Fachbereich 07 Wirtschaftswissenschaft. Lehrstuhl für ABWL, insbes. Finanzwirtschaft. Advanced Finance. Overview

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1 Advanced Finance Prof. Dr. Thorsten Poddig Lehrstuhl für Allgemeine Betriebswirtschaftslehre, insbes. Finanzwirtschaft Universität Bremen Hochschulring 4 / WiWi-Gebäude Bremen poddig@uni-bremen.de 1

2 Topic General Topic: Valuation and Empirical Capital Markets Research More specific: Efficient Capital Markets and Anomalies Questions: What is efficient? What are anomalies? What is valuation? Are anomalies relevant for valuation? 2

3 Efficient Markets Hypothesis (EMH) EMH: Asset prices always fully reflect available information! Information efficiency is a prerequisite for allocation efficiency! Allocation efficiency maximizes utility! If capital markets are not (information) efficient, optimal capital allocation is not possible! How can we judge about efficiency? 3

4 Anomalies Fama (1970, 1991) provides a famous framework in order to think about efficiency. Various anomalies challenge efficiency : Seasonal anomalies, e.g., Turn-of-the-year effect (January), Turn-of-the-week effect (Monday) Valuation anomalies, e.g., Size effect, Momentum effect Anomalies or a missing part in capital markets theory? Anomalies are a challenge to theory building. 4

5 Objectives Introduction to efficient markets theory Seasonal anomalies Valuation anomalies Capital markets theory (CAPM, APT) Factor models Modeling and empirical testing Implementation and test of factor models in Matlab 5

6 Two groups Two pillars of the course: Theory & Literature Review: Group A Empirical modeling & testing: Group B Lessons and papers presented by group A Empirical work and results presented by group B Self organized learning and research 6

7 Theory & Literature Sessions: May, 4 th : Efficient Markets (Fama (1970), Fama (1991)) June, 15 th : Seasonal Anomalies (Time-Series Anomalies) June, 29 th : Valuation Anomalies (Cross-Sectional Anomalies) Topics: Group A (Theory & Literature) Theoretical background Literature review Results & Discussions 7

8 Group B Empirical research Programming in Matlab Implementation of factor models Application to German stock market Modeling and testing Presentation and comparing empirical results Empirical insights? 8

9 Guidelines Group A: See document Home Work A Group B: See document Home Work B See also general information sheet Syllabus Examination: Group A: Class preparation, lectures, slides, paper Group B: Matlab codes, tables, documentation, paper, presentation 9

10 Self organized learning: objectives Master of Science: Preparation for long life learning Less or no lectures, self organized learning instead Lectures: coaching self organized learning, not story telling Class preparation: Every student must be prepared for every topic Group A: Organizing a lecture session 10

11 General principles Group A is responsible for: Organization, Topics, Teaching Group A has to think of how to teach : Powerpoint slide presentation? Discussion? Which topics? Group work, movies, Here are some recommendations which are not mandatory, i.e., Group A remains responsible: 11

12 Which topics should be covered? Many papers, to much content, impossible to address every aspect Most important question: What does really matter? Which papers are important? Why? Which aspects are relevant? Major propositions, key insights? Critical discussion Summary of the lecture: Lessons learned? 12

13 Structure of a lecture Definition of the problem: What is the lecture about? Why? How does the lecture fit into financial economics? Which papers are discussed? Why? Objectives? Which questions? Why relevant and important? How is the lecture organized? Why this way? What is the link between the papers and lectures? Actual and most recent results? Really new? Look back into scientific history! 13

14 Discussion of a paper What is the paper really about? Not often obvious! How does it fit into financial economics? Is the topic treated a more detailed one or of general interest? What kind of paper (Literature review, empirical study, )? Author: Who is it? What is her/his research about? Similar papers? Which? Contrary results? Impact of the paper? Relevant for research effort? 14

15 Paper: Content and presentation Basic assumption/prerequisite: Everybody has already read the paper, avoid story telling! More important: Comment the paper, what is interesting, relevant? Methodology applied? Suitable for the question? Important: Point out the structure of the paper and line of argumentation Major propositions? Methodology applied? 15

