Vorlesung und Seminar
Stock Market Anomalies and Quantitative Trading Strategies
The lecture, which takes place twice a week in the first half of the semester, 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.
Das Konzept besteht aus einer Vorlesung (1. Semesterhälfte) mit integriertem Seminar (2. Semesterhälfte), in welchem die Teilnehmer selber Handelsstrategien vorschlagen sollen.
- have a profound understanding of the most important stock market anomalies,
- are able to critically reflect to what extent these anomalies can be translated into real-life trading strategies,
- know the key insights of theoretical, experimental, and empirical research aiming at explaining these anomalies,
- have a profound understanding of the link between individual behavior in financial markets, market frictions, and resulting return patterns,
- can evaluate scientific studies accurately, understand the methodology used in leading papers of the field, can interpret estimation results correctly, and analyze them critically,
- are in a position to identify starting points for their own research and to present and defend their research proposals in a professional way.
Content of the lecture
- Introduction and “big picture”
- Conceptual foundations, behavioral finance, and limits to arbitrage
- The classical anomalies: Size, value, momentum
- The “high risk, low return” anomalies
- The post-earnings announcement drift and other event-based anomalies
- Violations of the law of one price and information spillover effects (e.g. pairs trading)
- The impact of sentiment
- The role of media for stock market anomalies
- Meta anomalies and other current trends in the literatur
As the course discusses recent research, there is no specific textbook that covers all aspects of the course.
Useful survey papers are:
- Zacks (2011), “The handbook of equity market anomalies”, Wiley Finance.
- Barberis/Thaler (2003), “A Survey of Behavioral Finance”, in: Handbook of the Economics of Finance, Chap. 18, 1054-1123.
- Subrahmanyam (2010), “The cross-section of expected stock returns: What have we learnt from the past twenty-five years of research?”, European Financial Management, 16, 27–42.
Methods of Assessment:
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.