Research Article
Building a Broad Asset Class Allocation Strategy Based on RBF Neural Networks
@INPROCEEDINGS{10.4108/eai.19-5-2023.2334216, author={Wufu Chen and Peiying Ye and Lan Wei}, title={Building a Broad Asset Class Allocation Strategy Based on RBF Neural Networks}, proceedings={Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19--21, 2023, Hangzhou, China}, publisher={EAI}, proceedings_a={ICBBEM}, year={2023}, month={7}, keywords={asset allocation; quadratic exponentialmethods; rbf neural network; correlation analysis}, doi={10.4108/eai.19-5-2023.2334216} }- Wufu Chen
Peiying Ye
Lan Wei
Year: 2023
Building a Broad Asset Class Allocation Strategy Based on RBF Neural Networks
ICBBEM
EAI
DOI: 10.4108/eai.19-5-2023.2334216
Abstract
Globally, there is an industry consensus on broad asset class allocation as a core investment approach. This paper gives investment strategies for several major asset classes and predicts the future state of China's economy through macroeconomic models, which will play a role in guiding the future development of China's economy. According to the problem studied in this paper we construct a time series forecasting model based on quadratic exponential methods, which takes the historical data of indicator data as input, derives the law based on time series changes, and finally realizes the simulation of China's economic growth, inflation, and interest rates in the next five years. We substitute the composite economic evaluation value into the quadratic exponential methods time series forecasting model to forecast the data for the next five years, based on the trend of the data to conclude that the economic state is in a high growth period in 2022-2024; The state of the economy is in a period of medium growth in 2025-2026. And then based on the model analysis of the strategy research, this study has an important role and research significance in the re-financing industry.