For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
Pleaserefer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
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At a theoretical level, the subject teaches principles of strategic and tactical asset allocation, and their potential impact on market-wide phenomena such as asset prices and trading volume (“asset pricing theory”). At the practical level, the subject provides students with opportunities to attempt implementing investment choices in purposely controlled online markets. Students will experience the effect of their actions on commonly used performance evaluation statistics. Mistakes will be put into perspective against recent advances in behavioural finance. Special attention will be paid to market-wide effects of such mistakes, if they exist, and whether these are easily recognisable in real-world financial markets. Lastly, students will investigate to what extent and how trading can be automated (algorithmic trading). Students with programming background (Python) have the option to test their algorithms live in controlled online markets.
Intended learning outcomes
On successful completion of this subject students should be able to:
- Put into practice commonly used techniques in tactical and strategic asset allocation;
- Understand to what extent common actions and mistakes impact market-wide phenomena such as prices and volume;
- Recognise and avoid (for oneself and others) common behavioural biases in the context of investments;
- Learn what forces behind prices and volume that one cannot see in historical data, by confronting theory with data from controlled experiments;
- Evaluate to what extent (and for those with programming skills, how) investment can be automated;
- Form an informed opinion about major issues in investments, such as portfolio performance evaluation, the efficient markets hypothesis, dark markets, and algorithmic trading.
High level of development: written communication; interpretation and analysis; critical thinking.
Moderate level of development: collaborative learning; problem solving; team work; application of theory to practice; accessing data and other information from a range of sources.
Some level of development: oral communication; statistical reasoning; synthesis of data and other information; evaluation of data and other information; use of computer software.
Last updated: 29 April 2020