Neural networks, financial trading and the efficient markets hypothesis
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Design and implementation of NN5 for Hong Kong stock price forecasting
Engineering Applications of Artificial Intelligence
Quarterly Time-Series Forecasting With Neural Networks
IEEE Transactions on Neural Networks
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Artificial neural networks (NN) can be used to model complex relations between inputs and outputs or to find patterns in data. When dealing with time series the process of prediction with NN has to be adopted to take into account the temporal characteristics of the data. A variety of different aspects of designing NN based forecasting models were introduced, and the most common way of dealing temporal data is by using sliding window. This paper presents a work where NNs are used to forecast stock market prices. We analyze which models are more adequate for different companies from Balkan stock exchanges and determine if there are possible relations among them. Unique aspect of our approach is that we experimentally determine optimal sliding window parameters and optimal number of hidden neurons for the appropriate companies. Also we try to emphasize the main reasons that influents on the forecasting stock market prices.