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Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode algorithm and differential evolution

Prediction of financial time series and its volatility using a hybrid dynamic neural network... A dynamic neural network (DNN) and a new computationally efficient functional link artificial neural network (CEFLANN) combination optimised with differential evolution (DE) is presented in this paper to predict financial time series like stock price indices and stock return volatilities of two important Indian stock markets, namely the Reliance Industries Limited (RIL), and NIFTY from one ahead to one month in advance. The DNN comprises a set of 1st order IIR filters for processing the past inputs and their functional expansions and its weights are adjusted using a sliding mode strategy known for its fast convergence and robustness with respect to chaotic variations in the inputs. Extensive computer simulations are carried out to predict simultaneously the stock market indices and return volatilities and it is observed that the simple IIR-based DNN-FLANN model hybridised with DE produces better forecasting accuracies in comparison to the more complicated neural architectures. Keywords: dynamic neural network; DNN; FLANN; stock price forecasting; stock return volatility prediction; sliding mode; differential evolution; DE. Reference to this paper should be made as follows: Bisoi, R. and Dash, P.K (2015) `Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode algorithm and differential evolution

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Publishers
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2015.068757
Publisher site
See Article on Publisher Site

Abstract

A dynamic neural network (DNN) and a new computationally efficient functional link artificial neural network (CEFLANN) combination optimised with differential evolution (DE) is presented in this paper to predict financial time series like stock price indices and stock return volatilities of two important Indian stock markets, namely the Reliance Industries Limited (RIL), and NIFTY from one ahead to one month in advance. The DNN comprises a set of 1st order IIR filters for processing the past inputs and their functional expansions and its weights are adjusted using a sliding mode strategy known for its fast convergence and robustness with respect to chaotic variations in the inputs. Extensive computer simulations are carried out to predict simultaneously the stock market indices and return volatilities and it is observed that the simple IIR-based DNN-FLANN model hybridised with DE produces better forecasting accuracies in comparison to the more complicated neural architectures. Keywords: dynamic neural network; DNN; FLANN; stock price forecasting; stock return volatility prediction; sliding mode; differential evolution; DE. Reference to this paper should be made as follows: Bisoi, R. and Dash, P.K (2015) `Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2015

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