Machine Valuation

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Show simple item record Geertsema, Paul en Lu, Helen en 2019-10-29T20:23:43Z en 2019-09-07 en
dc.identifier.citation SSRN (3447683). 07 Sep 2019. 56 pages en
dc.identifier.uri 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
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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
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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 en
pubs.publication-status Unpublished en
dc.rights.accessrights en
pubs.subtype Working Paper en
pubs.elements-id 780864 en Business and Economics en Accounting and Finance en
pubs.number 3447683 en
pubs.record-created-at-source-date 2019-09-17 en

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