Research on Quantitative Investment Based on Machine Learning

DOI

10.2991/aebmr.k.201128.049How to use a DOI?

Keywords

machine learning, deep learning, quantitative investment, quantitative stock picking

Abstract

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.

Copyright

© 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/).

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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  -