Reference Model Based Learning in Expectation Formation: Experimental Evidence
arxiv(2024)
摘要
How do people form expectations about future prices in financial markets? One
of the dominant learning rules that explains the forecasting behavior is the
Adaptive Expectation Rule (ADA), which suggests that people adjust their
predictions by adapting to the most recent prediction error at a constant
weight. However, this rule also implies that they will continually learn and
adapt until the prediction error is zero, which contradicts recent experimental
evidence showing that people usually stop learning long before reaching zero
prediction error. A more recent learning rule, Reference Model Based Learning
(RMBL), extends and generalizes ADA, hypothesizing that: i) People apply ADA
but dynamically adjust the adaptive coefficient with regards to the
auto-correlation of the prediction error in the most recent two periods; ii)
Meanwhile, they also utilize a satisficing rule so that people would only
adjust their adaptive coefficient when the prediction error is higher than
their anticipation. This paper utilizes a rich set of experimental data with
observations of 41,490 predictions from 801 subjects from the
Learning-to-Forecast Experiments (LtFEs), i.e., the experiment that has been
used to study expectation formation. Our results concludes that RMBL fits
better than ADA in all the experiments.
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