Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its Asian and European counterparts

Abstract

Recently, the price of a stock market changes often owing to a variety of factors. As a result, making an accurate stock price prediction is a difficult process. Hence, this research work proposes a novel intellectual stock market prediction model that incorporates the volatility spillover over Indian and its Asian countries. This intellectual model mainly involves two phases like data library construction and stock market prediction. For stock market prediction, a Neural Network (NN) model is employed and this model intake the data of calculated indicators in the data library and makes the prediction of Indian market. To attain more precise prediction, the NN weight is optimally chosen via novel hybrid algorithm namely Fly Updated Whale Optimization Algorithm (FU-WOA) that is the hybridization of WOA and Firefly Algorithm (FF). At last, the suggested model performance is exploited by comparing other conventional models in the view of various metrics. Especially, the computational cost of the proposed hybrid FU-WOA–NN model is 38.12%, 15.96%, 15.52%, 41.22%, 16.07% and 16.33% better than existing LM-NN, FF-NN, GWO-NN, WOA-NN, PCA as well as ARIMA methods respectively.