Research on Computational Simulation and Capital Structure Adjustment in Corporate Investment and Financing Decisions under ESG Rating Mechanisms
Bibliographic record
Abstract
The problems of debt value and optimal capital structure of enterprises are the main issues in corporate finance research.Under the ESG rating mechanism, the article first utilizes the real option theory to study the optimal capital structure and investment and financing decision-making methods of enterprises.Then it puts forward model assumptions and combines the jump diffusion model for the construction of enterprise project investment and financing decision-making model and the dynamic planning adjustment of capital structure.Finally, through specific numerical experiments, the influence process of each variable in the model on the enterprise investment and financing decision is analyzed, and the agency problem is analyzed.Through the experiment, it can be obtained that when the residual value after stopping production, the risk-free interest rate, the variable production cost and the tax rate are set to =1, r =0.1, =0.1, =0.2, respectively, with the increase of the frequency of the jump, the investment price of the positive-jump model gradually decreases, and the investment price of the negative-jump model gradually increases, which can be obtained that the reasonable simulation estimation of the relevant parameters has an important impact on the enterprise's investment strategy, so the enterprise should make a more accurate assessment of the parameters, otherwise they will lose part of the benefits or lose good investment opportunities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".