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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Predict Stock Price Trend by Using Classification Model
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Abstract
The stock market is changing daily and people are paying more and more attention to stocks. After the establishment of the stock market, the research on stocks has become more and more influential. The core of researching stock is the stock future price trend, bullish or bearish. In order to predict stock information in simple and efficient ways, this paper aims to predict stock rise or fall by using classification model with better performance index and strong operability. Firstly, the paper acquires Maotai Corporation’s daily stock data from tushare package. To define the label “up” and “down”, the paper compares the daily closing price with its yesterday price. If it is positive, it is recorded as up; if it is negative, it is recorded as down. The random forest, logistic regression and SVM models are established respectively. The best model was selected by comparing three models’ evaluation scores. The results show that logistic regression is better than the other two models in predicting the rise and fall of stocks. This study can promote the cross integration of financial field and technical level and provide new ideas for future stock investment.
Keywords
random forest, logistic regression, support vector machine
References
1. Xiong,Z.,Che,W.G.: Application of ARIMA-GARCH-M model in short-term stock forecasting. Journal of Shaanxi Technology University (Natural Science Edition)38(04),69-74(2022).
2. Zhang, J.K.,Sheng,Y.P.: Support vector machine prediction of stock price rise and fall. Journal of Information Science and Technology University of Beijing (Natural Science) 32(03):41-44.DOI:10.16508/j.cnki.11-5866/n.2017.03.008(2017).
3. Liu,Z.S.: Research on stock trend prediction based on Machine learning. UESTC (University of Electronic Science and Technology of China).DOI:10. 27005/d.cnk i.gdzku. 2020.003227(2020).
4. Cheng,M.F.,Gao,S.P.: Multi-scale stock forecasting based on deep transfer learning. Computer engineering and applications58(12),249-259(2022).
5. Phua, P. K. H., Zhu, X., & Koh, C. H.: Forecasting Stock Index Increments Using Neural Networks with Trust Region Methods.IEEE1,260-265(2003).
6. Oztekin, A., Kizilaslan, R., Freund, S., & Iseri, A.: A Data Analytic Approach to Forecasting Daily Stock Returns in an Emerging Market. European Journal of Operational Research253(3),697-710(2016).
7. Kim,K J.: Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing55(1),307-319(2003).
8. Biau, G., & Scornet, E.: A random forest guided tour. Test,25, 197-227(2016).
9. LaValley, M. P.: Logistic regression. Circulation117(18), 2395-2399(2008).
10. Chauhan, V. K., Dahiya, K., & Sharma, A.: Problem formulations and solvers in linear SVM: a review. Artificial Intelligence Review52(2), 803-855(2019).
11. Cui, X.H.,Liu,C.J., Xue,L.: Prediction of porosity based on stochastic forest regression algorithm. Exploration engineering in western China31(11):99-102+105(2019).
12. Jiang,X.L.,Wang,J.H.: Use logical regression model to predict the pathogenic factors of cervical cancer. Computer and information technology30(04):30-32.DOI:10.19414/j.cnki.10 05-1228.2022.04.017(2022).
13. Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W.:Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics15(1), 41-51(2018).
14. Fang,Y.H.,Chen,X.W..: Comparative study on estuary salinity prediction based on neural network and support vector machine.Hydrology42(05):51-55.DOI:10.19797/j.cnki.1000-0852.20210194(2022).
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
- 10.54254/2754-1169/45/20230260
- 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