Neural Networks for Financial Stability of Economic System

Abstract

In the present economic landscape, securing the monetary steadiness of economic structures, augmenting their financial efficacy, and competitiveness necessitates the scrutiny of the financial state of enterprises, along with predicting their future progressions utilizing contemporary technologies and models. In acquiring information regarding the fluctuations of significant financial hazards, machine and deep learning techniques can offer more precise projections founded on vast-dimensional datasets, authorize the employment of unbalanced datasets, and preserve all accessible information. The aim of this investigation is to construct a neural network-driven model for assessing the financial stability of economic systems. The study employed financial and economic activity data from 12,573 enterprises and opted for specific financial ratios that generate a significant set of indicators suitable for forecasting the financial stability of economic systems. Both feedforward neural networks (FNN) and recurrent neural networks (RNN) were utilized in the model development. The constructed models were evaluated using established data science techniques.