Mahmoud Amoudi^1* and Taleb Ahmad¹

¹ Department of Statistics and Programming, Faculty of Economics, Lattakia University, Syria.

(*Corresponding author: Mahmoud  Amoudi, Email:  mahmoud323@gmail.com)

Received: 20/ 5/ 2025      Accepted: 21/ 7/ 2025

Abstract

The researcher aims to study and analyze the economic and performance indicators of the Syrian Agricultural Cooperative Bank. He then reduces the bank’s performance indicators to a smaller number of factors using factor analysis. This approach aims to determine the nature of the relationship between the bank’s performance variables and the economic factors influencing them in Syria using neural networks. This is based on data published by the Central Bureau of Statistics and periodic reports from the Central Bank of Syria and the Syrian Agricultural Bank for a time series spanning the period from 2005 to 2020. The results of the study demonstrated the possibility of reducing the performance variables of the Agricultural Cooperative Bank from eight variables to two main factors. Furthermore, by studying the relationship between the agricultural bank’s performance variables and the economic factors influencing them using a multi-layer perceptron neural network model, we concluded that economic factors had a significant impact on all the performance variables of the Agricultural Cooperative Bank. The most important economic factors influencing the performance of the Agricultural Cooperative Bank were investment variables and inflation rates on the first factor, and money supply and unemployment rates on the second factor.

Keywords: Agricultural Cooperative Bank, economic factors, neural networks, factor analysis.

 Full paper in Arabic: PDF