Research on the relationship between economic efficiency and financial structure of enterprises based on multiple regression analysis
Bibliographic record
Abstract
In this paper, the financial structure is defined as two parts, asset structure and capital structure, with respect to the mechanism of enterprise financial management on the economic performance of enterprises.The multivariate regression model of asset structure and business performance is constructed with the dimensions of asset turnover efficiency and asset structure ratio.In order to represent the operating performance, total return on assets and return on net assets are chosen as the measures of operating performance and as the explanatory variables.It is proposed that there is a linear correlation between capital structure and corporate profitability, and the linear model between capital structure and corporate operating profitability is constructed.Combined with empirical tests to verify the relationship between asset structure or capital structure on business operations.The curve estimation method of the regression model is used to analyze the effects of inventory ratio, money fund ratio and fixed asset ratio in asset structure and capital structure on the total return on assets and return on net assets.The coefficients of fixed asset turnover on performance are 0.033 and 0.025 respectively, i.e., for every increase of 1 in fixed assets, total return on assets and return on net assets increase by 0.033 and 0.025.Similarly, the fixed asset turnover, inventory turnover, and the ratio of long term financial assets are positively correlated with the performance of the enterprise.The correlation coefficients of equity ratio and state-owned ratio of enterprise capital structure are positive, which bring positive impact on enterprise operating profitability.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".