A neural approach to the value investing tool F-score

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.