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

Xiaoyan Cheng, Ziyan Liu (), Zhijie Zhang and Zhiyue Zhu
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Xiaoyan Cheng: Xi’an University of Finance and Economics, School of Information
Ziyan Liu: University of Toronto, School of Innis
Zhijie Zhang: University of Manchester, School of Materials
Zhiyue Zhu: Shandong University of Finance and Economics, School of Foreign Language

A chapter in Proceedings of the 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022), 2023, pp 357-369 from Springer

Abstract: 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.

Keywords: Portfolio Selection; Random Forest; Time Series (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-054-1_40

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