Application of Adaptive Neuro-Fuzzy Inference System for EPS Prediction in the European Banking Sector
Bibliographic record
Abstract
Financial forecasting remains essential for supporting strategic decisions and risk oversight in the banking sector. This study examines whether Adaptive Neuro-Fuzzy Inference Systems (ANFISs) can enhance Earnings per Share (EPS) prediction for European banks by integrating four core financial indicators: Return on Assets, Return on Equity, Capital Ratio, and Profit Margin. Using an annual panel of 25 institutions between 2013 and 2023, we benchmark multiple membership function shapes and granularities to identify robust model configurations. The empirical analysis combines chronological holdout testing with Leave-One-Out cross-validation to evaluate accuracy and stability. Findings highlight a sigmoid-based ANFIS specification with four fuzzy sets per input as the most consistent performer, offering interpretable rules that complement conventional forecasting techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".