Research on corporate profitability prediction model integrating fuzzy logic and financial ratios
Bibliographic record
Abstract
This study integrates fuzzy logic with DuPont ratio analysis re lecting inancial ratios to construct enterprise pro itability prediction model.The main indicators of DuPont analysis system are processed by principal component analysis (PCA) algorithm to obtain the calculation method of the mean value of enterprise comprehensive pro itability indicators.The BP neural network is used to construct the enterprise pro itability index model, and the momentum term is introduced into the model to improve the convergence speed of the BP neural network.The Takagi-Sugeno type fuzzy neural network is utilized to construct the enterprise development ability index model, and the enterprise pro itability prediction model is constructed by combining the output structure of BP neural network.The relevant data of 792 listed enterprises in a certain industry in China's A-share market are selected as the research objects of this paper, and the data are inputted into BP neural network and Takagi-Sugeno fuzzy neural network to obtain the output results of the model, and the output results are used as the input data of the inal pro itability prediction model to forecast the pro itability of the enterprise in the next ive years.The experimental results show that the model in this paper can effectively realize the prediction of corporate pro itability, which is signi icantly conducive to the sustainable development of enterprises and the adjustment and improvement of strategic policies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".