A Portfolio Strategy Based on XGBoost Regression and Monte Carlo Method

DOI

10.2991/978-94-6463-036-7_132How to use a DOI?

Keywords

XGBoost; Monte Carlo; Portfolio Strategy; ETF; Machine learning; Optimization

ABSTRACT

In this research, XGBoost algorithm was used to choose stocks. The stock data was downloaded from Yahoo Finance. The volumes, the differences of open price and close price, the differences of high price and low price, the adjusted close prices of the previous three days were considered as factors. Based on XGBoost, the data were segmented and trained to obtain the importance of each factor for each stock. The price of the previous three days is the most important factor for most stocks. In addition, RMSE and MAPE were calculated. After selecting the stocks with the minimum MAPE, the mean variance portfolio optimization model and the Monte Carlo method were used to find a range of portfolio weights of each stock in the stock pool. The return was calculated under the condition of reducing the risk. When the weights of the stock portfolio with the maximum Sharpe ratio are applied to the next year, the portfolio will achieve higher returns. Therefore, the model can be considered as a suitable tool to help investors implement better portfolio strategies.

Copyright

© 2022 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  - Mingxuan Wang
PY  - 2022
DA  - 2022/12/31
TI  - A Portfolio Strategy Based on XGBoost Regression and Monte Carlo Method
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 896
EP  - 902
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_132
DO  - 10.2991/978-94-6463-036-7_132
ID  - Wang2022
ER  -