Design and application of Artificial Neural Networks for predicting the values of indexes on the Bulgarian Stock market

The Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. They are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, Artificial Neural Networks are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their importance in this field is growing. In this paper our aim will be to analyze different neural networks for financial time series forecasting. Specifically their ability to predict future values of The Bulgarian Stock exchange - Sofia and the respective representative indexes. In order to yield better results Artificial Neural Networks need to have an optimal architecture and be trained in a suitable way. This will be the main challenge for the authors of this paper. Conclusions made by multiple authors that Artificial Neural Networks do have the capability to forecast the stock markets studied and, if properly trained, can improve the robustness according to the network structure are put to the test in this paper by constructing and applying three different models that will be tested in the environment of the Bulgarian capital market.