Leverhulme International Professorship in Neuroeconomics at the University of Cambridge Faculty of Economics

Author: Robert Woods

  • Part I Paper 3 Quantitative Methods in Economics (Statistics only)

    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…

  • D002 Introduction to Algorithmic Trading Robot Design

    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…

  • PhD10 Economic Theory (Part 1)

    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…

  • F520 Behavioural Finance

    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…

  • Instrument building for financial markets research and teaching (including algorithmic trading)

    Experimental research on markets requires software with which one can readily set up multiple simultaneous markets where human participants can exchange tailor-made goods and securities. The software has to be flexible enough so many relevant economics situations can be studied (and taught). Exchange mechanisms have to be realistic, yet amenable to control. Because of the…

  • Learning in financial markets (human and artificial agents)

    Financial markets generate a type of risk which is rather unusual, in that there are lots of extreme events (“outliers”): either the market is very calm, or stormy; there does not seem to be a “normal” level of uncertainty. This raises the questions as to whether humans, or artificial agents (machine learning), are well prepared…

  • Markets as mechanisms to re-allocate risk, collect information, and spread knowledge

    The standard view of financial markets is that they serve an important role in re-allocating risk (across agents and over time). But they appear to be equally important as a tool to gather information (prediction markets), and to spread knowledge — whether that knowledge is pure data points or it helps resolve computational complexity.

  • Markets off equilibrium

    Before they got focused on equilibrium, economists (most prominently, Cambridge’s Alfred Marshall) actually explored markets behaviour without assuming that it somehow converges to equilibrium. This endeavour was put on the back burner however, because of lack of tools (mathematical framework and analysis; experimental methodology). We have worked at addressing this situation, engaging in a dialogue…

  • Markets: equilibrium predictions

    Virtually all economic models of social interaction, including through markets, assumes that outcomes reflect a system that is “in equilibrium.” Equilibrium restrictions are used to interpret historical data from the field and advise policy makers such as central bankers. But absent an economic law of “entropy,” it is by far a foregone conclusion that economic…

  • Uncertainty in social interaction

    Here we focus on strategic uncertainty: situations in which even formal theoretical modelling (“game theory”) makes it impossible to predict what one’s opponent/ally will do. Psychologists would argue that such situations require “theory of mind.”