Title: Forecasting intraday stock price using ANFIS and bio-inspired algorithms

Authors: S. Kumar Chandar

Addresses: School of Business and Management, CHRIST (Deemed to be University), Bengaluru, 560029, India

Abstract: The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods.

Keywords: adaptive neuro-fuzzy inference system; ANFIS; bio-inspired algorithm; genetic algorithm; intraday day; minute price; particle swarm optimisation; PSO.

DOI: 10.1504/IJNVO.2021.117754

International Journal of Networking and Virtual Organisations, 2021 Vol.25 No.1, pp.29 - 47

Received: 28 Jun 2020
Accepted: 21 Dec 2020

Published online: 17 Sep 2021 *

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