Abstract
Accurately predicting the movement of stock prices can help people make more informed investment decisions and thus obtain higher returns. They can also assess market trends, develop investment strategies and provide investment advice. In this paper, we used 5 models including Random Forest, XGBoost, ANN, RNN, LSTM to predict and verify the fit of 3 companies (AMZN, BABA and MSFT). It is found that LSTM and random forest model can predict well in most cases. The development of the financial industry does have some shortcomings, and the future financial field will be a field full of challenges and opportunities, so some machine learning and deep learning methods can be used to solve the prediction and modeling problems of financial aspects such as the stock market.
Reference19 articles.
1. Nabipour M, Nayyeri P, Jabani H, et al. Deep learning for stock market prediction[J]. Entropy, 2020, 22(8): 840.
2. Dong X., Yu Z., Cao W. et al. A survey on ensemble learning. Front. Comput. Sci. 14, 241–258 (2020). https://doi.org/10.1007/s11704-019-8208-z
3. Liu C, Chan Y, Kazmi S H Alam, et al. Financial fraud detection model: Based on random forest[J]. International journal of economics and finance, 2015, 7(7).
4. Khaidem L, Saha S, Dey S R. Predicting the direction of stock market prices using random forest[J]. arXiv preprint arXiv:1605.00003, 2016.
5. A study on predicting loan default based on the random forest algorithm