Title: Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode algorithm and differential evolution
Authors: Ranjeeta Bisoi; P.K. Dash
Addresses: Multidisciplinary Research Cell, Siksha O Anusandhan University, Bhubaneswar, Khandagiri Square, Bhubaneswar-751030, Odisha, India ' Multidisciplinary Research Cell, Siksha O Anusandhan University, Bhubaneswar, Khandagiri Square, Bhubaneswar-751030, Odisha, India
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 day 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 networks; DNNs; FLANNs; functional link ANNs; artificial neural networks; stock price forecasting; stock return volatility; volatility prediction; sliding mode; differential evolution; financial time series; India; stock markets; simulation; infinite impulse response; IIR filters.
International Journal of Information and Decision Sciences, 2015 Vol.7 No.2, pp.166 - 191
Available online: 09 Apr 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article