Wu, Z. Li, G. Srivastava, J. Frnda, V. G. Diaz and J. C. -W. Lin, "A CNN-based Stock Price Trend Prediction with Futures and Historical Price," 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), Taipei, Taiwan, 2020, pp. 134-139, doi: 10.1109/ICPAI51961.2020.00032.

[3]. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.

[4]. L. Bing, K. C. C. Chan and C. Ou, "Public Sentiment Analysis in Twitter Data for Prediction of a Company's Stock Price Movements," 2014 IEEE 11th International Conference on e-Business Engineering, Guangzhou, China, 2014, pp. 232-239, doi: 10.1109/ICEBE.2014.47.

[5]. Cakra, Y. E., & Trisedya, B. D. (2015, October). Stock price prediction using linear regression based on sentiment analysis. In 2015 international conference on advanced computer science and information systems (ICACSIS) (pp. 147-154). IEEE.

[6]. Dong, Y., Yan, D., Almudaifer, A. I., Yan, S., Jiang, Z., & Zhou, Y. (2020, December). Belt: A pipeline for stock price prediction using news. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1137-1146). IEEE.

[7]. Shah, D., Isah, H., & Zulkernine, F. (2018, December). Predicting the effects of news sentiments on the stock market. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4705-4708). IEEE.

[8]. Kim, Y., Jeong, S. R., & Ghani, I. (2014). Text opinion mining to analyze news for stock market prediction. Int. J. Advance. Soft Comput. Appl, 6(1), 2074-8523.

[9]. Velay, M., & Daniel, F. (2018). Using NLP on news headlines to predict index trends. arXiv preprint arXiv:1806.09533.

[10]. Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE.

Cite this article

Cao,C.;Yu,X.;Qian,C. (2023). Can natural language processing accurately predict stock market movements based on Reddit World News headlines?. Applied and Computational Engineering,29,84-91.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-259-6(Print) / 978-1-83558-260-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.29
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. N. Sharma and A. Juneja, "Combining of random forest estimates using LSboost for stock market index prediction," 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India, 2017, pp. 1199-1202, doi: 10.1109/I2CT.2017.8226316.

[2]. J. M. -T. Wu, Z. Li, G. Srivastava, J. Frnda, V. G. Diaz and J. C. -W. Lin, "A CNN-based Stock Price Trend Prediction with Futures and Historical Price," 2020 International Conference on Pervasive Artificial Intelligence (ICPAI), Taipei, Taiwan, 2020, pp. 134-139, doi: 10.1109/ICPAI51961.2020.00032.

[3]. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.

[4]. L. Bing, K. C. C. Chan and C. Ou, "Public Sentiment Analysis in Twitter Data for Prediction of a Company's Stock Price Movements," 2014 IEEE 11th International Conference on e-Business Engineering, Guangzhou, China, 2014, pp. 232-239, doi: 10.1109/ICEBE.2014.47.

[5]. Cakra, Y. E., & Trisedya, B. D. (2015, October). Stock price prediction using linear regression based on sentiment analysis. In 2015 international conference on advanced computer science and information systems (ICACSIS) (pp. 147-154). IEEE.

[6]. Dong, Y., Yan, D., Almudaifer, A. I., Yan, S., Jiang, Z., & Zhou, Y. (2020, December). Belt: A pipeline for stock price prediction using news. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1137-1146). IEEE.

[7]. Shah, D., Isah, H., & Zulkernine, F. (2018, December). Predicting the effects of news sentiments on the stock market. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4705-4708). IEEE.

[8]. Kim, Y., Jeong, S. R., & Ghani, I. (2014). Text opinion mining to analyze news for stock market prediction. Int. J. Advance. Soft Comput. Appl, 6(1), 2074-8523.

[9]. Velay, M., & Daniel, F. (2018). Using NLP on news headlines to predict index trends. arXiv preprint arXiv:1806.09533.

[10]. Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE.

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