Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/191601
Authors: 
Makarewicz, Tomasz
Year of Publication: 
2019
Series/Report no.: 
BERG Working Paper Series 141
Abstract: 
Behavioral and experimental literature on financial instability focuses on either subjective price expectations (Learning-to-Forecast experiments) or individual trading (Learning-to-Optimize experiments). Bao et al. (2017) have shown that subjects have problems with both tasks. In this paper, I explore these experimental results by investigating a model in which financial traders individually learn how to use forecasting and/or trading anchor-and-adjustment heuristics by updating them with Genetic Algorithms. The model replicates the main outcomes of these two threads of the experimental finance literature. It shows that both forecasters and traders coordinate on chasing asset price trends, which in turn causes substantial and self-fulfilling price oscillations, albeit larger and faster in the case of trading markets. When agents have to learn both tasks, financial instability becomes more persistent.
Subjects: 
Financial Instability
Learning-to-Forecast and Learning-to-Optimize Experiments
Genetic Algorithm Model of Individual Learning
JEL: 
C53
C63
C91
D03
D83
D84
ISBN: 
978-3-943153-62-0
Document Type: 
Working Paper
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