Individual Expectations, Limited Rationality and Aggregate Outcomes

Bao, Te; Hommes, Cars; Sonnemans, Joep; Tuinstra, Jan

doi:10.2139/ssrn.1808845

Cited by 32 publications

(56 citation statements)

References 32 publications

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“…The fundamental trader thus acts as a "far from equilibrium" stabilizing force in the market, mimicking the feature that more traders in the market expect the price to return in the direction of the fundamental when the deviations becomes large 8 . Subjects' earnings depend on forecasting performance and are given by a quadratic scoring rule…”

Section: Experimental Designmentioning

confidence: 99%

The heterogeneous expectations hypothesis: Some evidence from the lab

Hommes

2011

Journal of Economic Dynamics and Control

395

214

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This paper surveys learning to forecast experiments (LtFEs) with human subjects to test theories of expectations and learning. Subjects must repeatedly forecast a market price, whose realization is an aggregation of individual forecasts. Emphasis is given to how individual forecasting rules interact at the micro level and which structure they co-create at the aggregate, macro level. In particular, we focus on the question wether the evidence from laboratory experiments is consistent with heterogeneous expectations.JEL codes: E52, D83, D84, C62.

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Section: Experimental Designmentioning

confidence: 99%

The heterogeneous expectations hypothesis: Some evidence from the lab

Hommes

2011

Journal of Economic Dynamics and Control

395

214

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“…Bostian and Holt, 2009). Heemeijer et al (2009) and Bao et al (2012) investigate whether the non-convergence result is driven by the positive expectation feedback nature of the experimental market. Positive/negative expectation feedback means that the realised market price increases/decreases when the average price expectation increases/decreases.…”

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confidence: 99%

Bubble Formation and (In)Efficient Markets in Learning-to-Forecast and -Optimise Experiments

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. (0)10 408 Abstract. This experiment compares the price dynamics and bubble formation in an asset market with a price adjustment rule in three treatments where subjects (1) submit a price forecast only, (2) choose quantity to buy/sell and (3) perform both tasks. We find deviation of the market price from the fundamental price in all treatments, but to a larger degree in treatments (2) and (3). Mispricing is therefore a robust finding in markets with positive expectation feedback. Some very large, recurring bubbles arise, where the price is 3 times larger than the fundamental value, which were not seen in former experiments. Terms of use: Documents inJEL Classification: C91, C92, D53, D83, D84

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“…The experiments in Hommes, Sonnemans, Tuinstra, and van de Velden (2005), henceforth HSTV05, showed that the subjects can coordinate on oscillating and serially correlated time series, and that convergence to the fundamental equilibrium happens only under severe restrictions on the underlying law of motion. Further experiments in Heemeijer, Hommes, Sonnemans, andTuinstra (2009), henceforth HHST09, andBao, Hommes, Sonnemans, andTuinstra (2012), henceforth BHST12, demonstrated that the expectations feedback structure plays crucial role. Negative feedback systems (i.e., where more optimistic forecasts lead to lower market prices, as in supply driven commodity markets) tend to generate convergence to the fundamental equilibrium rather easily, while positive feedback systems (i.e., where more optimistic forecasts lead to higher market prices, as in speculative asset markets) typically generate behavior with the price oscillating around the fundamental equilibrium dynamics.…”

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confidence: 99%

Simple Forecasting Heuristics that Make Us Smart: Evidence from Different Market Experiments

2015

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Simple forecasting heuristics that make us smart: evidence from different market experiments Anufriev, M.; Hommes, C.H.; Makarewicz, T.A. Link to publicationCitation for published version (APA): Anufriev, M., Hommes, C., & Makarewicz, T. (2015). Simple forecasting heuristics that make us smart: evidence from different market experiments. (CeNDEF working paper; No. 15-07). Amsterdam: CeNDEF, University of Amsterdam. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 28 Apr 2019Simple Forecasting Heuristics that Make us Smart:Evidence from Different Market ExperimentsMikhail Anufriev and Cars Hommes and Tomasz Makarewicz * July 2015Abstract 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.JEL codes: C53, C63, C91, D03, D83, D84.

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Individual Expectations, Limited Rationality and Aggregate Outcomes

Cited by 32 publications

References 32 publications

The heterogeneous expectations hypothesis: Some evidence from the lab

The heterogeneous expectations hypothesis: Some evidence from the lab

Bubble Formation and (In)Efficient Markets in Learning-to-Forecast and -Optimise Experiments

Simple Forecasting Heuristics that Make Us Smart: Evidence from Different Market Experiments

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