Title: Two-phase hybridisation using deep learning and evolutionary algorithms for stock market forecasting

Authors: Raghavendra Kumar; Pardeep Kumar; Yugal Kumar

Addresses: Department of Computer Science & Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India; Department of Information Technology, KIET Group of Institutions, Ghaziabad, UP, India ' Department of Computer Science & Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India ' Department of Computer Science & Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

Abstract: In this paper, a two-phase hybrid model is proposed for stock market forecasting using deep learning approach and evolutionary algorithms. In the first phase of hybridisation, Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) are combined to compose linear and non-linear features of the data set. In the second phase, an improved Artificial Bee Colony (ABC) algorithm using Differential Evolution (DE) is used for the hyperparameter selection of proposed hybrid LSTM-ARIMA model. In this paper, experiments are performed over 10 years of the data sets of Oil Drilling & Exploration and Refineries sector of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) from 1 September 2010 to 31 August 2020. Obtained result demonstrates that the proposed LSTM-ARIMA hybrid model with improved ABC algorithm has superior performance than its counterparts ARIMA, LSTM and hybrid ARIMA-LSTM benchmark models.

Keywords: hybrid model; ARIMA; auto regressive integrated moving average; LSTM; long short-term memory; ABC; artificial bee colony.

DOI: 10.1504/IJGUC.2021.120120

International Journal of Grid and Utility Computing, 2021 Vol.12 No.5/6, pp.573 - 589

Received: 04 Mar 2021
Accepted: 06 May 2021
Published online: 07 Jan 2022 *

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