Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments
Mikhail Anufriev (),
Cars Hommes () and
No 29, Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney
We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental settings, (3) improved one-period and 50-period ahead forecasting of lab experiments, and (4) a characterization of the mean, median and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting heuristics can be used in designing or validating simple Heuristic Switching Model.
Keywords: Expectation Formation; Learning to Forecast Experiment; Genetic Algorithm Model of Individual Learning (search for similar items in EconPapers)
JEL-codes: C53 C63 C91 D03 D83 D84 (search for similar items in EconPapers)
Pages: 50 pages
New Economics Papers: this item is included in nep-cbe, nep-cmp, nep-exp, nep-for and nep-ore
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Journal Article: Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments (2019)
Working Paper: Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:uts:ecowps:29
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