Elise Payzan-Le Nestour

PROFESSOR OF FINANCE
UNSW Business School
Sydney
email: elise@elisepayzan.com

elise

Elise Payzan-Le Nestour

PROFESSOR OF FINANCE
UNSW Business School
Sydney
email: elise@elisepayzan.com

Research

Publications

Craving Money? Evidence from the Laboratory and the Field with James Doran, Science Advances, vol 6 issue 2, 2024

Gist: We provide evidence that in gambling contexts, humans may crave monetary rewards in the same way that they crave primary rewards such as food.

Reproducibility in Management Science Fišar, M., Greiner, B., Huber, C., Katok, E., Ozkes, A., and the Management Science Reproducibility Collaboration, Management Science, 70(3), 2023

Note: Member of the Management Science Reproducibility Collaboration.

Neurofinance in Handbook of Financial Decision making (edited by David McLean and Gilles Hillary),  Edward Elgar Publishing, 2023

Gist: Research in neurofinance attempts to apply what we know at the neurobiological level to investor decision-making.

Outlier Blindness: A Neurobiological Foundation for Neglect of Financial Risk with Michael Woodford, The Journal of Financial Economics, vol 143 issue 3, 2022 (code|data)

Gist: A well-known feature of brain function can lead people to initially underestimate the importance of regime shifts and macroeconomic shocks.

Biased Risk Perceptions: Evidence from the Laboratory and Financial Markets with Lionnel Pradier and Talis Putnins, Journal of Banking and Finance, 106685, 2022

Gist: This same brain function can also lead investors to be biased in their perception of volatility, and this perceptual bias distorts asset prices.

Harnessing Neuroscientific Insights to Generate Alpha with James Doran, Lionnel Pradier and Talis Putnins, Financial Analysts Journal, 78:2, 2022

Gist: We construct a trading strategy that exploits the foregoing bias in investor perception.

Impact of Ambient Sound on Risk Perception in Humans: Neuroeconomic Investigations with Bernard Balleine, James Doran, Gidi Nave and Lionnel Pradier, Nature — Scientific Reports, 2021

Gist: We provide evidence that the psychophysics notion that “What we see is what we hear” extends to the visual perception of risk generally, and of financial risk (“volatility”) in particular: the buzz would help traders better ‘feel’ risk.

Can People Learn About ‘Black Swans’? Experimental Evidence Review of Financial Studies, vol 31 issue 12, 2018 (code|data)

Gist: There appears to be important interindividual differences in the capacity to learn about “tail risk”—the risk of being hurt by rare disasters (the so-called ‘black swans’), and many people do not protect themselves adequately against this risk.

Variance After-Effects Bias Risk Perception in Humans with Bernard Balleine, Tony Berrada and Joel Pearson, Current Biology, vol 26, 2016 (code|data)

Gist: We document a novel kind of perceptual bias, entitled “risk after-effect”, which implies that traders that have been dealing with highly risky products tend to underestimate risk.

Learning About Unstable, Publicly Unobservable Payoffs with Peter Bossaerts, Review of Financial Studies, vol 28 issue 7, 2015 (code|data)

Gist: People can detect unexpected changes in their investment sets and adjust their investment plans accordingly, but they are dependent on the quantity and format of information disclosed about the statistics of their environment.

The Neural Representation of Unexpected Uncertainty During Value-Based Decision Making with Simon Dunne, Peter Bossaerts and John O’Doherty, Neuron, vol 79 issue 1, 191-201, 2013

Gist: We provide evidence that the LC/NE system plays a key role in human adaptation to abrupt changes.

Do Not Bet On The Unknown Versus Try To Find Out More: Estimation Uncertainty And ”Unexpected Uncertainty” Both Modulate Exploration with Peter Bossaerts, Frontiers in Neuroscience 6:150, 2012

Gist: People’s desire to explore new possibilities in their world is modulated by their fear of the unknown.

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings with Peter Bossaerts, PLoS Computational Biology 7(1), 2011

Gist: At the neural level, Bayesian learning under regime shifts ought to involve the separate encoding of three kinds of uncertainty.

Work in Progress

Predicting Asset Return Shifts: Experimental Evidence on Human Forecasting Ability, with Yunshen Yang and Qihe Tang

Somatic markers underlying harmful gambling, with Nina Sooter, Andrea Tugnoli, and Giuseppe Ugazio

Pavlovian “Sign-tracking” Influences Repeated Risk-Taking in Humans, with Samuel Thelaus and Bernard Balleine