EconPapers    
Economics at your fingertips  
 

Long-run expectations in a learning-to-forecast experiment: a simulation approach

Annarita Colasante (), Simone Alfarano (), Eva Camacho-Cuena () and Mauro Gallegati ()
Additional contact information
Eva Camacho-Cuena: University Jaume I

Authors registered in the RePEc Author Service: Eva Camacho Cuena ()

Journal of Evolutionary Economics, 2020, vol. 30, issue 1, No 5, 75-116

Abstract: Abstract In this paper, we elicit short-run as well as long-run expectations on the evolution of the price of a financial asset in a Learning-to-Forecast Experiment (LtFE). Subjects, in each period, have to forecast the the asset price for each one of the remaining periods. The aim of this paper is twofold: first, we fill the gap in the experimental literature of LtFEs where great effort has been devoted to investigate short-run expectations, i.e. one step-ahead predictions, while there are no contributions that elicit long-run expectations. Second, we propose a new computational algorithm to replicate the main properties of short and long-run expectations observed in the experiment. This learning algorithm, called Exploration-Exploitation Algorithm, is based on the idea that agents anchor their expectations around the last realized price rather than on the fundamental value, with a range proportional to the past observed price volatility. When compared to the Heuristic Switching Model, our algorithm performs equally well in describing the dynamics of short-run expectations and the realized price dynamics. The EEA, additionally, is able to reproduce the dynamics long-run expectations.

Keywords: Long-run expectations; Experiment; Evolutionary learning (search for similar items in EconPapers)
JEL-codes: D03 G12 C91 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s00191-018-0585-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Long-run expectations in a Learning-to-Forecast Experiment: A Simulation Approach (2017) Downloads
Working Paper: Long-run expectations in a Learning-to-Forecast-Experiment: a simulation approach (2017) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joevec:v:30:y:2020:i:1:d:10.1007_s00191-018-0585-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/191/PS2

DOI: 10.1007/s00191-018-0585-1

Access Statistics for this article

Journal of Evolutionary Economics is currently edited by Uwe Cantner, Elias Dinopoulos, Horst Hanusch and Luigi Orsenigo

More articles in Journal of Evolutionary Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2020-09-21
Handle: RePEc:spr:joevec:v:30:y:2020:i:1:d:10.1007_s00191-018-0585-1