Research on Quantitative Investment Based on Machine Learning
- 10.2991/aebmr.k.201128.049How to use a DOI?
- machine learning, deep learning, quantitative investment, quantitative stock picking
The stock market is a complex nonlinear system with low signal-to-noise ratio. Machine learning is used to model fuzzy nonlinear data and has proved to be a powerful tool in many fields. Machine learning has been continuously improved, and the successful application of the algorithms in the fields of computer vision, expert systems, etc. makes it obvious advantages to use machine learning methods to construct quantitative investment strategies. Stock selection is essentially a sorting problem. Investors all want to pick relatively better performing stocks. Therefore, this article discusses how to choose a more appropriate investment strategy in the investment process. This paper analyzes the basic situation of the application of machine learning methods in the field of quantitative investment in conjunction with the relevant technical background, and studies and constructs a rate of return prediction model based on the analysis. As a product of the current fusion research of quantitative investment and machine learning methods, the subject is a research hotspot in the industry and has strong practical guidance and reference value.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kaiwen Zhang PY - 2020 DA - 2020/11/30 TI - Research on Quantitative Investment Based on Machine Learning BT - Proceedings of the 2020 2nd International Conference on Economic Management and Cultural Industry (ICEMCI 2020) PB - Atlantis Press SP - 245 EP - 249 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.201128.049 DO - 10.2991/aebmr.k.201128.049 ID - Zhang2020 ER -