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

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.