Bibliographic record
Abstract
Purpose Corporate risk management is one of the critical concerns of managers when they make investment allocation decisions among multiple projects. The purpose of this paper is to address corporate investment issues illustrated by target‐beating in capital budgeting, and further discuss their applications in financial management, especially in venture capital finance. Design/methodology/approach Value‐at‐risk, a typical down‐side risk measure which is considered more appropriate for economic agents, is applied to the analysis. Probability theory and optimal control methodologies are used to derive analytical solutions. Findings By maximizing the probability of beating a pre‐determined target, an analytical optimal corporate investment allocation strategy is presented, and the corresponding probability and expected earliest time of success derived. Research limitations/implications Various types of utility functions of economic agents and other dynamic downside risk measures can be considered in future research along this line. Practical implications This paper paves the road for applications of continuous‐time downside risk in making corporate investment decisions, especially in the field of new venture finance. Originality/value As one of the early studies investigating optimal investment decisions in continuous‐time downside risk‐based capital budgeting system, this project sheds light on corporate risk management, and provides risk‐averse decision makers with an effective tool.
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 | 0.003 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".