Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 45 , 01 December 2023
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Stock Price Prediction Based on Daily News Headlines: Logistic Regression Model and LSTM Model
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Abstract
The influence of daily news on the economy, especially stock market, cannot be underestimated with big data gaining its momentum. In this article, the author uses two years of daily news headlines to predict the stock market movements of the Dow Jones Industrial Average. The first treatments on the dataset are collecting news from Kaggle.com and stock data from Yahoo Finance. The two datasets are then combined into one CSV file and split into training and test sets. Two machine learning models, Logistic Regression and Long Short-Term Memory, are built to fit the combined dataset, and the test index is the accuracy of prediction. The test accuracy is 0.58 with the three-word phrase by Logistic Regression and 0.65 after ten times training with the LSTM model. The final result demonstrates that the two models are feasible and effective for seeking the relationship between daily news and stock market movements and, thus, valuable for stock prediction. The attempts to set parameters give reference to further study, especially the word count of phrases and the number of training circulation.
Keywords
daily news, stock prediction, logistic regression, LSTM
References
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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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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-137-7
- ISBN (Online)
- 978-1-83558-138-4
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/45/20230271
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited