Topic: Asset Pricing under Computational Complexity
Speaker: Peter Bossaerts, Professor of Experimental Finance and Decision Neuroscience, the University of Melbourne
Date: December 5 (Wednesday)
Location: Building 4 Room 101
We often think of investments as playing roulette, with “laws” that somehow can be discovered using statistics or machine learning, and optimal policies that can be acquired through reinforcement learning. Yet many investment problems actually fall in a completely different category. Firm valuation, determining what to look for when predicting markets, even portfolio construction, are not statistical problems, but computationally complex decision problems. These require methodic approaches that resonate with the theory of computation, and individuals do tend to follow those, even to the extent that the theory, developed for electronic computers, predicts human performance. But what about markets? I show that markets ought to treat these problems as if they were statistical ones, and as a result, should underperform the average investor. Experiments confirm this prediction. Still, markets help individuals make better decisions, and the improvements appear to depend on security design. This suggests a novel aim for markets, that of transmitting crucial, even if limited, information, rather than that of revealing all available information (the Efficient Markets Hypothesis). This resonates well with Friedrich Hayek’s original conjecture of the role of markets in information transmission. And it suggest that markets can play a kind of “oracle” role as defined in the theory of computation.
About the speaker:
Peter Bossaerts is Redmond Barry Distinguished Professor and Professor of Experimental Finance and Decision Neuroscience at The University of Melbourne, and Fellow at The Florey Institute of Neuroscience and Mental Health. He pioneered the use of controlled experimentation (with human participants) in the study of financial markets. He also pioneered the use of decision and game theory in cognitive neuroscience, thereby helping establish the novel fields of neuroeconomics and decision neuroscience. Recently, he has pioneered the use of computational complexity theory in the study of human behavior. He graduated with a PhD from UCLA, and spent most of his career at the California Institute of Technology (Caltech). He also worked at Carnegie Mellon University and EPFL (ETH-Lausanne), among others. He is Fellow of the Econometric Society, the Academy of the Social Sciences in Australia, and the Society for The Advancement of Economic Theory.