Application of Neural Networks to Studying the Impact of the Russian Central Bank’s Monetary Policy

Abstract

The paper presents analysis of the Russian Federation's monetary policy. The Central Bank's key rate is an important parameter. It is hypothesized that the key rate (KR) could be predicted by means of artificial intelligence, a perceptron, the input of which is generated by neural-network quantization. Applying the results of such "smart" analysis to predicting the CBR key rate seems appropriate.

A Study on the Impact of Digital Finance on Regional Productivity Growth Based on Artificial Neural Networks

“…A vector error correction model is used to separately verify the existence of a significant positive relationship between financial development and total factor productivity. There is a large body of research literature, both domestic and international, on the utility of digital finance for economic growth [ 7 ]. An endogenous growth model based on digital finance argues that digital finance, as the main carrier of knowledge products, is an alternative to knowledge and innovation.…”

Neural network model of interaction between real economy sector entrepreneurship and financial field under risk

“…Studies show that in modern conditions, neural network models and artificial intelligence systems play an increasingly important role in solving the problems raised, in particular, in forecasting the implementation of regional investment projects [10], studying the systemic impact on the economy of the monetary policy of the RF Central Bank [11], developing the AI algorithm for trading with the SiH8 futures contract on the MoEx based on quantization of big data [12] and in the use of intellectual data in the evaluation of economic security [13].…”