Research on Forecasting of China's Monetary Policy Based on Random
Forest Algorithm
Abstract
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 indicators 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.