Enterprise Value and Intellectual Capital: Study of BSE 500 Firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this paper is to estimate the intellectual capital coefficient of the firms under study and to study the relationship, if any between intellectual capital and intellectual capital and its constituents. In this empirical paper, analytical research design has been used. Pulic’s VAIC (modified) has been used to estimate the intellectual capital of BSE S&P 500 listed firms from 2007-2016. The data has been collected from CMIE and collected data has been analyzed using Pearson correlation and linear multiple regression analysis using CMIE PROWESS. Findings show that almost all firms under study have a good VAIC score means above 4 and the top VAIC scorer firms were mainly from refinery, metal, cement, steel, tobacco. Correlation analysis and Linear multiple regression analysis show that M/B ratio has a significant relationship with VACA, VAHU, Research and Development (Innovation capital) and Advertisement expenses (customer capital). Year-wise results depicts that value of adjusted R2 is increasing, in 2007 it was just .164 and in the year 2016 it is .607 which infers that VAIC’s role is improving in measuring the market value of firms under study. Year wise analysis shows that adjusted R2 is improving, so findings may serve as significant input for the firms to use intellectual capital as the main factor for improving the market value of firms. This paper will definitely contribute to the existing literature.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it