An analysis of the applications of neural networks in finance

Over the last 10 years, neural networks have been increasingly applied to various areas of finance. Neural networks are more often applied on the assets side than on the liabilities side of the balance sheet. Some major characteristics of the areas of these applications are their data intensity, unstructured nature, high degree of uncertainty, and hidden relationships. Most of the applications use the backpropagation model with one hidden layer. In most of these applications, neural networks outperformed traditional statistical models, such as discriminant and regression analysis. Furthermore, these applications have shown significant success in financial practice, for example, in forecasting T-bills, in asset management, in portfolio selection, and in fraud detection.