21/1/20: Investor Fear and Uncertainty in Cryptocurrencies

Our paper on behavioral biases in cryptocurrencies trading is now published by the Journal of Behavioral and Experimental Finance volume 25, 2020:

We cover investor sentiment effects on pricing processes of 10 largest (by market capitalization) crypto-currencies, showing direct but non-linear impact of herding and anchoring biases in investor behavior. We also show that these biases are themselves anchored to the specific trends/direction of price movements. Our results provide direct links between investors' sentiment toward:

1. Overall risky assets investment markets,
2. Cryptocurrencies investment markets, and
3. Macroeconomic conditions,

and market price dynamics for crypto-assets. We also show direct evidence that both markets uncertainty and investor fear sentiment drive price processes for crypto-assets.

9/1/20: Herding and Anchoring in Cryptocurrency Markets

Our new paper, with Daniel O'Loughlin, titled "Herding and Anchoring in Cryptocurrency Markets: Investor Reaction to Fear and Uncertainty" has been accepted to the Journal of Behavioral and Experimental Finance, forthcoming February 2020.

The working paper version is available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3517006.

Abstract:
Cryptocurrencies have emerged as an innovative alternative investment asset class, traded in data-rich markets by globally distributed investors. Although significant attention has been devoted to their pricing properties, to-date, academic literature on behavioral drivers remains less developed. We explore the question of how price dynamics of cryptocurrencies are influenced by the interaction between behavioral factors behind investor decisions and publicly accessible data flows. We use sentiment analysis to model the effects of public sentiment toward investment markets in general, and cryptocurrencies in particular on crypto-assets’ valuations. Our results show that investor sentiment can predict the price direction of cryptocurrencies, indicating direct impact of herding and anchoring biases. We also discuss a new direction for analyzing behavioral drivers of the crypto assets based on the use of natural language AI to extract better quality data on investor sentiment.