A crash course on constructing and using experiments for research and teaching.
Financial economics is rather abstract and mathematical, and its value is difficult to ascertain from merely observing real-world financial markets, which operate in a complex environment where many key variables either remain unobserved or cannot be measured reliably. In this class, students learn about the theory of finance through participation in a series of online market trading sessions.
This is a unique class, yet its format should be familiar from engineering and sciences classes: the theory is taught by means of purposely designed controlled experiments. Here, students will be both participants and observers of the experimental financial markets. The setting therefore provides an invaluable way to both learn to trade financial securities and to reflect on the validity of the theories that were meant to predict the outcomes.
This class was made possible because of 15 years of experimental research with financial markets. Material for the class (software, instructions, excel spreadsheet aids, etc.) was developed with an "Innovation in Education" grant from the provost office at the California Institute of Technology.
Decision neuroscience brings together ideas from economics (decision theory, game theory, etc.), neurobiology, computational neuroscience, computer science and psychology to further our understanding of neural processes behind human choice. A deeper understanding of the algorithms encoded in these processes should clarify the enormous heterogeneity in human choice, over time and across individuals. It is hoped that it will shed light on the true causes behind maladaptive choice (“cognitive biases”) in general, and the symptoms of mental disorders (e.g., schizophrenia, gambling addiction), in particular. Decision neuroscience is a truly interdisciplinary field, where the emphasis is on rigorously controlled experiments and mathematical modeling of choice and neural signals.
The class contains some standard components, such as introductions to decision and game theory, learning theory, neural processes and imaging techniques, and sometimes even a prior on pharmacology. Topics within decision neuroscience are chosen depending on the interests of the students.
Global equity markets have changed fundamentally over the last two decades. Regulatory reforms to promote competition for trading services have led to considerable fragmentation of markets. New entrants and new technology have contributed to innovative new trading mechanisms and pricing structures. Today, markets are overwhelming electronic, with trading occurring using algorithms rather than manually. Students wishing to pursue careers in financial markets need to understand the new market structure that exists and have skills to understand and implement trading strategies in this environment. Our class ensures that students develop these skills and knowledge, through a combination of lectures and hands-on experience of manual and robot trading in online experimental markets.
Students are given the opportunity to get hands-on experience in purposely designed online financial markets, as manual traders, or as algorithmic traders, depending on programming skills and career concerns.
Sample Lecture Notes
- Experimental Finance. An Overview.
- Market Structure
- Diversification and CAPM
- 'Lucas' in the Laboratory
- Markets for Contracts
- Market Bubbles and Crashes