The Risk Comparison between Large Enterprises and SMEs on Chinese Pharmaceutical Industry: Based on the Solvency Indicators/ COMPARAISON DES RISQUES ENTRE GRANDES ENTREPRISES ET PME DE L'INDUSTRIE PHARMACEUTIQUE CHINOISE: BASÉE SUR LES INDICATEURS DE SOLVABILITÉ
Bibliographic record
Abstract
Abstract: The development of Medium and small enterprises (SMEs) in China are feeing many difficulties to overcome its economic contradictions and problems. Financing will bear the brunt. As the SME's own characteristics, which is small and the controllable resources are scarcer, often are growing rapidly, and require large capital investments. The current situation of SME financing is not good. The paper contributes using the basic method of statistical analysis of variance according to the research on financing difficulties of SMEs in relation to their business risks. Firstly the solvency indexes reflect the businesses are illustrated. Secondly, the paper introduces the basic principles of variance analysis and homogeneity of variance test and variance analysis of multiple comparisons. Finally the use of SPASS on the pharmaceutical industries' solvency targets is analysed through the financial indicators related to SMEs' finance difficult situation verified whether the risk of their operations has a significant impact. Keywords: SMEs; finance difficulty; operation risk; variance analysis; solvency ability. Resume: Le developpement des petites et moyennes entreprises (PME) en Chine est en train de faire face a de nombreuses difficultes pour surmonter les contradictions et les problemes economiques. Le financement fera les frais. Comme les caracteristiques propres des PME, elles sont petites, leurs ressources controlables sont plus rares, elles ont une croissance de plus en plus rapide et ont besoin de gros investissements. La situation actuelle du financement des PME n'est pas tres bonne. Le present article utilise une methode de base de l'analyse statistique de la variance en fonction de la recherche sur les difficultes de financement des PME par rapport a leurs risques d'affaires. Tout d'abord, les indices de solvabilite refletant les affaires sont illustres. Ensuite, l'article presente les principes de base de l'analyse de la variance, l'homogeneite du test de variance et l'analyse de la variance des comparaisons multiples. Enfin le SPASS a ete utilise pour analyser les objectifs de solvabilite des industries pharmaceutiques a travers les indicateurs financiers lies a des difficultes de financement des PME afin de verifier si le risque de leurs activites a un impact significatif. Mots-cles: PME; difficulte de financement; risque de fonctionnement; analyse de la variance; capacite de solvabilite (ProQuest: ... denotes formulae omitted.) 1. INTRODUCTION According to the end of November 2009 data, there are 9,718,900 domestic-funded enterprises (including private enterprises), and 4,341,000 forgeign-invested enterprises, and 3,184,020 individual businesses in china. SMEs in China weighted more than 99%, more than 40 million. The industrial output value, profits and taxes, exports have accounted for the country's 60%, 50% and 68% respectively. SMEs provided 75% job opportunities. The financing difficulties of SMEs has become the hotspot, has restricted the further development of SMEs in China. Financing difficulties of SMEs whether increases their operation risk is very important. The paper will explore the links between financing difficulties and operational risk of SMEs. Financial indicators refer to the collection of financial informations and illustration of financial activities which reflect production and operation process and outcome of economic indicators. Financial indicators is the datas in the calculation indexs through company's financial report. Solvency ability is the capacity to repay corporate debt (including principal and interest) within maturity. Repayment of debts in due date is an important indicator reflects the financial situation good or bad. Common indicators include: current ratio, quick ratio, asset-liability ratio, equity ratio. Current Ratio = current assets / current liabilities. The indicators reflect the company's capacity to repay short- term debt. …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".