Unravelling Cryptocurrency and Stock Market Dynamics: Predictive Models and Macroeconomic Implications

Chong, Tze Yeng (2023) Unravelling Cryptocurrency and Stock Market Dynamics: Predictive Models and Macroeconomic Implications. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study examines the complex relationships between cryptocurrencies and stock markets, including volatility dynamics, price prediction, and the influence of macroeconomic factors. Using a variety of models, including ARIMA, VAR, and LSTM, and leveraging feature importance analysis with Random Forest regression, our research reveals significant correlations between cryptocurrency and stock markets, which are accentuated during times of economic uncertainty. The LSTM model forecasts cryptocurrency prices with the highest accuracy among all predictive models. The findings demonstrate the importance of stock market performance and interest rates in determining the price trends of cryptocurrencies. Our research has implications for both academics and investors, promoting informed investment decisions. Future research should expand sample sizes and investigate a broader range of macroeconomic factors, thereby enhancing our understanding of this intricate financial interaction.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Byrne, Brian UNSPECIFIED
Uncontrolled Keywords:	Cryptocurrency; Stock Markets; Price Predictions; Macroeconomic Indicators; Machine Learning Models
Subjects:	H Social Sciences > HG Finance > Money > Digital currency > Cryptocurrencies H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Tamara Malone
Date Deposited:	02 Aug 2024 12:12
Last Modified:	02 Aug 2024 12:12
URI:	https://norma.ncirl.ie/id/eprint/7017

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