Neural networks for time-series predictions in finance and investing

Artificial intelligence with neural networks gives the possibility to improve classical methods with its capability of learning, higher degree of robustness and fault tolerance. The paper is concerned on usage of neural networks in domain of finance and investing. Various authors are compared and their results in neural network application in area of finance and investing are presented. In our research, we tested and evaluated several different architectures of backpropagation neural network algorithm on profit prediction problem. Given results show that the best performance is obtained by three layer network with 9 neurons in. input and hidden layer and one neuron in output layer, with learning parameter of 0.2. Future research can focus On. other evaluating measures and types of networks.