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dc.contributor.author
Fievet, Lucas
dc.contributor.author
Sornette, Didier
dc.date.accessioned
2018-03-28T15:06:15Z
dc.date.available
2018-03-15T03:59:33Z
dc.date.available
2018-03-28T15:06:15Z
dc.date.issued
2018-03
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0193290
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/250045
dc.identifier.doi
10.3929/ethz-b-000250045
dc.description.abstract
Since the 2008 financial crisis, agent-based models (ABMs), which account for out-of-equilibrium dynamics, heterogeneous preferences, time horizons and strategies, have often been envisioned as the new frontier that could revolutionise and displace the more standard models and tools in economics. However, their adoption and generalisation is drastically hindered by the absence of general reliable operational calibration methods. Here, we start with a different calibration angle that qualifies an ABM for its ability to achieve abnormal trading performance with respect to the buy-and-hold strategy when fed with real financial data. Starting from the common definition of standard minority and majority agents with binary strategies, we prove their equivalence to optimal decision trees. This efficient representation allows us to exhaustively test all meaningful single agent models for their potential anomalous investment performance, which we apply to the NASDAQ Composite index over the last 20 years. We uncover large significant predictive power, with anomalous Sharpe ratio and directional accuracy, in particular during the dotcom bubble and crash and the 2008 financial crisis. A principal component analysis reveals transient convergence between the anomalous minority and majority models. A novel combination of the optimal single-agent models of both classes into a two-agents model leads to remarkable superior investment performance, especially during the periods of bubbles and crashes. Our design opens the field of ABMs to construct novel types of advanced warning systems of market crises, based on the emergent collective intelligence of ABMs built on carefully designed optimal decision trees that can be reversed engineered from real financial data.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science (PLoS)
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Calibrating emergent phenomena in stock markets with agent based models
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS ONE
ethz.journal.volume
13
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e0193290
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
San Francisco, CA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03738 - Sornette, Didier / Sornette, Didier
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03738 - Sornette, Didier / Sornette, Didier
ethz.date.deposited
2018-03-15T03:59:34Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-03-28T15:06:21Z
ethz.rosetta.lastUpdated
2019-01-02T12:40:04Z
ethz.rosetta.versionExported
true
ethz.COinS
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