A neural approach to the value investing tool F-score

Gimeno Losilla, Ruth (Universidad de Zaragoza) ; Lobán Acero, Lidia (Universidad de Zaragoza) ; Vicente Gimeno, Luis Alfonso (Universidad de Zaragoza)

Resumen: This work is the first neural approach to Piotroski’s (2000) F-Score. From the same informative signals, our approach based on network data envelopment analysis allows for 1) overcoming the binary perspective of classification between companies with good/bad fundamentals, and 2) appropriately assessing the existing interaction among a company’s main financial areas. The analysis of a complete sample of the largest listed companies in the Eurozone and in the U.S. market in the period 2006-2017 shows that our neural F-Score significantly improves the portfolio returns obtained by the original F-Score.

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Idioma: Inglés

DOI: 10.1016/j.frl.2019.101367

Año: 2020

Publicado en: Finance Research Letters 37, 101367 (2020), [6 pp.]
ISSN: 1544-6123

Factor impacto JCR: 5.596 (2020)

Categ. JCR: BUSINESS, FINANCE rank: 6 / 108 = 0.056 (2020) - Q1 - T1

Factor impacto SCIMAGO: 1.339 - Finance (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/S38-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/RTI2018-093483-B-I00

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Economía Finan. y Contab. (Dpto. Contabilidad y Finanzas)