Accurate prediction of financial time series (which can be considered as nonlinear systems) especially in relation to emerging markets like India assumes prominence in that, these markets offer significantly higher opportunities for wealth creation for the investor. This paper compares the effectiveness of different types of Adaptive network architectures in one-step ahead prediction of the daily returns of Bombay Stock Exchange Sensitive Index (SENSEX). The performance of each network is evaluated using 17 different performance measures to find the best network architecture. Also, an empirical evaluation of the weak form of Efficient Market Hypothesis (EMH) for the data in reference is carried out here. © 2012 IEEE.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@1325448f ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@73b8121a Through org.apache.xalan.xsltc.dom.DOMAdapter@39aca450; Conference Code:89543
Dr. Binoy B. Nair, Patturajan, M., Mohandas, V. P., and Sreenivasan, R. R., “Predicting the BSE sensex: Performance comparison of adaptive linear element, feed forward and time delay neural networks”, in 2012 International Conference on Power, Signals, Controls and Computation, EPSCICON 2012, Thrissur, Kerala, 2012.