A neural approach to the value investing tool F-Score
Ruth Gimeno,
Lidia Lobán and
Luis Vicente
Finance Research Letters, 2020, vol. 37, issue C
Abstract:
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.
Keywords: Value investing; F-Score; Network data envelopment analysis; Financial statement Information (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612319304660
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:37:y:2020:i:c:s1544612319304660
DOI: 10.1016/j.frl.2019.101367
Access Statistics for this article
Finance Research Letters is currently edited by R. Gençay
More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().