Using ML and Explainable AI to understand the interdependency networks between classical economic indicators and crypto-markets

Abstract

In a global world, no country, market, or economy is isolated. Interconnectivity is becoming a fundamental feature of 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 traditional financial markets. We develop a methodology for analyzing the relationships between the largest cryptocurrencies and select 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 develop an Explainable ML 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 market indexes (BSE, Dow Jones, S&P500, FTSE, and Hang Seng) and two commodity prices (Oil and Gold).