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 Forecasting with Machine Learning
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Abstract
Stock price prediction has always been a problem that investors are very concerned about. This paper studies the use of financial information of listed companies to predict whether the stock price will rise after the release of its financial report. This paper extracts the financial information of listed companies from the Guotaian database, obtains their stock price data from Yahoo Finance, and calculates the corresponding technical indicators. And exploring the effect of these indicators on the stock price prediction. The study found that there is a small gap between forecasting with financial information alone and forecasting with technical indicators alone. The combined model performs slightly better than the single model. This study demonstrates that financial information can effectively aid in predicting stock prices and overcome the limitations of certain technical indicators. By incorporating both financial data and stock price information, investors can make more accurate prediction regarding fluctuations in the stock market.
Keywords
machine learning, stock price prediction, financial indicators
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
- 10.54254/2754-1169/45/20230275
- 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