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    An experimental analysis of selected training algorithms for artificial neural network in financial time series prediction
    (01-03-2007)
    Kumar, Manish
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    This study investigates the performance of four training algorithms, namely, Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG), Resilient Backpropgation (RBP) and Levenberg-Marquardt (LM) Backpropagation in forecasting three financial time series, namely, Indian call rates, INR/USD exchange rates and S&P CNX Nifty Index. The models are trained from historical data using six technical indicators. The predicted results show that among the four training algorithms, LM based model outperforms other models when measured on commonly used non-penalty based metrics while SCG based model outperforms the other models when direction and sign based performance metrics are used.