Machine Valuation

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dc.contributor.author Geertsema, Paul en
dc.contributor.author Lu, Helen en
dc.date.accessioned 2019-10-29T20:23:43Z en
dc.date.issued 2019-09-07 en
dc.identifier.citation SSRN (3447683). 07 Sep 2019. 56 pages en
dc.identifier.uri http://hdl.handle.net/2292/48761 en
dc.description.abstract We present a machine learning approach to firm valuation that requires only historical accounting data as input. The machine learning model generates a median absolute percentage error of 17.2% in out-of-sample firm value predictions. The model out-performs a sample of final-year finance students (41.3%) and individual analyst forecasts of one-year-ahead firm value (22.4%). We show that subsequent market valuations move towards the model valuation, generating return predictability over horizons of up to five years. en
dc.relation.ispartof SSRN en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://www.ssrn.com/index.cfm/en/ssrn-faq/#ssrn_copyright en
dc.subject valuation; asset pricing; return predictability; machine learning; gradient boos- ted trees en
dc.title Machine Valuation en
dc.type Report en
dc.identifier.doi 10.2139/ssrn.3447683 en
dc.rights.holder Copyright: The authors en
pubs.author-url https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3447683 en
pubs.publication-status Unpublished en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Working Paper en
pubs.elements-id 780864 en
pubs.org-id Business and Economics en
pubs.org-id Accounting and Finance en
pubs.number 3447683 en
pubs.record-created-at-source-date 2019-09-17 en


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