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Long-run expectations in a Learning-to-Forecast Experiment: A Simulation Approach

Author

Listed:
  • Annarita Colasante

    () (LEE and Department of Economics, Universitat Jaume I, Castellón, Spain)

  • Simone Alfarano

    () (LEE and Department of Economics, Universitat Jaume I, Castellón, Spain)

  • Eva Camacho-Cuena

    () (LEE and Department of Economics, Universitat Jaume I, Castellón, Spain)

  • Mauro Gallegati

    () (Department of Economics, Università Politecnica delle Marche, Ancona, Italy)

Abstract

In this paper, we elicit both short and long-run expectations about the evolution of the price of a financial asset by conducting a Learning-to-Forecast Experiment (LtFE) in which subjects, in each period, forecast the asset price for each one of the remaining periods. The aim of this paper is twofold: on the one hand, we try to fill the gap in the experimental literature of LtFEs where great effort has been made in investigating short-run expectations, i.e. one step-ahead predictions,while there are no contributions that elicit long-run expectations. On the other hand, we propose an alternative computational approach with respect to the Heuristic Switching Model (HSM), to replicate the main experimental results. The alternative learning algorithm, called Exploration-Exploitation Algorithm (EEA), is based on the idea that agents anchor their expectations around the last market price, rather than on the fundamental value, with a range proportional to the recent past observed price volatility. Both algorithms perform well in describing the dynamics of short-run expectations and the market price. EEA, additionally, provides a fairly good description of long-run expectations.

Suggested Citation

  • Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena & Mauro Gallegati, 2017. "Long-run expectations in a Learning-to-Forecast Experiment: A Simulation Approach," Working Papers 2017/03, Economics Department, Universitat Jaume I, Castellón (Spain).
  • Handle: RePEc:jau:wpaper:2017/03
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    References listed on IDEAS

    as
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    Cited by:

    1. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW).
    2. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena, 2018. "The term structure of cross-sectional dispersion of expectations in a Learning-to-Forecast Experiment," Working Papers 2018/02, Economics Department, Universitat Jaume I, Castellón (Spain).

    More about this item

    Keywords

    Expectations; Experiment; Evolutionary Learning;

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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