The lecture, which this term takes place from the 20th to the 23rd of April, gives an introduction to the field of equity market anomalies. It provides an overview over well-known as well as and recently discovered cross-sectional quantitative anomalies and discusses from both a theoretical and an empirical point of view why these return patterns might arise and persist. It also discusses to which extent these anomalies may be translated into effective investment strategies, and explains potential pitfalls when evaluating trading strategies. In the second half of the semester, students make use of their newly acquired knowledge by writing and presenting a seminar paper in which they critically evaluate specific trading strategies/market anomalies. Students can decide whether their paper is based mainly on a synthesis of the literature or based mainly on programming, backtesting, and critically discussing a self-proposed trading strategy (for instance via the online platform “Quantopian”).
Students will better understand to what extent stock market are efficient and to what extent potential inefficiencies can be translated into profitable quantitative trading strategies. The acquired skills and knowledge are highly relevant for work in the financial industry (e.g., asset or wealth management, equity research, fintech), but may also be of interest to economic research and teaching institutions, or regulatory authorities.
Content of the lecture
As the course discusses recent research, there is no specific textbook that covers all aspects of the course.
Useful survey papers are:
The module-related examination consists of a seminar paper (usually 15 pages, 65% of the grade), of an accompanying presentation (usually 15 minutes, 25% of the grade), as well as of the active participation in the discussions of other presentations (10%)
Students are assumed to have an undergraduate level knowledge of finance (for instance by having taken an introductory course in investments or asset pricing). Basic econometric skills are helpful to understand empirical research conducted in the research papers, which the course’s content is based on. Programming experience (in particular in Python) can be useful (see the Abstract below for details). A sufficient level of spoken and written English language skills is necessary.