16 Critical discussion Discuss most important aspects of the paper, e.g., propositions, approaches, methodology, reasoning. Be aware: The paper is already discussed in the relevant literature! Review such papers before formulating your own position! In general: All critical aspects have been already discussed, it is mostly improbable that you add some new aspects unknown in literature! 16

17 Summary, lessons learned A good summary is not a repetition! The audience has just followed your lecture! Instead: What are the most relevant aspects? Lessons learned: Why is this important (for me)? Which insights are relevant for my M.Sc.? Which problems remain for future research? In general and especially for me? 17

18 Reading scientific papers, some recommendations: Do not try to understand every detail: Papers do not emerge spontaneous; usually they belong to a long history of research; The details only become clear as long as one is familiar with the research history; More important at beginning: ability to extract major propositions without understanding every detail. Try to understand the rest by tracking back research history! Old papers are not outdated, they clarify many puzzles! 18

19 Recommendations (cont.): Use textbooks, research is more or less reflected in (advanced) textbooks and well explained/discussed, but textbooks only provide some basics! Read similar papers: The same topics are often treated in seemingly unrelated papers, but from a different point of view. This might help! Discuss the paper within your group: Everyone might understand the paper in a different way, this is great, not puzzling! Make use of this! 19

20 Some final remarks Many M.Sc. students do not really know what science is. Often, there is a common, but misleading understanding. Theory of Science : What is Science? Which conditions must be fulfilled in order to talk about science or scientific models? Attention: Science is made by humans, as such a sociological process! 20

21 Do not judge based on assumptions! Principle: Do not judge a theory or a model only according to the assumptions made! Ask for the ability to explain real world phenomena! Be aware: Any theory or model is a simplification, as such based on simplifying assumptions, which in turn might appear to be unrealistic! Often: The most simple models, based on most unrealistic assumptions, do the best job! Attention: A model is not reality, never confuse both! 21

22 Rejection, testable implications Finally, a theory must be able to explain empirical phenomena and to provide forecasts. Therefore, a theory must be clear and well formulated. Concrete: A theory must have testable implications and can be rejected in the light of empirical evidence! A story which is sufficient unspecific and vague and therefore is able to explain everything and nothing is not a theory! We do not need nice stories in science! 22

23 Occam s Razor Principle: As long as two competing theories explain the world equally well, choose the most simple one! It is trivial to construct a theory in the most complicated way! A more complicated theory must pay, it must be able to explain more empirical phenomena. Be aware: There is always a trade-off between complexity and accuracy! Do we really need a complex theory in every circumstances? Often a simple one is quite sufficient! 23

24 Generalization A most parsimonious set of assumptions (Occam s Razor) should explain empirical phenomena in the most general way. It is impossible to cover any special case! Theory must forgo some phenomena/effects! Important question: Is an anomaly (unexplained phenomena) so relevant that it is worth extending or changing existing theory? Think off Occam s Razor! 24

25 Literature Group A Fama (1970): Efficient Capital Markets: A Review of Theory and Empirical Work, in: Journal of Finance 25(2), Fama (1991): Efficient Capital Markets: II, in: Journal of Finance 46(5), Introduction to seasonal anomalies: Schwert, G. (2003), Anomalies and market efficiency, in G. Constantinides, M. Harris & R. Stulz, eds, Handbook of the Economics of Finance, North- Holland. Introduction to valuation anomalies: Fama, E. & French, K. (2008), Dissecting anomalies, Journal of Finance 63(4),

26 Literature Group B Testing explanatory power of CAPM beta: Fama, E. & French, K. (1992), The cross-section of expected stock returns, Journal of Finance 47(2), Extending CAPM beta to the three-factor model: Fama, E. & French, K. (1993), Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33(1), Testing explanatory power of the three-factor model and extending the three-factor model to the four factor model: Carhart, M. (1997), On persistence in mutual fund performance, Journal of Finance 52(1),

27 Literature Group B Empirical evidence for German stock returns: Artmann/Finter/Kempf: Determinants of Expected Stock Returns: Large Sample Evidence from the German Market, forthcoming in: Journal of Business Finance and Accounting Artmann/Finter/Kempf/Koch/Theissen: The Cross-Section of German Stock Returns: New Data and New Evidence, forthcoming in: Schmalenbach Business Review 27