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Exploring the Ethereum Merge: Pearson Correlation, Granger Causality, and Wavelet Coherence Analysis of the Lead-Lag Relationship Between Ethereum, Bitcoin, Twitter Sentiment and Twitter Uncertainty

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Exploring the Ethereum Merge: Pearson Correlation, Granger Causality, and Wavelet Coherence Analysis of the Lead-Lag Relationship Between Ethereum, Bitcoin, Twitter Sentiment and Twitter Uncertainty

Giacometti, Mathieu (2023) Exploring the Ethereum Merge: Pearson Correlation, Granger Causality, and Wavelet Coherence Analysis of the Lead-Lag Relationship Between Ethereum, Bitcoin, Twitter Sentiment and Twitter Uncertainty. Masters thesis, Concordia University.

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Abstract

In the realm of cryptocurrencies, the environmental impact of proof-of-work (POW) mining has long been a contentious issue, primarily due to its substantial energy consumption. This paper explores the transition from POW to proof-of-stake (POS) in Ethereum, known as "The Merge," which drastically reduced energy consumption and enhanced scalability and security. Leveraging a vast dataset of over 1.6 million tweets and specific hashtags, this study constructs a Twitter cryptocurrency sentiment index. Along with the Twitter-based Uncertainty Index constructed by Baker et al., 2021, this study delves into the lead-lag relationship between Ethereum, Bitcoin and the two aforementioned indexes using Pearson correlation, Granger causality, and wavelet analysis. We found that Bitcoin and Ethereum exhibit a lead-lag relationship, that the influence of social media sentiment on cryptocurrency prices appeared not to change post-merge and that Bitcoin and Ethereum remained relatively stable and less susceptible to the effects of the merge. Furthermore, the study develops a lead-lag momentum trading strategy, highlighting a robust relationship between Ethereum and Bitcoin prices, offering lucrative trading opportunities. This research contributes to a deeper understanding of cryptocurrency market dynamics and their interconnectedness with social media sentiment.

Divisions:Concordia University > John Molson School of Business > Finance
Item Type:Thesis (Masters)
Authors:Giacometti, Mathieu
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Finance
Date:11 October 2023
Thesis Supervisor(s):Proelss, Juliane
ID Code:993117
Deposited By: mathieu giacometti
Deposited On:05 Jun 2024 15:37
Last Modified:05 Jun 2024 15:37
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