Big data tools for Islamic financial analysis

Mnif, Emna; Jarboui, Anis; Hassan, M. Kabir; Mouakhar, Khaireddine

doi:10.1002/isaf.1463

Cited by 15 publications

(6 citation statements)

References 51 publications

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“…Behavioral science states that emotions, principles, and the manner of thinking can affect the behavior of individuals and even investors in their decision‐making on financial markets. Mnif, Jarboui, Hassan, and Mouakhar (2020) tried to measure the investor sentiment by three means of big data. The first was based on a search query of a list of words related to Islamic context.…”

Section: Discussionmentioning

confidence: 99%

“…The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. They conducted a user study in real time with 21 users, and the mean accuracy across all users was 86.42%.Each user gave feedback on the proposed approach, and most of them welcomed the idea.Behavioral science states that emotions, principles, and the manner of thinking can affect the behavior of individuals and even investors in their decision-making on financial markets Mnif, Jarboui, Hassan, and Mouakhar (2020). tried to measure the investor sentiment by three means of big data.…”

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confidence: 99%

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Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

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Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

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“…Behavioral finance provides an explanation of these anomalies and abnormalities using identified psychological, emotional and other social biases (Mnif et al , 2020). Academics mentioned that if one can take an advantage of such anomalies, he/she can earn a superior return (Li et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Ramadan effect and indices movement estimation: a case study from eight Arab countries

Najjar

Assous

Al-Najjar

et al. 2022

JIMA

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Purpose This study aims to investigate the Ramadan effect anomaly on the stock markets’ indices and estimate the movement of these indices in the light of the phenomenon. Design/methodology/approach Stock market indices are used as financial indicators to show the Ramadan effect. To validate this effect, eight Arab countries, which comprises Jordan, Saudi Arabia, Oman, Qatar, United Arab Emirates, Bahrain, Kuwait and Egypt, are adopted. A linear regression with R2, error, F-value and p-value is considered to analyze and understand the effect of Ramadan on the aforementioned Arab countries. Findings Results found that Ramadan has a strong effect on estimating and predicting the performance of stock market indices in all studied Arab countries, except Kuwait. Results found that the majority of the Ramadan effect occurred after the second 10 days of Ramadan, where the direction of stock indices is opposite of Ramadan variables in all aforementioned cases. Originality/value This study is considered as an enrichment of the existing literature review with regard to the Ramadan effect. The study presents a new methodology that can be followed to improve the predictions of stock market indices by using a weight least square method with linear regression. This study presents the most affected periods of time that could decrease or increase the stock prices. Finally, the study proves the capability of the weight least square method in building a predictive model that takes the date into consideration in predicting stock market indices.

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“…Therefore, new approaches must be considered. For example, Mnif et al (2020) tried to measure investor sentiment using big data. They applied a search query of a list of words related to Islamic context and examined the engagement degree on social media and Twitter API (classified into positive and negative directions).…”

Section: Introductionmentioning

confidence: 99%

Wikipedia pageviews as investors’ attention indicator for Nasdaq

Martínez

Orden‐Cruz

Martínez‐Navalón

2022

Intell Sys Acc Fin Mgmt

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Summary The attempt to measure investors’ mood to find an early indicator of financial markets has evolved and developed with the advancement of technology over the years. The first attempts were based on surveys, a long and expensive process. Nowadays, big data has made it possible to measure the investor’s mood accurately and almost entirely online. This paper analyzes the explanatory and predictive capacity of Wikipedia pageviews for the Nasdaq index. For this purpose, two econometric models have been developed. In both models, the explanatory variable is the number of Wikipedia visits, and the endogenous variable is Nasdaq index return. As an alternative to this approach, an algorithmic trading system has been developed. It uses Wikipedia visits as investment signals for long and short positions to check the predictability power of this indicator. It is determined that the volume of queries about Nasdaq companies is a statistically significant variable for expressing the evolution of this index. However, it has no predictive capacity. Keeping in mind the capacity of Wikipedia to exemplify Nasdaq trends, further studies should be conducted to determine how to make this indicator profitable.

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Big data tools for Islamic financial analysis

Cited by 15 publications

References 51 publications

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Ramadan effect and indices movement estimation: a case study from eight Arab countries

Wikipedia pageviews as investors’ attention indicator for Nasdaq

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