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Reseach Article

Trend Prediction of DJIA index based on News Extraction from Yahoo Finance

by Komal Batool, Ubaida Fatima, Mirza Faizan Ahmed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 64
Year of Publication: 2025
Authors: Komal Batool, Ubaida Fatima, Mirza Faizan Ahmed
10.5120/ijca2025924379

Komal Batool, Ubaida Fatima, Mirza Faizan Ahmed . Trend Prediction of DJIA index based on News Extraction from Yahoo Finance. International Journal of Computer Applications. 186, 64 ( Feb 2025), 42-46. DOI=10.5120/ijca2025924379

@article{ 10.5120/ijca2025924379,
author = { Komal Batool, Ubaida Fatima, Mirza Faizan Ahmed },
title = { Trend Prediction of DJIA index based on News Extraction from Yahoo Finance },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2025 },
volume = { 186 },
number = { 64 },
month = { Feb },
year = { 2025 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number64/trend-prediction-of-djia-index-based-on-news-extraction-from-yahoo-finance/ },
doi = { 10.5120/ijca2025924379 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-03T23:25:34.525338+05:30
%A Komal Batool
%A Ubaida Fatima
%A Mirza Faizan Ahmed
%T Trend Prediction of DJIA index based on News Extraction from Yahoo Finance
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 64
%P 42-46
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Decision making in a financial world is a very challenging task for any investor as it can leads towards a very heavy loss as well as very higher returns. Therefore, proper understanding of market behavior is required. It is found in research that movement of prices in financial market is random in nature and depends on multiple factors. In this research sentiment-based prediction of DJIA (Dow Jones Industrial Average) index is performed to forecast the future direction of the indices. The objective behind this research is to analyze if the market is sensitive to news or not and if the web news data contributes in the movement of the market. Five different classification models of machine learning are used which include decision tree, random forest, support vector machine, K-Nearest Neighbor and logistic regression. It is observed that KNN is the best predictive model among all for our dataset with the accuracy of 70%. The results are validated on NASDAQ composite and proved that KNN outperforms other considered classifiers.

References
  1. Batool, Komal, Mirza Faizan Ahmed, and Muhammad Ali Ismail. "A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index." Reviews of Management Sciences Vol 4.1 (2022)
  2. Mankar, Tejas, et al. "Stock market prediction based on social sentiments using machine learning." 2018 international conference on smart city and emerging technology (ICSCET). IEEE, 2018.
  3. Soloviev, V.N., A. Bielinskyi, and V. Solovieva. Entropy Analysis of Crisis Phenomena for DJIA Index. in ICTERI Workshops. 2019.
  4. Fatima, Ubaida, Saman Hina, and Muhammad Wasif. "A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets." Journal of Computational Science 70 (2023): 102008.
  5. 2Song, Y.Y. and Y. Lu, Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry, 2015. 27(2): p. 130-5.
  6. Biau, G. and E. Scornet, A random forest guided tour. TEST, 2016. 25(2): p. 197-227.
  7. Wang, Yiwen, et al. "Improved KNN-based Stock Price Prediction." Academic Journal of Computing & Information Science 7.6 (2024): 38-43.
  8. Cunningham, P. and S.J. Delany, K-nearest neighbour classifiers-a tutorial. ACM computing surveys (CSUR), 2021. 54(6): p. 1-25.
  9. Siddartha Reddy, A., et al. "Stock Market Trend Prediction Using K-Nearest Neighbor (KNN) Algorithm." (2024).
  10. Awad, M. and R. Khanna, Support Vector Machines for Classification, in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, M. Awad and R. Khanna, Editors. 2015, Apress: Berkeley, CA. p. 39-66.
  11. LaValley, M.P., Logistic Regression. Circulation, 2008. 117(18): p. 2395-2399.
  12. Chang, P.-C., et al., A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 2009. 36(3, Part 2): p. 6889-6898.
  13. Bharathi, Shri, and Angelina Geetha. "Sentiment analysis for effective stock market prediction." International Journal of Intelligent Engineering and Systems 10.3 (2017): 146-154.
  14. Farimani, Saeede Anbaee, Majid Vafaei Jahan, and Amin Milani Fard. "From text representation to financial market prediction: A literature review." Information 13.10 (2022): 466.
  15. Bi, J., Stock market prediction based on financial news text mining and investor sentiment recognition. Mathematical Problems in Engineering, 2022. 2022(1): p. 2427389.
  16. Ab. Rahman, A.S., S. Abdul-Rahman, and S. Mutalib. Mining Textual Terms for Stock Market Prediction Analysis Using Financial News. in Soft Computing in Data Science. 2017. Singapore: Springer Singapore.
  17. Manzoor, N., D.S. Rai, and S. Goswami. Stock Exchange Prediction Using Financial News and Sentiment Analysis. in Proceedings of Integrated Intelligence Enable Networks and Computing. 2021. Singapore: Springer Singapore.
  18. Dang, Minh, and Duc Duong. "Improvement methods for stock market prediction using financial news articles." 2016 3rd National foundation for science and technology development conference on information and computer science (NICS). IEEE, 2016.
Index Terms

Computer Science
Information Sciences
Sentiment Analysis
Machine Learning
Classification Techniques

Keywords

Trend forecasting Web news Stock market analysis KNN