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dc.contributor.authorWoike, Jan Kristian
dc.contributor.authorHoffrage, U
dc.contributor.authorPetty, JS
dc.date.accessioned2020-10-21T14:48:18Z
dc.date.available2020-10-21T14:48:18Z
dc.date.issued2015-08
dc.identifier.issn0148-2963
dc.identifier.issn1873-7978
dc.identifier.urihttp://hdl.handle.net/10026.1/16573
dc.description.abstract

Using computer simulation, we investigate the impact of different strategies on the financial performance of VCs. We compare simple heuristics such as equal weighting and fast and frugal trees with more complex machine learning and regression models and analyze the impact of three factors: VC learning, the statistical properties of the investment environment, and the amount of information available in a business plan. We demonstrate that the performance of decision strategies and the relative quality of decision outcomes change critically between environments in which different statistical relationships hold between information contained in business plans and the likelihood of financial success. The Equal Weighting strategy is competitive with more complex investment decision strategies and its performance is robust across environments. Learning only from those plans that the simulated VC invested in, drastically reduces the VC's potential to learn from experience. Lastly, the results confirm that decision strategies differ in respect to the impact of added information on the outcomes of decisions. Finally, we discuss real-world implications for the practice of VCs and research on VC decision making.

dc.format.extent1705-1716
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectDecision making
dc.subjectVenture capital
dc.subjectFinancial investment
dc.subjectSimple heuristics
dc.subjectSimulation
dc.titlePicking profitable investments: The success of equal weighting in simulated venture capitalist decision making
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000356743100007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue8
plymouth.volume68
plymouth.publication-statusPublished
plymouth.journalJournal of Business Research
dc.identifier.doi10.1016/j.jbusres.2015.03.030
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Psychology
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA04 Psychology, Psychiatry and Neuroscience
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA04 Psychology, Psychiatry and Neuroscience/UoA04 Psychology, Psychiatry and Neuroscience MANUAL
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.identifier.eissn1873-7978
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.jbusres.2015.03.030
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
plymouth.funderSimple Heuristics for Human Inferences::Swiss National Science Foundation
plymouth.funderSimple Heuristics for Human Inferences::Swiss National Science Foundation


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