| Naslov: | Private firm valuation using multiples : can artificial intelligence algorithms learn better peer groups? |
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| Avtorji: | ID Jagrič, Timotej (Avtor)
ID Fister, Dušan (Avtor)
ID Grbenic, Stefan Otto (Avtor)
ID Herman, Aljaž (Avtor) |
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| Datoteke: |  https://www.mdpi.com/2078-2489/15/6/305
 Private_Firm_Valuation_Using_Multiples.pdf (673,36 KB)
MD5: 3BA5A201074EBC621E3B9B676AE4CCEF
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Znanstveno delo |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | EPF - Ekonomsko-poslovna fakulteta
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| Opis: | Forming optimal peer groups is a crucial step in multiplier valuation. Among others, the traditional regression methodology requires the definition of the optimal set of peer selection criteria and the optimal size of the peer group a priori. Since there exists no universally applicable set of closed and complementary rules on selection criteria due to the complexity and the diverse nature of firms, this research exclusively examines unlisted companies, rendering direct comparisons with existing studies impractical. To address this, we developed a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understanding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20,000 private firm transactions, model performance was evaluated employing multiplier prediction error measures (emphasizing bias and accuracy) as well as prediction superiority directly. Emphasizing five enterprise and equity value multiples, the results allow for the overall conclusion that the self-organizing map algorithm outperforms the traditional linear regression model in both minimizing the valuation error as measured by the multiplier prediction error measures as well as in direct prediction superiority. Consequently, the machine learning methodology offers a promising way to improve peer selection in private firm multiplier valuation. |
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| Ključne besede: | private firm valuation, multiples, peer group, peer selection, artificial intelligence, self-organizing map |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 08.04.2024 |
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| Datum sprejetja članka: | 23.05.2024 |
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| Datum objave: | 24.05.2024 |
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| Založnik: | MDPI |
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| Leto izida: | 2024 |
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| Št. strani: | str. 1-16 |
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| Številčenje: | Vol. 15, issue 6, [art. no.] 305 |
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| PID: | 20.500.12556/DKUM-92124  |
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| UDK: | 004.8 |
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| COBISS.SI-ID: | 196747779 |
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| DOI: | 10.3390/info15060305 |
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| ISSN pri članku: | 2078-2489 |
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| Datum objave v DKUM: | 01.07.2025 |
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| Število ogledov: | 237 |
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| Število prenosov: | 8 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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