Research

• Measuring Probabilistic Risk Attitudes (with Che Tat Ng and Richard Gonzalez).

Management Science, 2025. https://doi.org/10.1287/mnsc.2024.04870.

Abstract:  We introduce formal measures for two psychological factors of probabilistic risk attitudes: attractiveness (motivational factor) and discriminability (cognitive factor). Unlike previous approaches that relied on heuristic proxies, our measures precisely capture these two fundamental factors. Our measures are mathematically tractable, robust to discontinuities, such as in the NEO-additive case, and flexible to be applied to any weighting function, as well as to both small and large probabilities. Additionally, through detailed numerical analysis, we examine to what extent existing weighting function parameters capture the two factors: attractiveness and discriminability. Finally, using these new measures, we provide a formal understanding of the independence between motivational and cognitive factors.

• An axiomatization of the Goldstein-Einhorn weighting functions (with Che Tat Ng). 

Journal of Mathematical Psychology, 109, 2022. https://doi.org/10.1016/j.jmp.2022.102669.

Abstract:  In 1999, Richard Gonzalez and George Wu proposed an axiomatization for the widely used Goldstein–Einhorn probability weighting functions. Our present study analyzes the preference conditions in the axioms, leading to the discovery of a larger family of weighting functions. Furthermore, we present a new preference condition which is necessary and sufficient for the Goldstein–Einhorn weighting functions.

• Prospect Theory and Narrow Framing Bias: Evidence from Brazil and Emerging Markets  (with Marcelo Klotzle , Luiz Brandão and Antonio Figueiredo). 

The Quarterly Review of Economics and Finance, 80, p. 90-101, 2021.  https://doi.org/10.1016/j.qref.2021.01.016.

Abstract: Using prospect theory, we analyzed the narrow framing bias in investment decisions in certain emerging countries: Brazil, China, Russia, Mexico, and South Africa. In all cases, we empirically identified the predictive power of prospect theory for stock returns. We also found that the probability weighting function is the most important factor in this predictive power. The relationship between prospect theory and stock returns is different in each country and may be influenced by factors associated with cultural aspects.