Development of Financial Indicator Set for Automotive Stock Performance Prediction Using Adaptive Neuro-Fuzzy Inference System

Tamás Szabó, Sándor Gáspár and Szilárd Hegedűs ()
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Tamás Szabó: Faculty of Finance and Accountancy, Budapest University of Economics and Business, 1149 Budapest, Hungary
Sándor Gáspár: Institute of Rural Development and Sustainable Economy, Hungarian University of Agriculture and Life Sciences, 2100 Gödöllő, Hungary
Szilárd Hegedűs: Department of Finance, Faculty of Finance and Accountancy, Budapest University of Economics and Business, 1149 Budapest, Hungary

JRFM, 2025, vol. 18, issue 8, 1-23

Abstract: This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains.

Keywords: ANFIS; fuzzy logic; automobile industry; stock price prediction; fundamental analysis (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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