Abstract
We analyzed the possibility of predicting stock prices on a short-term, day-to-day basis with help of neural networks by studying three important German stocks chosen at random (BASF, COMMERZBANK, MERCEDES). We examined the use of PERCEPTRON, ADALINE, MADALINE and BACK-PROPAGATION networks. The results were encouraging. Within short prediction time spans (10 days), we achieved a very hight degree of accuracy of up to 90%. With a BACK-PROPAGATION network we carried out an absolute-value prediction. The network was thereby able to recognize on its own an obvious heuristic and showed a behaviour similar to the exponential smoothing algorithm. The results we achieved led us to expect that neural network could considerably improve the prognosis of stock prices (and more generally, the prognosis of semi-chaotic time series) in the future. Nevertheless considerable improvements are needed in the theory of neural networks, as practicable methods to support the design of neural networks for specific applications are not available yet.
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