Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022)

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Forecasting China's Military Industry Index: Based on Decision Tree, Random Forest and Time Series Models

Authors
Xiaoyan Cheng1, Ziyan Liu2, *, Zhijie Zhang3, Zhiyue Zhu4
1School of Information, Xi’an University of Finance and Economics, Xi’an, 710100, China
2School of Innis, University of Toronto, Toronto, M5S 2E8, Canada
3School of Materials, University of Manchester, Manchester, M13 9PL, UK
4School of Foreign Language, Shandong University of Finance and Economics, Jinan, 250002, China
*Corresponding author. Email: zyan.liu@mail.utoronto.ca
Corresponding Author
Ziyan Liu
Available Online 14 December 2022.
DOI
10.2991/978-94-6463-054-1_40How to use a DOI?
Keywords
Portfolio Selection; Random Forest; Time Series
Abstract

IncrEasing uncertainty about geopolitical conflicts and downward economic pressure have contributed to increased stock price volatility in the military industry sector as a result of the ongoing Russia-Ukraine conflict which has gradually developed into a protracted tug-of-war and a war of attrition, as well as the previous financial crises. To strengthen the role of investment profitability, this paper intends to conduct more research on the index of the military industry sector. To predict the trend of sector index, a decision tree, random forest model, time series-based ARIMA model, and neural network model are used. The sector indices are forecasted using the ARIMA model and the neural network model after the correlation test is completed with the random forest model. It is predicted that the sector index will continue to rise with possible fluctuations in the future. By using the random forest, ARIMA model, and neural network model, investors are able to avoid military industry sector risks and gain stable benefits.

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 2nd International Conference on Financial Management and Economic Transition (FMET 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
14 December 2022
ISBN
978-94-6463-054-1
ISSN
2352-5428
DOI
10.2991/978-94-6463-054-1_40How 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  - Xiaoyan Cheng
AU  - Ziyan Liu
AU  - Zhijie Zhang
AU  - Zhiyue Zhu
PY  - 2022
DA  - 2022/12/14
TI  - Forecasting China's Military Industry Index: Based on Decision Tree, Random Forest and Time Series Models
BT  - Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022)
PB  - Atlantis Press
SP  - 357
EP  - 369
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-054-1_40
DO  - 10.2991/978-94-6463-054-1_40
ID  - Cheng2022
ER  -