Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics

Tomasz Makarewicz

No 141, BERG Working Paper Series from Bamberg University, Bamberg Economic Research Group

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.

Keywords: Financial Instability; Learning-to-Forecast and Learning-to-Optimize Experiments; Genetic Algorithm Model of Individual Learning (search for similar items in EconPapers)
JEL-codes: C53 C63 C91 D03 D83 D84 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-cbe, nep-cmp and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bamber:141

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