Geodesic
Semi-Chaotic Financial Series and Neural Network Forecasting: Evidence from European Stock Markets
Yugoslav journal of operations research, Tome 6 (1996) no. 2, p. 305 .

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This study examines time series from five European Stock Markets (UK. Germany, Belgium, Spain and Greece). Based on empirical evidence concerning nonlinear dependence, long-term memory effects and low-dimensional chaos, we assess the predictability of the series and determine the appropriate parameters for neural network modeling. We apply artificial neural network forecasting data sets from the semi-chaotic Greek Stock Exchange and utilize the outputs in the construction of a trading system. It is found that the neural network trading system performs significantly above random chance and other investment strategies.
Classification : 91B84 91B28
Keywords: Chaos theory, R/S analysis, financial forecasting, artificial neural networks, European capital markets 
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     author = {Costas Siriopulos and Raphael Markellos},
     title = {Semi-Chaotic {Financial} {Series} and {Neural} {Network} {Forecasting:} {Evidence} from {European} {Stock} {Markets}},
     journal = {Yugoslav journal of operations research},
     pages = {305 },
     publisher = {mathdoc},
     volume = {6},
     number = {2},
     year = {1996},
     zbl = {0949.91536},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/YJOR_1996_6_2_a14/}
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Costas Siriopulos; Raphael Markellos. Semi-Chaotic Financial Series and Neural Network Forecasting: Evidence from European Stock Markets. Yugoslav journal of operations research, Tome 6 (1996) no. 2, p. 305 . http://geodesic.mathdoc.fr/item/YJOR_1996_6_2_a14/