Paradigm Shift in Finance: The Transformation of the Theory from Perfect to Imperfect Capital Markets Using the Example of Company Valuation
Bibliographic record
Abstract
In the capital market and financing theory, we are currently observing major upheavals. For decades, the neoclassical paradigm has dominated in science and practice. Triggered by economic and political crises, transformations, the COVID-19 pandemic, and political instabilities, a paradigm shift is currently occurring in finance. This paradigm shift leads to models and theories that can explain imperfections in capital markets and provide decision support for managers. The aim of this article is to analyse the paradigm shift and to demonstrate it using an example of business valuation theory. We draw on the insights of the philosopher Thomas Samuel Kuhn. He vividly explains the paradigm shift in science in his major work “The Structure of Scientific Revolutions”. A paradigm shift in science always encounters resistance. The reasons for this include the strong neoclassical school in finance and the dependence on research funds. Funders expect the use of established methods and the simplicity and dissemination of the models that have prevailed so far. On the other hand, the neoclassical models are unsuitable to explain the transformation processes on financial markets. This fact has been empirically proven. We show a variety of arguments that speak clearly about this paradigm shift. Their importance clearly outweighs the reasons to continue subscribing to the old paradigm. Accordingly, new theories and models have been developed to better explain the changes in the markets. With the simulation-based business valuation, an approach has been developed that considers different degrees of market imperfections. The simulation-based valuation can also depict the special case of the neoclassical paradigm, so that all market constellations can be covered.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".