Big Data Tools for Islamic Financial Analysis

E. Mnif, A. Jarboui, M. Kabir Hassan and K. Mouakhar
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E. Mnif: LARTIGE - Laboratoire de recherche en Technologie de l’Information, Gouvernance et Entrepreneuriat - Université de Sfax - University of Sfax
A. Jarboui: Université de Sfax - University of Sfax
K. Mouakhar: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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Abstract: Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent-based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied. © 2020 John Wiley & Sons, Ltd.

Keywords: agent-based simulation; big data; Islamic finance; sentiment analysis; sequential Monte Carlo simulation (search for similar items in EconPapers)
Date: 2020
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Published in Intelligent Systems in Accounting, Finance and Management, 2020, 27 (1), pp.10-21. ⟨10.1002/isaf.1463⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04457135

DOI: 10.1002/isaf.1463

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