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Published on 21 May 2024
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Jin,Z.;Ma,M.;Zhou,Z.;Gan,S.;Min,Y. (2024). Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?. Advances in Economics, Management and Political Sciences,82,131-141.
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Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?

Zekai Jin *,1, Martin Ma 2, Zihao Zhou 3, Shulan Gan 4, Yuhan Min 5
  • 1 Amsterdam Business school, University of Amsterdam, Amsterdam, 1018WV, Netherlands
  • 2 College of Arts and Science(sas), University of Pennsylvania, Philadelphia, 19104, USA
  • 3 Department of Mathematics, University College London, London, WC1E6BT, UK
  • 4 Department of electrical and electronic engineering, Hong Kong Polytechnic University, Hong Kong, 999077, China
  • 5 College du Leman, Versoix, 1290, Switzerland

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/82/20230971

Abstract

The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.

Keywords

Stock market, Natural Language Processing, Machine Learning

[1]. Hogenboom, F., de Winter, M., Frasincar, F., and Kaymak, U. (2015). A news event-driven approach for the historical value at risk method. Expert Systems with Applications, 42(10), 4667-4675.

[2]. Da, Z., Engelberg, J., and Gao, P. (2011). In search of attention. The journal of finance, 66(5), 1461-1499.

[3]. Bollen, J., Mao, H., and Zeng, X. (2011). Twitter's mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.

[4]. Zhang, X., Fuehres, H., and Gloor, P. (2011). Predicting stock market indicators through Twitter: A feasibility study. Decision Support Systems, 52(1), 828-834.

[5]. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.

[6]. Easley, D., Hvidkjaer, S., and O’Hara, M. (2002). Is information risk a determinant of asset returns? Journal of Finance, 57(5), 2185-2221.

[7]. Lamont, O., and Thaler, R. H. (2003). Can the market add and subtract? Mispricing in tech stock carve-outs. Journal of Political Economy, 111(2), 227-268.

[8]. Barun´ık, J., and Kˇrehl´ık, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296.

[9]. Dimpfl, T., and Jank, S. (2016). Can internet search queries help to predict stock market volatility? European Financial Management, 22(2), 171-192.

[10]. Ding, X., Liu, B., and Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 international conference on web search and data mining (pp. 231-240).

[11]. Cambria, E., Schuller, B., Xia, Y., and Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15-21.

[12]. Sun, J. (2016). Daily News for Stock Market Prediction, Version 1. https://www.kaggle.com/aaron7sun/stocknews.

[13]. Loughran, T., and McDonald, B. (2014). Measuring readability in financial disclosures. Journal of Finance, 69(4), 1643-1671.

[14]. Nielsen, F. A(˚). (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. Proceedings of the International Conference on Weblogs and Social Media (ICWSM).

Cite this article

Jin,Z.;Ma,M.;Zhou,Z.;Gan,S.;Min,Y. (2024). Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?. Advances in Economics, Management and Political Sciences,82,131-141.

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 2nd International Conference on Financial Technology and Business Analysis

Conference website: https://2023.icftba.org/
ISBN:978-1-83558-429-3(Print) / 978-1-83558-430-9(Online)
Conference date: 8 November 2023
Editor:Javier Cifuentes-Faura
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.82
ISSN:2754-1169(Print) / 2754-1177(Online)

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