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Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning
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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 Qiang Swan (SUNY Buffalo State, USA)
DOI: 10.4018/IJDA.2021010101
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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.
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