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

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. AbstractIn 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 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.JEL: D03 G12 C91

The term structure of cross-sectional dispersion of expectations in a Learning-to-Forecast Experiment

“…The empirical analysis considering the dynamics of expectations and prices generalize the results of Colasante et al (2017Colasante et al ( , 2018) that eliciting long-run expectations has no significant effect on subjects' short-run expectations independently on the expectations' feedback systems. Table 1: Wilcoxon test on the convergence of k-steps-ahead expectations to the fundamental value in the positive and negative feedback treatment.…”

“…We implement a LtFEs where we contemporaneously elicit short and long-run expectations about the evolution of the price in experimental markets characterized by different expectations feedback systems. In particular, we generalize the original contribution of Heemeijer et al (2009) eliciting long-run expectations and extend the results of Colasante et al (2017Colasante et al ( , 2018 to markets with a negative expectations feedback system. Our results reveal that eliciting long-run expectations does not influence subjects' short-run expectations.…”

“…In a setting with a positive expectations feedback system, Colasante et al (2017Colasante et al ( , 2018 show that subjects do anchor their short-and long-run expectations to the last realized price. 9 Anchoring expectations to the last realized price helps the subjects to persistently coordinate their short-run expectations.…”

Heuristic Switching Model and Exploration-Exploitation Algorithm to Describe Long-Run Expectations in LtFEs: a Comparison

“…The most commonly used is the so-called Heuristic Switching Model, HSM hereafter (see Brock and Hommes (1998)). Using experimental data on long-run expectations, Colasante et al (2018c) have introduced an alternative adaptive learning model of bounded rationality, the Exploration-Exploitation Algorithm (hereafter EEA) that, contrary to the HSM, accounts contemporaneously for subjects' short-and long-run expectations.…”

“…Individual expectations are elicited in a series of Learning-to-Forecast Experiments (LtFEs) with di↵erent feedback mechanisms between expectations and market price: positive and negative feedback markets. We implement the EEA proposed by Colasante et al (2018c). Moreover, we modify the existing version of the HSM in order to incorporate the long-run predictions.…”