Bubbles in Dark Markets and CAPM in Dark Markets are two ULEEF projects that will be presented at the Experimental Finance Conference.
Elena Asparouhova is co-organizing and co-teaching at the Society for Experimental Economics and Finance Summer School .
Utah State University Experimental Economics Workshop
Keynote Speakers are the Nobel Laureate Prof. Vernon Smith (Chapman) and Prof. Dan Houser (George Mason University).
In this workshop 10 to 12 participants each have one hour to present and discuss their paper. ULEEF's own Corina Besliu and Wenhao Yang are on the program. The focus of this workshop is experimental research with policy relevance.
Experimental Finance Meets FMA
With the support and encouragement of FMA's President Jeff Coles, the annual meetings hosted 4 sessions with experimental finance research.
The group of experimental researches that gathered in Boston included Elena Asparouhova, Corina Besliu, Peter Bossaerts, Nelson Camanho, Sean Crockett, Mark DeSantis, Ann Gillette, Zwetelina Iliewa, Chad Kendall, Brian Kluger, Jennifer Miele, Tibor Neugebauer, Elena Pikulina, David Porter, Sébastien Pouget, Alexander Wagner, and Wenhao Yang.
The sessions, discussions and meals we shared were nothing short of amazing.
Thank you to all who participated!
We rolled out the new Software as a Service (SaaS) Flex-E-Markets
Workshop for the Promotion of Experimental Validation of the Theory of Asset Pricing
The workshop is to brought together experimentalists and theorists working on financial markets, in order to promote the scientific validation of the theory of finance, in the true meaning of the term, through the use of controlled experiments. We hope that the workshop heralds a new era where theorists work in closer collaboration with experimentalists.
For a second time, we will organize a trading session during the Utah Winter Finance Conference at Snowbird, Utah. Click here for detailed information.Summary
This year you will be endowed with claims issued against a pool of money. Across several trading rounds, you will be able to trade these claims on an electronic exchange system, Flex-e-Markets. In addition to trading, you will also be able to submit the claims to ULEEF, to be cashed in for a known amount called the face value. The face value increases each trading round. The pool of money is used to pay those who want to cash in. Over time, the money in the pool grows but at a slower rate than the face value.
If there is insufficient money in the pool to honor the requested cash-ins at face value, the pool is put into liquidation and all outstanding claims will be paid pro rata. Trading will last at most 5 rounds. Earnings equal final cash minus the cash you are initially endowed with.
Acknowledgment: Funding for the 2015 UWFC trading exercise is provided by the U.S. National Science Foundation under grant SES-1426428
•'Lucas' in the Laboratory, with by Elena Asparouhova, Peter Bossaerts, Nilanjan Roy, and Bill Zame forthcoming in the Journal of Finance
We study the Lucas asset pricing model in a controlled setting. Key experimental design features allow us to emulate the stationary, infinite-horizon setting of the model and to incentivize participants to smooth earnings (consumption) across periods. Consistent with the Lucas model, prices were aligned with consumption betas, they commoved with aggregate dividends, and more strongly so in sessions with higher risk premia. Trading significantly increased participants’ payoffs relative to the autarky as it smoothed their consumption. Nevertheless, prices were excessively volatile: at most 18\% of price changes was explained by aggregate dividends; the remainder was noise. This corrupted traditional GMM tests of the model because these rely on returns and returns were computed as ratios of noisy prices. Choices displayed substantial heterogeneity, to the extent that the average trades and prices did not reflect the experience of any one individual.
Neoclassical finance assumes that investors are Bayesian. In many realistic situations, Bayesian learning is challenging. We study investment opportunities that change randomly, while payoffs are only observable when investment takes place. In a stylized version of the task, we investigate whether performance is affected if one were to follow reinforcement learning principles instead. The answer is a definite yes. Participants overwhelmingly learned in a Bayesian way. They stopped being Bayesians, though, when not nudged into paying attention to contingency shifts. This raises an issue for financial markets: who has the incentive to nudge investors?
Chimpanzees evidently are better than humans to figure out opponents’ incentives in a competitive game, and end up playing closer to predictions of mathematical game theory. Collaboration with the Camerer lab at Caltech.