The Empirical Research on Financial Risk Factors of GEM Companies
Bibliographic record
Abstract
GEM has injected fresh blood into the development of China’s securities market; it eased the long-term “financing” problem of high-tech SMEs effectively. However, the high risk of the GEM market has caused great concern of all parties. This paper analyzed the factors of affecting financial risk of GEM companies with the regression analysis methods. Empirical results show that GEM Company’s financial risk and the scale of equity is a negative correlation, and corporate solvency, profitability, operational capacity, development capacity, investment income and cash flow does not have a significant linear relationship. The paper further proposed the measures of controlling financial risk in the GEM Company. Key words: GEM; Financial risk; Regression analysis Resume: Le GEM a injecte du sang neuf dans le developpement du marche chinois sur les valeurs mobilieres, il allege le long terme financement probleme de PME de haute technologie de maniere efficace. Cependant, le risque eleve du marche de GEM a provoque une grande inquietude de toutes les parties. Ce document analyse les facteurs qui affectent des risques financiers des societes GEM avec les methodes d'analyse de regression. Les resultats empiriques montrent que le risque financier de la Societe GEM et l'echelle de l'equite est une correlation negative, et la solvabilite des entreprises, la rentabilite, la 1 capacite operationnelle, la capacite de developpement, les revenus d'investissement et les flux de tresorerie n'a pas de relation lineaire significative. Le document propose en outre les mesures de risque financier majoritaire dans la Compagnie du GEM. Mots cles: GEM; Des risques financiers; L'analyse de regression
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".