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.
