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

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  • Makarewicz, Tomasz

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.

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  • Makarewicz, Tomasz, 2019. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," BERG Working Paper Series 141, Bamberg University, Bamberg Economic Research Group.
    • Handle: RePEc:zbw:bamber:141
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    Cited by:

    1. Christoph March & Marco Sahm, 2019. "The Perks of Being in the Smaller Team: Incentives in Overlapping Contests," CESifo Working Paper Series 7994, CESifo.
    2. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2020. "Survival and the ergodicity of corporate profitability," BERG Working Paper Series 162, Bamberg University, Bamberg Economic Research Group.
    3. Arata, Yoshiyuki & Mundt, Philipp, 2019. "Topology and formation of production input interlinkages: Evidence from Japanese microdata," BERG Working Paper Series 152, Bamberg University, Bamberg Economic Research Group.
    4. Proaño Acosta, Christian & Lojak, Benjamin, 2019. "Animal spirits, risk premia and monetary policy at the zero lower bound," BERG Working Paper Series 148, Bamberg University, Bamberg Economic Research Group.
    5. Mundt, Philipp & Oh, Ilfan, 2019. "Asymmetric competition, risk, and return distribution," BERG Working Paper Series 145, Bamberg University, Bamberg Economic Research Group.

    More about this item

    Keywords

    Financial Instability; Learning-to-Forecast and Learning-to-Optimize Experiments; Genetic Algorithm Model of Individual Learning;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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