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Application of artificial neural networks to price forecasting in the stock exchange market

Abstract:

An artificial neural network model to forecast the price of two of the main shares traded in the Colombian stock exchange is proposed in this work. The model is applied to study the shares of Ecopetrol and Preferencial Bancolombia, companies that trade in the stock exchanges of Colombia and New York. Two network structures including the daily price series in the first and the price series plus the dollar index DXY in the latter are used. Different neural networks configurations are trained using a series of six months, where five months are used as training patterns and the next month is left to test the pbkp_redictive capabilities of the network. The results show good performance of the neural networks with low training and testing errors.