Investments

Classes on the theory of asset pricing (UG, Honours, PhD)

This is an advanced class on asset pricing theory, offered at the advanced undergraduate (“Honours”) and, separately, at the Ph.D. level. The goal of asset pricing theory is to study how to invest, and how actual investment behaviour affects pricing of risky prospects in financial markets.

At the Honours level, the class teaches the following:
At a theoretical level, an investments class teaches principles of “strategic” (“diversification”) and “tactical” (“timing”) asset allocation, and their potential impact on market-wide phenomena such as asset prices and trading volume (“asset pricing theory”). At the practical level, we offer students opportunities to attempt implementing investment choices in purposely controlled online markets. Students will then experience the effect of their actions on commonly used performance evaluation statistics. Mistakes can be put into perspective against recent advances in behavioural finance. Special attention is paid to market-wide effects of such mistakes, if they exist, and whether these are easily recognisable in real-world financial markets.  We also urge students to investigate to what extent and how trading can be automated (algorithmic trading). Students with programming background (Python) are given the option to test their algorithms live in controlled online markets.

The class is currently part of the core curriculum of the Honours in Finance program at The University of Melbourne (FNCE40002 Advanced Investments).

At the Ph.D. level, the class teaches the following:
Traditionally, asset pricing theory is taught as a logical set of principles applied to assets with uncertain payoffs. The principles are based on general equilibrium theory, a branch of economic theory that provides a holistic analysis of an economy, rather than focusing on a single market at a time – traditional partial equilibrium analysis – or on relationships between two people in an economy – contract theory – or between a limited number of people – game theory and mechanism design; market microstructure theory.

Central to general equilibrium theory is the concept of Pareto (welfare) optimality, and when applied to asset pricing, informational efficiency (do prices reveal all available information?). To study Pareto optimality in a finance (uncertainty) context, the notion of complete markets is key (can all risks be insured through markets?). Indeed, some of the celebrated results in general equilibrium theory (e.g., that competitive equilibrium generates optimal allocations) do not readily carry over to a world of uncertainty, and market completeness is key to understand when/why it does.

There are two branches of asset pricing theory, the static (one-period) and the dynamic (multi-period) branch. While most real-world securities are dynamic (they live for multiple “periods:” days, months, years…), much of empirical analysis in mainstream finance is (unfortunately) based on the static branch. To facilitate analysis, the dynamic branch has increasingly been appealing to a mathematical setting that is rarely taught even in advanced mathematics classes, namely, stochastic calculus. This isn’t always fruitful, because it often means that some of the shortcomings of the static branch (focus on mean-variance analysis) find their way in the dynamic branch (risk is locally Gaussian, and hence mean-variance analysis applies over small – infinitesimal – intervals). The class includes a primer on stochastic calculus. The aim is not only for students to be able to price simple dynamic securities, but foremost for students to appreciate the power and limitations of the approach.

The class does not only cover theory, but also empirical analysis, which for finance has meant: testing of the main pricing predictions on historical data from field markets. The focus is not so much be on the empirical results themselves, but on the robustness of the empirical (econometric) approach: to what extent would inference be affected by small violations of the inevitable auxiliary assumptions that the theory made in order to get traction and yet are obviously violated in the real world?

The class will also touch upon the emerging field of experimental finance, where asset pricing is tested in a controlled setting. In the context of this class, experiments allow the student to see concrete economies of the type studied in asset pricing theory. The class makes use of our online markets software, Flex-E-Markets.

Levels: Honours, PhD

Both classes are offered at The University of Melbourne in 2017.