The Role of Investor Sentiment in Cryptocurrency Markets

Shao, Weijia (2025) The Role of Investor Sentiment in Cryptocurrency Markets. Doctoral thesis, University of East Anglia.

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Abstract

The rapid rise of Bitcoin ignited a global frenzy over cryptocurrencies, driving a surge in their issuance and investment. Unprecedented returns on these assets have led to comparisons with irrational exuberance. Against this backdrop, and adopting a behavioural finance perspective, this thesis thoroughly investigates the role of investor sentiment in cryptocurrency prices and volatility across three empirical studies. Sentiment is proxied by the Fear and Greed Index (FGI), published by Alternative.me, and the Economic News Sentiment Index (NSI), developed by the Federal Reserve Bank of San Francisco.

The first study examines volatility connectedness among six Bitcoin (BTC) currency pairs and identifies its determinants. The results indicate that BTC/USD and BTC/GBP are major volatility transmitters, while BTC/USDT remains relatively isolated. Key determinants include trading volume, sentiment, economic policy uncertainty, and gold volatility. There is an asymmetric effect of sentiment derived from the FGI: optimistic sentiment intensifies volatility connectedness, whereas pessimistic sentiment dampens it. Policy uncertainty and gold volatility are positively associated with spillover intensity, with cryptocurrency-related events further shaping the degree of connectedness.

The second study focuses on intraday cross-exchange (i.e., Bitfinex and Kraken) price discovery for Bitcoin and Ethereum, along with its potential drivers. The results show that Bitfinex dominates price discovery during most periods, but the pandemic alters this process. We identify a shift in drivers, with market quality factors losing significance and news sentiment emerging as a more prominent influence in the post-pandemic period. During episodes of heightened news sentiment, Bitfinex consolidates its leading position. Additionally, Ethereum’s price discovery is significantly associated with intraday volatility.
The third study assesses whether the inclusion of the FGI in GARCH(1,1), EGARCH(1,1), and HAR(1,7,30) models improves the accuracy of volatility forecasts for twelve cryptocurrencies. The findings suggest that incorporating the FGI enhances predictive performance, with variation across assets. Bitcoin, Ethereum, and cryptocurrencies technologically linked to them (e.g., Litecoin, DASH and Ethereum Classic) benefit from the integration of sentiment in most cases. EGARCH+FGI and HAR+FGI consistently outperform competing models, particularly at the weekly forecast horizon.

This thesis provides behavioural finance insights for cryptocurrency investors and policymakers. Investors may leverage the FGI and the NSI to refine trading strategies and inform venue selection, while policymakers may incorporate the FGI into monitoring frameworks to better identify systemic risks and anticipate excessive cross-market spillovers.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Depositing User:	Chris White
Date Deposited:	01 Dec 2025 10:32
Last Modified:	01 Dec 2025 10:32
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101158
DOI:

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