Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)

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Research on Quantitative Investment of the CSI 300 Stocks Based on Monte Carlo Algorithm

Authors
Ziwei Wang1, Weirui Liu1, Yiqi Zheng1, Yanke Wu1, *
1Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, Guangdong, China
*Corresponding author. Email: yanke.wu@163.com
Corresponding Author
Yanke Wu
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-064-0_39How to use a DOI?
Keywords
quantitative investment; factor analysis; k-means clustering; Monte Carlo simulation; Markowitz portfolio theory
Abstract

With the development of science and technology, quantitative investment was involved as the three main methods of stock investment together with fundamental analysis and technical analysis. In this paper, a quantitative investment model is proposed to automatically and accurately capture the real-time updated stock data of CSI 300 stocks. Its specific logic is that the K-means clustering model based on factor analysis is used to screen out several good stocks and then automatically calculate the optimal portfolio mode for investors. In addition, using this model, we conducted an empirical study on the Shanghai and Shenzhen 300 stock data from 2018 to 2020. It is concluded that the top ten stocks during this period are ZTE, Gree Electric Appliance, BOE A, Wuliangye, Dongfang Wealth, CITIC Securities, Sany Heavy Industry, Zhejiang Long sheng, Guizhou Mao-tai and China Ping An. Then, for these ten stocks, we allocate them according to the weights of [0.0097, 0.0189, 0.0147, 0.2403, 0.1576, 0.0863, 0.2129, 0.0403, 0.1655, 0.0535], and the expected return is 0.5019 and the volatility is 0.2979. It can be seen that using this model to make quantitative investment can obtain higher returns with lower risks.

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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Volume Title
Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
978-94-6463-064-0
ISSN
2589-4900
DOI
10.2991/978-94-6463-064-0_39How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Ziwei Wang
AU  - Weirui Liu
AU  - Yiqi Zheng
AU  - Yanke Wu
PY  - 2022
DA  - 2022/12/27
TI  - Research on Quantitative Investment of the CSI 300 Stocks Based on Monte Carlo Algorithm
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
PB  - Atlantis Press
SP  - 359
EP  - 371
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-064-0_39
DO  - 10.2991/978-94-6463-064-0_39
ID  - Wang2022
ER  -