Contrarian Behavior, Information Networks and Heterogeneous Expectations in an Asset Pricing Model

• Published in: Computational economics Vol. 50; no. 2; pp. 231 - 279
• Main Author: Makarewicz, Tomasz
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2017; Springer Nature B.V
• Subjects: Agents; Architecture; Asset pricing; Asymmetry; Behavior; Behavioral/Experimental Economics; Computer Appl. in Social and Behavioral Sciences; Economic models; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Extrapolation; Friendship; Genetic algorithms; Learning; Markets; Math Applications in Computer Science; Mathematical models; Networks; Operations Research/Decision Theory; Pricing; Pricing policies; Prone; Reagents; D85; C63; D84; Learning to forecast; D53; Learning in networks; Financial bubbles; Herding behavior; D83
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-016-9607-y

This paper studies the emergence of contrarian behavior in information networks in an asset pricing model. Financial traders coordinate on similar behavior, but have heterogeneous price expectations and are influenced by friends. According to a popular belief, they are prone to herding. However, in laboratory experiments subjects use contrarian strategies. Theoretical literature on learning in networks is scarce and cannot explain this conundrum (Panchenko et al. in J Econ Dyn Control 37(12):2623–2642, 2013 ). The paper follows Anufriev et al. (CeNDEF Working paper 15–07, 2015 ) and investigates an agent-based model, in which agents forecast price with a simple general heuristic: adaptive and trend extrapolation expectations, with an additional term of (dis-)trust towards their friends’ mood. Agents independently use Genetic Algorithms to optimize the parameters of the heuristic. The paper considers friendship networks of symmetric (regular lattice, fully connected) and asymmetric architecture (random, rewired, star). The main finding is that the agents learn contrarian strategies, which amplifies market turn-overs and hence price oscillations. Nevertheless, agents learn similar behavior and their forecasts remain well coordinated. The model therefore offers a natural interpretation for the difference between the experimental stylized facts and market surveys.