Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/30608
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dc.contributor.authorVayas-Ortega, Germania-
dc.contributor.authorSoguero-Ruiz, Cristina-
dc.contributor.authorRojo-Álvarez, José Luis-
dc.contributor.authorGimeno Blanes, Francisco Javier-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Comunicacioneses_ES
dc.date.accessioned2024-01-24T11:26:43Z-
dc.date.available2024-01-24T11:26:43Z-
dc.date.created2020-08-
dc.identifier.citationApplied Sciences Volume 10 Issue 17 (2020)es_ES
dc.identifier.issn2076-3417-
dc.identifier.urihttps://hdl.handle.net/11000/30608-
dc.description.abstractThe Discounted Cash Flow (DCF) method is probably the most extended approach used in company valuation, its main drawbacks being probably the known extreme sensitivity to key variables such asWeighted Average Cost of Capital (WACC) and Free Cash Flow (FCF) estimations not unquestionably obtained. In this paper we propose an unbiased and systematic DCF method which allows us to value private equity by leveraging on stock markets evidences, based on a twofold approach: First, the use of the inverse method assesses the existence of a coherentWACC that positively compares with market observations; second, different FCF forecasting methods are benchmarked and shown to correspond with actual valuations. We use financial historical data including 42 companies in five sectors, extracted from Eikon-Reuters. Our results show that WACC and FCF forecasting are not coherent with market expectations along time, with sectors, or with market regions, when only historical and endogenous variables are taken into account. The best estimates are found when exogenous variables, operational normalization of input space, and data-driven linear techniques are considered (Root Mean Square Error of 6.51). Our method suggests that FCFs and their positive alignment with Market Capitalization and the subordinate enterprise value are the most influencing variables. The fine-tuning of the methods presented here, along with an exhaustive analysis using nonlinear machine-learning techniques, are developed and discussed in the companion paper.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent21es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectstock marketes_ES
dc.subjectprivate equityes_ES
dc.subjectvaluationes_ES
dc.subjectcash flowes_ES
dc.subjectdiscounted cash flowes_ES
dc.subjectenterprise valuees_ES
dc.subjectdiscount ratees_ES
dc.subjectmachine learninges_ES
dc.subjectlinear regressiones_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleOn the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (I): Empowering Discounted Cash Flow Valuationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.3390/app10175875es_ES
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