A Multivariate Functional Analysis of Cryptocurrency Accounting and Its Impact on Market Valuation: Mathematical Modeling of Thai Listed Companies
Bibliographic record
Abstract
This paper applies multivariate functional analysis to model the relationship between cryptocurrency accounting choices and market valuation. Analyzing data from 14 companies on the Stock Exchange of Thailand (2021-2024), a predictive function is developed through regression analysis to quantify the impact of accounting treatment choices on market capitalization. The mathematical model demonstrates that intangible asset classification (IAS 38) correlates with significantly higher market valuations compared to inventory classification (IAS 2), with an estimated difference of 14.9 billion Thai Baht when controlling for financial variables. The research contributes to applied functional analysis, numerical computation, and optimization theory by providing a mathematical framework that transforms qualitative accounting decisions into quantifiable market outcomes. This approach enables computational analysis of accounting-market relationships and offers an optimization framework for financial decision-making in the emerging digital asset domain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".