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

• Published in: Journal of evolutionary economics Vol. 30; no. 1; pp. 75 - 116
• Main Authors: Colasante, Annarita, Alfarano, Simone, Camacho-Cuena, Eva, Gallegati, Mauro
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2020; Springer Nature B.V
• Subjects: Algorithms; Assets; Economic Growth; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Entrepreneurship; Experiments; Exploitation; Institutional/Evolutionary Economics; Learning; Microeconomics; R & D/Technology Policy; Regular Article; Simulation; G12; Long-run expectations; C91; Experiment; D03; Evolutionary learning
• ISSN: 0936-9937; 1432-1386
• DOI: 10.1007/s00191-018-0585-1

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.