The possibility of applying artificial neural networks in different areas determined the discovery of more complex structures. This chapter describes the characteristic aspects of using a back-propagation neural network algorithm in making financial forecasting improved by a different technology: genetic algorithms. These can help build an automatic artificial neural network by two adaptive processes: first, genetic search through the data entry window, the forecast horizon, network architecture space, and control parameters to select the best performers; second, back propagation learning in individual networks to evaluate the selected architectures. Thus, network performance population increases from generation to generation. This chapter also presents how genetic algorithms can be used both to find the best network architecture and to find the right combination of inputs, the best prediction horizon and the most effective weight. Finally, this study shows how the results obtained using these technologies can be applied to obtain decision support systems that can lead to increased performance in economic activity and financial projections.
Industrial Financial Forecasting using Long Short-Term Memory Recurrent Neural Networks