IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning

Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning
View Sample PDF
Author(s): Manas K. Sanyal (Department of Business Administration, University of Kalyani, India), Indranil Ghosh (Department of Operations Management and IT, Calcutta Business School, India) and R. K. Jana (Operations and Quantitative Methods Area, Indian Institute of Management, Raipur, India)
Copyright: 2021
Volume: 2
Issue: 1
Pages: 31
Source title: International Journal of Data Analytics (IJDA)
Editor(s)-in-Chief: Bruce Sun (SUNY Buffalo State, USA)
DOI: 10.4018/IJDA.2021010101

Purchase


Abstract

This paper proposes a granular framework for examining the dynamics of stock indexes that exhibit nonparametric and highly volatile behavior, and subsequently carries out the predictive analytics task by integrating detrended fluctuation analysis (DFA), maximal overlap discrete wavelet transformation (MODWT), and machine learning algorithms. DFA test ascertains the key temporal characteristics of the daily closing prices. MODWT decomposes the time series into granular components. Four pattern recognition algorithms—adaptive neuro fuzzy inference system (ANFIS), dynamic evolving neural-fuzzy inference system (DENFIS), bagging and deep belief network (DBN)—are then used on the decomposed components to obtain granular level forecasts. The entire exercise is performed on daily closing prices of Dow Jones Industrial Average (DJIA), National Stock Exchange of India (NIFTY), Karachi Stock Exchange (KSE), Taiwan Stock Exchange (TWSE), Financial Times Stock Exchange (FTSE), and German Stock Exchange (DAX). MODWT-Bagging and MODWT-DBN appear as superior forecasting models.

Related Content

. © 2022.
Sonam Gupta, Lipika Goel, Abhay Kumar Agarwal. © 2021. 14 pages.
Ramesh R., Udayakumar E., Srihari K., Sunil Pathak P.. © 2021. 11 pages.
Arti Saxena, Vijay Kumar. © 2021. 14 pages.
Dhyan Chandra Yadav, Saurabh Pal. © 2021. 17 pages.
Manas K. Sanyal, Indranil Ghosh, R. K. Jana. © 2021. 31 pages.
V. Sakthivel Samy, Koyel Pramanick, Veena Thenkanidiyoor, Jeni Victor. © 2021. 29 pages.
Body Bottom