Predicting the BSE Sensex: Performance comparison of adaptive linear element, feed forward and time delay neural networks

Abstract

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.

Sreenivasan Ramasamy Ramamurthy

Assistant Professor of Computer Science

My research interests includes Mobile Computing, Machine Learning, Cyber-Physical Systems