040187 KU Financial Markets and Information (MA) (2021S)

In the course ‘Financial Markets and Information’ we analyse the inclusion of information in prices, from an intuitive and theoretical perspective. In addition, we are going to test how our intuition matches with our observation of real financial markets.
The course splits up in four different parts. In the first part we introduce the optimal learning and information updating when receiving signals. First, we cover Bayes’ rule and continue with filtering information about a fundamental out of signals we observe. We study the Kalman filter as an optimal linear filter.
In the second part, we consider, how information gets included and is reflected in prices. In this part, we study competitive models in which individual traders do not move prices. In this context, the rational expectations equilibrium (fully revealing, partially revealing, noisy information) plays a key role. We will also cover costly information acquisition and the Grossman-Stiglitz paradox. Herding and social learning conclude this part.
The third part covers strategic market models, in which one informed agent’s information can change prices. We investigate the relationship between price impact and the market mechanism design. The timing of information, the number of informed traders and market clearing mechanisms are important factors to consider.
In the fourth part, we cover behavioral information processing. As individuals tend to follow heuristics or make mistakes in their information processing, we examine well documented shortcomings in individual information processing and their impact on financial markets. Overconfidence and learning from experience will play a key role in our study of behavioral learning.