Investor sentiment and cryptocurrency market dynamics: A Vector Autoregressive and Granger causality approach

The growing significance of cryptocurrencies as a major asset class has prompted increasing attention to the factors driving market behavior, with investor sentiment being one of the most influential. This study examines the relationship between investor sentiment and cryptocurrency market returns, focusing on the top 10 cryptocurrencies by market capitalization including Bitcoin, Ethereum, XRP, Tether, Dogecoin, Litecoin, Stellar, Neo, Monero, and Ethereum Classic. Using weekly data from January 1, 2016, to December 31, 2021, we construct an investor sentiment index employing five widely recognized sentiment measures: the Money Flow Index (MFI), Crypto Index Turnover (CIT), Relative Strength Index (RSI), Crypto Index (CI), and the Standard & Poor's Global Index (S&PGI). The sentiment index is then analyzed through a Vector Autoregressive (VAR) model and Granger Causality tests to capture the dynamic interplay between sentiment and market returns. The empirical results reveal a significant positive relationship, indicating that investor sentiment is a reliable predictor of cryptocurrency market returns. This study contributes to the growing literature on cryptocurrency market dynamics and offers important policy implications for investors, businesses, and regulators. Our findings suggest that investor sentiment significantly predicts cryptocurrency market returns. The study introduces a sentiment index based on market-based proxies and applies a VAR and Granger causality framework. These insights are relevant for forecasting, trading strategies, and market surveillance.