Startup Company Valuation: The State of Art and Future Trends
Bibliographic record
Abstract
The aim of this conceptual article is to present a systematic literature review about the most used and innovative startup valuation methods to define the state of art and future trends on this important topic. Because of the particular features of early-stage companies, it is not easy to find an adequate method to assess their value. Traditional valuation methods are unsuitable for startups. Therefore, over time, academic literature and experienced investors created alternative and innovative valuation models. We analysed the main models, outlining the advantages and limits for each one. The results of our analysis show that there is currently no "perfect" method to assess a startup’s value. Each model discussed has significant limits, and the possibilities for improvement are many. We are witnessing a gradual withdrawal from more arbitrary valuation models, and consciousness is growing towards the idea that to better assess startup’s value, it is necessary to consider three aspects: attention to future forecasts instead of past data, using probability to consider different scenarios, and understanding of and attention to the specific business model of the startup rather than data on comparable companies in the market. Currently, none of the discussed methods integrates these three features harmoniously. We expect that in the near future, the academic literature will develop new valuation methods (or will perfect existing ones) that should consider the three characteristics mentioned previously. In this way, it would be possible to create a more suitable method to assess a startup's value, i.e., a method to reduce uncertainty and that better represents the startup’s value and makes startup company valuation more reliable.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".