Open AccessResearch on Forecasting of China’s Monetary Policy Based on Random Forest Algorithm
Author(s) -
chuanxin qiu,
Tong Li,
Xuelin Qiu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/3/032078
Subject(s) - random forest , decision tree , computer science , cart , monetary policy , support vector machine , artificial neural network , china , artificial intelligence , algorithm , machine learning , economics , engineering , macroeconomics , geography , mechanical engineering , archaeology
This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People’s Bank of China under the input of 16 macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank’s monetary policy.