A Better Understanding of Granger Causality Analysis: A Big Data Environment

Song, Xiaojun; Taamouti, Abderrahim

doi:10.1111/obes.12288

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…The problem is that big data is much more focused on correlation than on causality and thus ignores average events or conditions (Song and Taamouti, 2019;Wamba et al, 2020). (4) Electrification: the Fourth Industrial Revolution concerns the sustainability aspect of production and the environmental aspect, and the technical aspect of converting fossil energy to renewable energy and resource efficiency.…”

Section: Conceptual Backgroundmentioning

confidence: 99%

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The Role and Meaning of the Digital Transformation As a Disruptive Innovation on Small and Medium Manufacturing Enterprises

et al. 2021

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The research reported in this paper explores the impact of digital transformation as a disruptive innovation on manufacturing SMEs. The research is based on a qualitative Delphi study encompassing 49 experts from eleven EU countries. The paper aims to demonstrate how disruptive innovations affect organizational changes and determine critical factors in organizations that impact the initiating and promoting R&D of disruptive innovation. We discovered that disruptive innovations impact product/process development methods, new production concepts, new materials for products, and new organization plans. Additionally, we identified organizational changes related to the development and use of disruptive innovations in the future. We also indicate how disruptive innovations influence social and technological changes in the organizational environment. The analysis also disclosed three main groups of disruptive innovations and their impact on future smart factory development, namely the following: technological changes, the emergence of innovative products, business models and solutions and organizational culture as one of the crucial key success factors. The analysis also examined the enablers of the successful development/introduction of disruptive innovations, wherein internal and external factors were determined. Additionally, we presented obstacles and the approaches necessary to mitigate them. We can conclude from the findings that in the timeframe of 5–10 years, only the SME that uses/develops disruptive innovations will survive in the market. However, the companies do not always have a clear idea of the meaning of disruptive innovations. Therefore, it is important to set clear goals regarding the achievement of disruptive innovations in companies. It is also necessary to creatively apply presented instruments enabling improvement of organizational changes and apply some additional concepts, which we have suggested.

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Section: Conceptual Backgroundmentioning

confidence: 99%

“…It is necessary to be aware that big data overlaps or neglects irregularities unless we enable this with a search-analytical algorithm. The problem is that big data is much more focused on correlation than on causality and thus ignores average events or conditions ( Song and Taamouti, 2019 ; Wamba et al, 2020 ).…”

Section: Conceptual Backgroundmentioning

confidence: 99%

The Role and Meaning of the Digital Transformation As a Disruptive Innovation on Small and Medium Manufacturing Enterprises

et al. 2021

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“…Where 𝑎 0 is the coefficient of the intercept and 𝜖 is the error term (Song & Taamouti, 2019). In essence, H0: No Causality effect between RV and VOL because the p-value is more than 5%.…”

Section: Method Data and Analysismentioning

confidence: 99%

Exploring the relation between realised volatility and Trading volume: Evidence from international stock market

Enow

2023

J. Keuang. dan Perbank.

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The sequential information theory and mixed distribution hypothesis contends that there exists a bi-directional relation between realised volatility and trading volume. This position has led to the proposition that new information spreads sequentially and reaches market participants and investors at varying times. The purpose of this study was to re-examine these theories using the most recent data. A Granger causality test, Mean Square Error and Mean Average error models where applied to investigate the relationship between realised volatility and trading volume for a sample of five international stock markets from March 5, 2018 to March 5, 2023. The findings of this study contradict the proposition put forth by the sequential information theory and mixed distribution hypothesis where no meaningful relationship was observed except for the CAC 40. Hence, new information rather filters through financial markets at the same time. The finding of this study maybe the explanation for the ever-increasing financial contagion between financial markets. DOI: 10.26905/jkdp.27i2.9777

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“…The primary focus of granger causality tests is predictability, which is significant to economists and policymakers. According to Song and Taamouti (2019), granger causality is commonly examined for bivariate processes. The bivariate Granger-causality is given in the below equations:…”

Section: Granger Causality Testmentioning

confidence: 99%

Exploring the nexus between price and volume changes in the cryptocurrency market

2023

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The analysis of the price-volume relationship offers valuable insights into the underlying dynamics of financial markets, enabling investors and market participants to make informed inferences based on the data derived from price and volume. This concept has extensively been studied in the stock market, while it remained understudied in the cryptocurrency trading system. The study aims to contribute to the literature by examining the causal relationship between price changes and trading volume for the top three cryptocurrencies (Bitcoin, Ethereum and Tether) listed on CoinMarketCap. Previous research that examined causal relationship focused on Bitcoin and Ethereum, while neglecting Tether, which is often used by investors to avoid price volatility. The study employs correlation matrix, vector autoregression Model, and granger causality to conduct the analysis of the research. The data for this study were extracted from CoinMarketCap, which provides the daily closing price and 24 hours traded volume of the top three cryptocurrencies from January 1, 2016 to December 31, 2021. The outcome of the preliminary study suggested that there is a positive significant price-volume relationship for Bitcoin and Ethereum, while there is a negative relationship for Tether. Furthermore, the study exhibits that there is a unidirectional causality between price return and volume change of Bitcoin and Ethereum, while there is no evidence of causality for Tether. Therefore, the empirical results establish the reliability of variable forecasting, empowering market participants and investors to make informed investment decisions.

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A Better Understanding of Granger Causality Analysis: A Big Data Environment

Cited by 15 publications

References 28 publications

The Role and Meaning of the Digital Transformation As a Disruptive Innovation on Small and Medium Manufacturing Enterprises

The Role and Meaning of the Digital Transformation As a Disruptive Innovation on Small and Medium Manufacturing Enterprises

Exploring the relation between realised volatility and Trading volume: Evidence from international stock market

Exploring the nexus between price and volume changes in the cryptocurrency market

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