International Journal of Knowledge Based Computer System

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Understanding the Impact of News on Stock Market Trends Using Natural Language Processing and Machine Learning Algorithms


Affiliations
1 Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
     

   Subscribe/Renew Journal


Short-term trading, specifically day-trading involves a high risk concerning monetary resources. Investing money becomes risky without adequate know-ledge and understanding of factors governing the market, some of which include public sentiment, commodity prices, political stability, etc. News is a common way via which people get updates about the latest happenings around the world and hence form opinions about industries, companies, stocks, etc. This affects their trading decisions; substantially impacting their chances of making a profit. Our research focused on building software models that could analyze general news during trading hours and predict the probable stock index closing trend for the end of that day. We used top 25 articles from the Reddit World News Channel and tried to correlate their impact on the DJIA in this study. Using two approaches to the process the text and further deploying machine learning models, we achieved fairly acceptable prediction accuracies.

Keywords

Dow Jones Industrial Average, K-Nearest Neighbors, Logistic Regression, Multi-layer Perceptron, N-gram Model, Reddit World News Channel, Sentiment Analysis.
Subscription Login to verify subscription
User
Notifications
Font Size


  • P. Paakkonen, and D. Pakkala, “Reference architecture and classification of technologies, products and services for big data systems,” Big Data Research, vol. 2, no. 4, pp. 166-186, 2015.

  • X. Zhang, H. Fuehres, and P. A. Gloor, “Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”,” Procedia - Social and Behavioral Sciences, vol. 26, pp. 55-62, 2011.

  • K. Mizumoto, H. Yanagimoto, and M. Yoshioka, “Sentiment analysis of stock market news with semi-supervised learning,” 2012 IEEE/ACIS 11th International Conference on Computer and Information Science (ICIS), pp. 325-328, Shanghai, 30 May - 01 June 2012.

  • J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, vol. 2, no. 1, pp. 1-8, 2011.

  • M. Z. F. W. Antweiler, “Is all that talk just noise? The information content of internet stock message boards,” The Journal of Finance, vol. 59, no. 3, pp. 1259-1294, 2004.

  • R. Ahuja, H. Rastogi, A. Choudhuri, and B. Garg, “Stock market forecast using sentiment analysis,” 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1008-1010, March 2015.

  • N. Lin, J. Yuan, W. Xu, L. Wei, and X. Wang, “How web news media impact futures market price linkage?,” Sixth International Conference on Business Intelligence and Financial Engineering (BIFE), pp. 562-566, November 2013.

  • M. Hagenau, M. Liebmann, M. Hedwig, and D. Neumann, “Automated news reading: Stock price prediction based on financial news using context-specific features,” 45th Hawaii International Conference on System Science (HICSS), pp. 1040-1049, January 2012.

  • J. Gong, and S. Sun, “A new approach of stock price prediction based on logistic regression model,” NISS International Conference on New Trends in Information and Service Science, pp. 1366-1371, June 2009.

  • B. Xie, D. Wang, and R. J. Passonneau, “Semantic feature representation to capture news impact,” Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, pp. 231-236, 2014.

  • A. Kloptchenko, T. Eklund, B. Back, J. Karlsson, H. Vanharanta, and A. Visa, “Combining data and text mining techniques for analyzing financial reports,” 8th America’s Conference on Information Systems, pp. 20-28, 2002.

  • M.-A. Mittermayer, and G. F. Knolmayer, “Text mining systems for market response to news: A survey,” Working Paper No 184, Institute of Information Systems, University of Bern, August 2006.

  • R. P. Schumaker, and H. Chen, “Textual analysis of stock market prediction using financial news articles,” 12th Americas Conference on Information Systems (AMCIS), 2006.

  • V. H. Shah, and M. Mohri, “Machine learning techniques for stock prediction,” Foundations of Machine Learning, Courant Institute of Mathematical Science, New York University, 2007.

  • N. Li, and D. D. Wu, “Using text mining and sentiment analysis for online forums hotspot detection and forecast,” Decision Support Systems, vol. 48, no. 2, pp. 354-368, January 2010.

  • X. Tang, and C. Yang, and J. Zhou, “Stock price forecasting by combining news mining and time series analysis,” IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, pp. 279-282, 2009.

  • E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” The Journal of Finance, vol. 25, no. 2, pp. 383-417, 1970.

  • T. Loughran, and B. McDonald, “When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks,” The Journal of Finance, vol. 66, no. 1, pp. 35-65, February 2011.

  • P. Veronesi, “Stock market overreactions to bad news in good times: A rational expectations equilibrium model,” The Review of Financial Studies, vol. 12, no. 5, pp. 975-1007, 1999.

  • G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1-3, pp. 489-501, December 2006.

  • X. Li, H. Xie, R. Wang, Y. Cai, J. Cao, F. Wang, H. Min, and X. Deng, “Empirical analysis: Stock market prediction via extreme learning machine,” Neural Computing and Applications, vol. 27, no. 1, pp. 67-78, January 2016.

  • C.-Y. Yeh, C.-W. Huang, S.-J. Lee, “A multiple-kernel support vector regression approach for stock market price forecasting,” Expert Systems with Applications, vol. 38, no. 3, pp. 2177-2186, March 2011.

  • A. Wadhwa, N. Jain, M. Wadhwa, and S. Dhall, “Observing the effect of news on stock market using sentiment analysis and machine learning,” Proceedings of the 12th INDIACom, INDIACom-2018, Computing for Sustainable Global Development, pp. 3754-3757, 2018.


Abstract Views: 196

PDF Views: 0




  • Understanding the Impact of News on Stock Market Trends Using Natural Language Processing and Machine Learning Algorithms

Abstract Views: 196  |  PDF Views: 0

Authors

Alka Leekha
Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Arnav Wadhwa
Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Nikhil Jain
Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Mehul Wadhwa
Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

Abstract


Short-term trading, specifically day-trading involves a high risk concerning monetary resources. Investing money becomes risky without adequate know-ledge and understanding of factors governing the market, some of which include public sentiment, commodity prices, political stability, etc. News is a common way via which people get updates about the latest happenings around the world and hence form opinions about industries, companies, stocks, etc. This affects their trading decisions; substantially impacting their chances of making a profit. Our research focused on building software models that could analyze general news during trading hours and predict the probable stock index closing trend for the end of that day. We used top 25 articles from the Reddit World News Channel and tried to correlate their impact on the DJIA in this study. Using two approaches to the process the text and further deploying machine learning models, we achieved fairly acceptable prediction accuracies.

Keywords


Dow Jones Industrial Average, K-Nearest Neighbors, Logistic Regression, Multi-layer Perceptron, N-gram Model, Reddit World News Channel, Sentiment Analysis.

References