ON THE PERFORMANCE OF ARTIFICIAL INTELLIGENCE METHODS FOR FAILURE PREDICTION: EVIDENCE FROM ISTANBUL STOCK EXCHANGE
Bibliographic record
Abstract
The prediction of business failure is a widely studied subject in financial literature. Many earlier studies on this topic employed statistical methods such as multiple discriminant analysis, logit and probit to predict corporate failure using past financial data (especially the ratio data). However, there has been a recent surge in academic interest in the use of artificial intelligence (AI) methods to predict financial distress. Numerous studies documented that AI methods outperform traditional methods. Majority of these studies used data from established markets, the number of studies on emerging market data is rather limited and only a handful of studies employed Turkish data for analysis. This study aims to contribute to the literature by applying the artificial neural networks to predict deletions from Istanbul Stock Exchange (ISE) National 100 Index. The sample is constructed using the quarterly fundamental data of the companies listed in this index the period between January 2008 and December 2012. We employed Neural Networks (NN), logit and probit to predict deletions from index one quarter before they have occurred. Results show that although the logit provides slightly better in-sample predictions, all of the methods fail to identify deletions in the out-of-sample periods. Key Words: Neural Networks, Genetic Programming, Business Failure Prediction, Emerging Markets
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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 | 0.001 |
| 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".