The Interplay of Macroeconomic Sentiments at Financial Markets: A Comparison of S&P Stock and Cryptocurrency Index

The global financial system is constantly evolving through technological integration. This has led to the inception and rise in the cryptocurrency market, opening new avenues of comparative studies on market behavior. Therefore, the current study aimed to identify nuances in stock and cryptocurrency behavior. Based on the socionomic theory of finance, the study is a pioneer in considering the interplay of economic, market, and social media sentiments while providing a comparative view of cryptocurrencies and stocks. The study utilizes data of economic news sentiments, cryptocurrency fear and greed index, CNN fear and greed index, and Twitter sentiments against the movement of S&P Cryptocurrencies and S&P 500 stock index return spanning from 2018 to 2023. The study applied a vector autoregressive-based spillover model to assess the theorized linkage and applied robustness measures, including linear regression and the Granger causality test, for validation. The findings unveil distinct weak and moderate associations of sentiments across cryptocurrencies and stocks, respectively. The former is primarily driven by market sentiments while shaping economic news and social media sentiments. Meanwhile, the findings for stock return movements are found to be significantly associated with economic and market sentiments. This led to the inference that the cryptocurrency environment is an isolated system driven by internal sentiments, while stock markets are more economically integrated, and in both cases, social media sentiments are found to be the receiver of market spillover, weakly influencing economic news. The study is pioneering in its exploration of the interlinkage between selected macroeconomic sentiments; additionally, the comparative findings further add to the existing debate on influence of sentiment across financial markets. The varying realities identified in the findings hold significant practical implications for portfolio optimization, risk assessment and policy making.