CHINA’S ECONOMY BASED ON GREY FORECAST MODEL AND K-MEANS CLUSTERING ALGORITHM
Author(s):
Chan Wen, Ziqi Li, Xiaomin Zhou
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Abstract
In recent years, there are more and more studies on financial asset allocation and portfolio. Managing the idle assets and maximizing the capital income have become an important research issue. In the common asset classes, equity assets, which refer to the assets related to stocks and corporate equity, have lower investment thresholds and larger investment returns than other investment assets. However, this investment method has high risks, making the asset allocation necessary. There is always one industry in the actual investment portfolio that will outperform others. In this dynamic investment, we need to build a model to predict the optimal investment portfolio. The core of the dynamic adjustment of the investment portfolio is to judge the future monetary and credit cycle. By building the grey forecast model and the K-means clustering model, we divided China’s economy in 2001.01-2021.12 into four stages: tight money and tight credit, easy money and easy credit, tight money and easy credit, easy money and tight credit based on the theory and index data of the monetary and credit cycle, and predicted China’s economy in 2022.10-2023.12. Finally, we put forward investment suggestions to help investors to optimize their investment portfolio and maximize their investment benefits.
KEYWORDS:
K-means clustering algorithm, grey forecast model, economy, investment