Predicting the BSE Sensex: Performance comparison of adaptive linear element, feed forward and time delay neural networks
Jan 1, 2012·,,
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0 min read
Binoy B. Nair
M. Patturajan
V. P Mohandas
Sreenivasan Ramasamy Ramamurthy
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.
Type
Publication
2012 International Conference on Power, Signals, Controls and Computation
Authors
Assistant Professor (Tenure-Track) of Computer Science
Sreenivasan Ramasamy Ramamurthy is an Assistant Professor (Tenure-Track) of Computer Science at Bowie State University. His research interests include Human-Centered Intelligent Systems, Embodied AI and Robotics, Edge Intelligence, and Human-Machine Teaming. He received his Ph.D. in Information Systems from UMBC, a Master’s in Biomedical Engineering from VIT University, and a Bachelor’s in Electronics and Instrumentation Engineering from Amrita Vishwa Vidyapeetham. He is a recipient of grants from NAVAIR, Army Research Laboratory, and the Department of Energy in support of his research.