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.

27/11/2012: Neural Data and Investor Behavior

Fascinating stuff... really: a new study, titled "Testing Theories of Investor Behavior Using Neural Data" by Cary Frydman, Nicholas Barberis, Colin Camerer, Peter Bossaerts and Antonio Rangel (link) finds that "...measures of neural activity provided by functional magnetic resonance imaging (fMRI) can be used to test between theories of investor behavior that are difficult to distinguish using behavioral data alone."

How so? "Subjects traded stocks in an experimental market while we measured their brain activity. Behaviorally, we find that, our average subject exhibits a strong disposition effect [the robust empirical fact that individual investors have a greater propensity to sell stocks trading at a gain relative to purchase price, rather than stocks trading at a loss] in his trading, even though it is suboptimal."

More so: "We then use the neural data to test a specific theory of the disposition effect, the “realization utility” hypothesis, which argues that the effect arises because people derive utility directly from the act of realizing gains and losses. [Note to my Investment Theory (TCD) and Financial & Business Environments (UCD) students - we talked about direct utility derived from actual transactions, plus indirect utility effects of learning from same... remember?..] Consistent with this hypothesis, we find that

• activity in an area of the brain known to encode the value of decisions correlates with the capital gains of potential trades, 
• that the size of these neural signals correlates across subjects with the strength of the behavioral disposition effects, and that 
• activity in an area of the brain known to encode experienced utility exhibits a sharp upward spike in activity at precisely the moment at which a subject issues a command to sell a stock at a gain."

Awesome! We might not be wired for living in the world of uncertainty, but we might be somewhat wired for deriving utility out of uncertain gambles?

Now, that's what I call taking investment to MRI and getting results... well, might be not investable results, but...