An Assessing The Performance Of Companies Using F-Score And Z-Score With Artificial Neural Networks

This research examines how to assess financial performance of a corporation holistically by integrating the use
of both the Piotroski F-Score and Altman Z-Score with an ANN (Artificial Neural Network) analytical method. It
looks at 15 firms in the Nifty Auto Index over the timeframe of 2021 to 2025 through the application of several
statistical techniques using secondary data: descriptive statistics, correlation analysis, one-way ANOVA, trend
analysis and ANN modeling. Key findings show that (1) financial performance, as measured by the F-Score, is
very variable year to year, while measured using the Z-Score, financial stability is stable over time; therefore, (2)
a weak relationship exists between the two F-Scores due to disparate influencing factors for financial performance
and financial stability. The ANN model does not strongly associate financial performance with financial
risk. Overall, the research will allow investors/credit rating agencies/financial analysts with investment interests
in this sector located within India, to more readily assess the health of their investments within a dynamic
environment.
Keywords: Piotroski F-Score, Altman Z-Score, Artificial Neural Network, Financial Performance, Financial
Distress, Automobile Sector, Nifty Auto Index.