The financial value of corporations in a cobweb economy
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a model and simulation results for the corporate financial value of an industry in a cobweb economy. Production‐consumption cycles affect profit and financial needs in terms of equity and debt capital, and thus corporate value over time. The model is applied to the Champagne industry. Design/methodology/approach The paper simulates the financial value of Champagne makers by taking into account developments on the Champagne market and the short‐ and long‐term responses by input suppliers and Champagne makers. Financial modeling is combined with a vertical coordination model of production and consumption in a cobweb economy. Findings This paper makes theoretical advances in modeling the impact of short‐ and long‐run temporal tensions in production decisions on the financial value of processors. Temporal tensions are central to the decisions made by input suppliers (grape growers) and processors (Champagne makers) as they negotiate in a context of vertical coordination in a cobweb economy. Financial aggregates are forecast by the model and used as market multiples for estimating corporate financial value. Furthermore, this research strengthens previously published simulation studies in agriculture and food markets since system dynamics (SD) is applied in modeling both input production and consumption and the processor's financial value. SD modeling is well suited to simulation in a critical context, and Champagne makers find themselves in such a context: the Champagne protected designation of origin (PDO) area has reached its legally authorized size limit, while world demand continues to grow. Practical implications The market for corporate control of Champagne makers is active. The model presented is a useful guide for decision makers because it improves the anticipation of corporate value and improves understanding of the future of value creation in a legal framework currently considering revision of both authorized annual yield (short‐run decision) and the size of the appellation area (long‐run decision). Originality/value Two original features of this paper add specific value to the existing research: first, the theory is enlarged to capture the temporal tensions affecting decision making by input suppliers and processors operating in a cobweb economy, and deduce processors' financial value using financial aggregates forecast by the model. Second, the SD simulation method is applied in modeling input production and consumption and processors' financial value.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".