Web proceedings papers


Miroslav Janeski and Slobodan Kalajdziski


In recent years, use of data mining and machine learning techniques in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. However, the large numbers of parameters that must be selected to develop a good forecasting model have meant that the design process still involves much trial and error. The objective of this paper is to select the optimal parameters for designing of a neural network model for forecasting economic time series data. There is proposed a neural network based forecasting model for forecasting the stock market price movement. The system is tested with data from one Macedonian Stock, the NLB Tutunska Banka stock. The system is shown to achieve an overall prediction rate of over 60%. A number of difficulties encountered when modeling such forecasting model are discussed.


neural network, backpropagation, financial forecasting, time series, stock forecasting