Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/27903
Title: Interdependency between Classical Economic Indicators and Crypto-Markets
Authors: Todorovska, Ana
Rusevski, Ivan
Peshov, Hristijan
Spirovska, Eva
Marojevikj, Jovana
Angelovski, Gorast
Vodenska, Irena
Chitkushev, Lubomir
Trajanov, Dimitar 
Keywords: Cryptocurrencies, Classical economic indicators, Networks, Natural Language Processing, Machine Learning
Issue Date: Aug-2022
Journal: Available at SSRN 4196995
Abstract: In a global world, no country, market, or economy is isolated. Interconnectivity is becoming a fundamental feature of the economic systems, including macroeconomic trends, traditional financial markets, and digital markets. Cryptocurrencies, as a new digital asset, are becoming an integral part of the global economy. This study aims to explore the relationships between cryptocurrencies and the traditional financial markets. We develop a methodology for analyzing the relationships between largest cryptocurrencies and selected global market-based economic indicators based on multimodal publicly available datasets incorporating structured numerical and unstructured news and social network data. To find the existence of directional associations we developan explainable AI model that first learns the dependencies between different assets and then explains them in a form understandable by humans. We apply our methodology to analyze connectivity networks of seven cryptocurrencies (Bitcoin, Ethereum, Cardano, Chainlink, Litecoin, Stellar, and Ripple) and seven classical economic indicators, including five marked indexes (BSE, Dow Jones, S\&P500, FTSE, and Hang Seng) and two commodity prices (Oil and Gold).
URI: http://hdl.handle.net/20.500.12188/27903
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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