Stock Selection Model Based on Random Forest

DOI

10.2991/978-94-6463-010-7_67How to use a DOI?

Keywords

Stock Selection; Quantitative Model; Random Forest; Machine Learning

Abstract

Nowadays, the stock selection has become increasingly significant in financial field with the rapid development of Quantitative Investment. At first, as we all known, traditional style of equity investment involves personal scrutiny of available data on a company, including subjective assessments of the company’s operating and financial situation John, Miller, & Kerber [4]. However, with the continuous development of the financial industry, the number of shares has increased rapidly, which lead to a mass of data. At the same time, the limited calculation and quantitative ability of quants lead to inadequate and incomplete stock selection strategy. Besides, there are also some boundedness of the traditional type such as the subjective assume of the quants and the low recoverability. Using the machine learning, we establish a new model used to predict the stock’s return rank of next term based the factors from Barra Equity Model Lu, & Lu, [7]. We mainly make use of Random forest to establish the model. And it has been divided into two models while the one of it is the regression model to predict the rank of the stock’s return rate, the other is the classification model to predict if the rank of the stock portfolio can exceed 50% of all. Finally, we take the back test on the basis of the model to verify the accuracy of the model, and finally we concluded that the model we built was effective.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chenyao Ma
PY  - 2022
DA  - 2022/12/02
TI  - Stock Selection Model Based on Random Forest
BT  - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022)
PB  - Atlantis Press
SP  - 654
EP  - 663
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-010-7_67
DO  - 10.2991/978-94-6463-010-7_67
ID  - Ma2022
ER  -