BFO Theory Principles and New Opportunities for Company Value and Risk Management
Bibliographic record
Abstract
This article explores the significance and additional capabilities of new principles for analyzing the capital structure and calculating the market value of a company. These principles are being developed as part of Brusov–Filatova–Orekhova theory (BFO) and are aimed at considering the diverse factors which affect the market value of companies. These principles include accounting and calculating the value of a company within its lifecycle; focusing on a more complete and differentiated assessment of a company’s risks and their consideration in the course of running the company and managing its market value, compared to in the Modigliani–Miller theory. According to these principles, one should take into account and assess all significant possible effects that are formed in the course of running a company with regard to its value, even if such effects do not explicitly materialize until a certain point of time, are not taken into account during the market appraisal and are used during the company valuation as some kind of a virtual, imaginary value. Changes in the calculation of such virtual values of a company value may suggest that risks have accumulated both at the micro and macro level of economy. Studying the mechanisms created in the course of running a company and aimed at transforming the virtual values of its value into real positive or negative changes in the value can be an important tool for enhancing the effectiveness of risk management in companies and economic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".