Delisting sharia stock prediction model based on financial information: Support Vector Machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this research is to develop an early warning system model that can anticipate the occurrence of delisting of Islamic stocks (ISSI) using Support Vector Machines (SVM). Financial variables used consist of debt to equity, return on invested capital, asset turn over, quick ratio, current ratio, return on assets, return on equity, leverage, long term debt, and interest coverage. The population of this study is 335 sharia shares registered at ISSI in the period 2012-2017, with a total sample of 102 companies. The results show that the financial variables had a predictive power to the occurrence of delisting of Islamic stocks in the ISSI index. The effect of the independent variable or predictor variable is the financial ratio to the target variable or the dependent variable that is the potential for delisting of Islamic stocks in the ISSI index. With the development of 4 SVM models with different levels of prediction accuracy, SVM Model 1 with an accuracy rate of 71.57%, SVM Model 2 with an accuracy rate of 72.55%, SVM Model 3 with an accuracy rate of 82.35% and SVM Model 4 with an accuracy rate of 100%, it can be concluded that the SVM Model 4 is the best model.
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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 | 0.012 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it