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.

This paper provides evidence that forecasts based on global stock returns transmission yield better returns in day trading, for both developed and emerging stock markets. The study investigates the performance of global stock market price transmission information in forecasting stock prices using support vector regression for six global markets—USA (Dow Jones, S&P500), UK (FTSE-100), India (NSE), Singapore (SGX), Hong Kong (Hang Seng) and China (Shanghai Stock Exchange) over the period 1999–2011. The empirical analysis shows that models with other global market price information outperform forecast models based merely on auto-regressive past lags and technical indicators. Shanghai stock index movement was predicted best by Hang Seng Index opening price (57.69), Hang Seng Index by previous day’s S&P500 closing price (54.34), FTSE by previous day’s S&P500 closing price (57.94), Straits Times Index by previous day’s Dow Jones closing price (54.44), Nifty by HSI opening price (60), S&P500 by STI closing price (55.31) and DJIA by HSI opening price (55.22), and Nifty was found to be the most predictable stock index. Trading using global cues-based forecast model generates greater returns than other models in all the markets. The study provides evidence that stock markets across the globe are integrated and the information on price transmission across markets, including emerging markets, can induce better returns in day trading.