Course 2005-2006 a.y.

Go to class group/s: 31

GM-LS (6 credits - I sem. - AI) - MM-LS (6 credits - I sem. - AI) - OSI-LS (6 credits - I sem. - AI) - AFC-LS (6 credits - I sem. - AI) - CLAPI-LS (6 credits - I sem. - AI) - CLEFIN-LS (6 credits - I sem. - AI) - CLELI-LS (6 credits - I sem. - AI) - CLEACC-LS (6 credits - I sem. - AI) - DES-LS (6 credits - I sem. - AI) - CLEMIT-LS (6 credits - I sem. - AI) - CLG-LS (6 credits - I sem. - AI)

Course Director:
ERIO CASTAGNOLI

Course Objectives

The traditional finance paradigm seeks to understand financial markets using models in which agents are rational. Rationality means two things. First, when they receive new information, agents update their beliefs correctly, in the manner described by Bayes law. Second, given their beliefs, agents make choices that are normatively acceptable, in the sense that they are consistent with Savages notion of Subjective Expected Utility (SEU).
This traditional framework is appealingly simple, and it would be very satisfying if its predictions were confirmed in the data. Unfortunately, after years of effort, it has become clear that basic facts about the aggregate stock market, the cross-section of average returns and individual trading behavior are not easily understood in this framework.
Behavioral finance is a new approach to financial markets that has emerged, at least in part, in response to the difficulties faced by the traditional paradigm. In broad terms, it argues that some financial phenomena can be better understood using models in which some agents are not fully rational.  More specifically, it analyzes what happens when we relax one, or both, of the two tenets that underlie individual rationality. In some behavioral finance models, agents fail to update their beliefs correctly. In other models, agents apply Bayes law properly but make choices that are normatively questionable, in that they are incompatible with SEU..." (see Barberis and Tahler, 2003).
The object of this course is introducing its participants to the problems and methods of Behavioral Economics and Finance.

Course Content Summary

It ideally consists of four parts:
A review of the classical models to deal with uncertainty in economic and financial theory:

• Von Neumann - Morgenstern expected utility.
• Subjective probabilities and subjective expected utility.
• Bayesian updating.

Empirical evidence (both from real markets and lab experiments) challenging the classical paradigms:

• Framing affects, Allais paradox, Ellsberg paradox.
• Overconfidence, optimism and wishful thinking, representativeness, conservatism, belief perseverance, anchoring, ambiguity aversion, availability biases.
• Twin shares, index inclusions, internet carve-outs, call-put parity violations, equity premium puzzle.

The introduction of the basic tools that cognitive psychology and decision theory provide to capture such evidence:

• Multiple prior models.
• Probability distortions.
• Prospect theory.
• Ambiguity attitudes.
• Non-Bayesian Updating.
• Temptation and self control.

Some application of these instruments to explain the introduced puzzles and anomalies observed in the first part of the course. An alternative perspective on pricing and pricing puzzles in terms of frictionality of financial markets, by means of the multiple priors model.

Detailed Description of Assessment Methods

The exam consists in a final test and an (optional) oral exam or paper review.

Textbooks

• N. BARBERIS, R. TAHLERA, Survey of Behavioral Finance, in G.M. CONSTANTINIDES, M. HARRIS, R. STULZ (EDS.), Handbook of the Economics of Finance, Elsevier, 2003.
• D. KAHNEMAN, A. TVERSKY, Choices, Values and Frames, Cambridge University Press, 2000.
• A. SHLEIFER, Inefficient Markets: An Introduction to Behavioral Finance, Oxford University Press, 2000.
• The course is taught in English. Lecture notes and reading lists will be distribuited for specific topics.

Exam textbooks & Online Articles (check availability at the Library)

Last change 28/04/2005 00:00