Lecturer: Peter Bossaerts

Department: Economics

Level: Post grad

Summary: The goal is to better understand human attitudes towards uncertainty in general, and financial risk in particular. The method to get there is to go beyond a pure behaviouralist approach (the tradition of economics), point to the difficulty of deciphering the psychology behind behaviour (the “thinking”/cognition and “feeling”/affect), to eventually land squarely in the domain of neurobiology. After all, humans are biological computers, and they must act accordingly, so why not start there? The approach promises more comprehensive insights than from a purely behavioural study (which makes humans look like a bug-plagued organism), and it bypasses difficult issues of awareness and consciousness (what if people don’t know what they think or feel?). Among others, the results are: novel insights into the role of emotions; an appreciation that neurobiology provides foundations for machine learning (and how the newest in computational neuroscience may yet revolutionise machine learning); a deeper understanding as to whether and how “smart drugs” (popular among students and professionals) work.

Supervision by: Konstantinos Ioannidis

Department: Economics

Level: Undergraduate

Summary: By the end of the course, students should be in possession of a good grasp of the elementary tools of descriptive statistics; should understand elementary principles of probability and statistical theory; should be competent in applying basic methods of statistical inference; and should be familiar with the use of spreadsheets to undertake graphical and statistical analysis of economic data.

Lecturer: Peter Bossaerts

Department: Economics

Level: Post grad

Summary: (Part 1 only). The course focuses on how markets deal with uncertainty. General equilibrium is emphasized, which refers, loosely speaking, to the point at which traders in multiple, simultaneous markets no longer desire further trade. Are the resulting allocations optimal? What if everyone can re-trade in the future after some information is revealed? What if some traders have privileged information? We will not just cover theory, but engage in a dialogue between theory and experiment, as if this were a physics class (for a beautiful version of a physics class, see Feynman’s Lecture Notes). This avoids the impression that (i) economic theory is like ‘legal argument,’ a logically coherent way to think about economics that allows one to set policy, (ii) economic theory is merely a way to make sense (‘rationalize’) economic history, (iii) economic theory is valid if it can somehow be confirmed in historical data. We will at times try to comment on how the theory sheds light on history. A succinct discussion of the approach we take can be found in a chapter of the Handbook of Experimental Finance.

Lecturer: Peter Bossaerts

Department: Economics

Level: Post grad

Summary: The overall aim is to introduce students to the microstructure of modern financial markets in general, and to algorithmic trading in particular. Algorithmic trading refers to the use of robots (automatic order submission computer program) to accomplish a certain trading goal, such as automatic market making, statistical arbitrage, technical analysis, portfolio rebalancing, etc. Students will be given the opportunity to get hands-on experience in purposely designed online financial markets, directly as manual traders, and indirectly by means of python scripts. We use Flex-E-Markets as online trading platform, and a python package called FMClient that allows python scripts to interface with Flex-E-Markets.