Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network

Area of Science:

• Economics
• Financial Development
• Regional Economics

Background:

• Financial depth and breadth significantly impact economic development, but effects vary across regions and time.
• Understanding these complex relationships is crucial for targeted economic strategies.

Purpose of the Study:

• To develop a neural network model to analyze the relationship between financial data and economic development.
• To determine optimal convolutional neural network parameters for this analysis.
• To empirically assess the impact of financial factors on economic development in Region X.

Main Methods:

• Utilized a convolutional neural network (CNN) for economic benefit modeling.
• Performed comparative simulation analysis to optimize CNN parameters (convolution layers, kernel size, number of kernels).
• Applied the optimized CNN model to simulate financial and economic data from Region X.

Main Results:

• The density of financial personnel was found to correlate with economic development.
• Optimized CNN parameters were identified through simulation and comparative analysis.
• The study successfully modeled the financial and economic data of Region X.

Conclusions:

• Improving the comprehensive quality of financial personnel is recommended to foster regional economic development.
• The developed CNN approach offers a feasible method for in-depth research on financial and economic development.
• Findings highlight the nuanced impact of financial factors on economic growth.