Abstract
Reliable prediction of automotive companies’ stock prices is crucial for investment decisions and financial analysis. In our study, we investigated the applicability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting stock prices based on fundamental financial indicators. The primary objective was to examine how the prediction accuracy of the ANFIS model changes when individually removing one of the four validated fundamental indicators Return on Assets (ROA), Return on Equity (ROE), Earnings per Share (EPS), and Profit Margin (PM) thus identifying the relative importance of each indicator. For our analysis, we utilized financial data from the ORBIS database covering 103 publicly listed automotive companies for the period 2019–2023. A Sugeno-type ANFIS model was developed and evaluated using the root mean square error (RMSE) and normalized root mean square error (nRMSE). Our results indicate that removing the ROA indicator caused the most significant deterioration in model performance, whereas the removal of PM resulted in the smallest decrease. Based on these findings, the fundamental indicators ranked in terms of predictive importance were ROA, ROE, EPS, and PM. Our research strongly supports the effectiveness of ANFIS models in fundamental financial analysis and stock price forecasting, highlighting the differential significance of individual financial indicators in the valuation of automotive companies’ stocks.
Keywords: ANFIS; Neuro-Fuzzy; Fundamental Analysis; Automotive Industry; Stock Price; Financial Forecasting
JEL codes: C45; C53; G11; G17
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