M Thenmozhi

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.

The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns. © 2014 Inderscience Enterprises Ltd.

In this study forecasting of the NIFTY stock index futures returns is carried out using backpropagation and recurrent neural network model and a linear ARIMA model. A comparison of different models shows that for NIFTY index futures returns backpropagation neural network model outperforms the recurrent neural network and the traditional ARIMA models. Moreover recurrent neural network models outperform the traditional ARIMA models. A 3-2-1 neural network architecture is best fit for forecasting futures returns.