Abstract This paper explores the potential of neural networks to forecast earnings per share. The neural network would serve as a decision support system for finance managers, stock brokers and investment analysts and investors.
Further investigations are necessary.
The EPS serves as an important variable for investment performance evaluation. The value of the neural network for the purpose of predicting earnings per share includes pattern recognition, learning, generalization and abstraction, and interpretation of incomplete and noisy data. As a financial model, the neural network possesses an ability to recognize patterns and interdependencies based on specific variables. The purpose of this project is to develop a neural network capable of forecasting earnings per share of Fortune 500 companies. Literature Review Neural networks are being utilized by many financial decision makers for various types of forecasting decisions.