The modeling firm's value based on financial ratios, intellectual capital and dividend policy
Bibliographic record
Abstract
The firm's value becomes fundamental and important when it goes public which is a basis for investment decision.Intellectual capital as a spectrum of artificial intelligence capabilities helps us reveal patterns of big data in information-based historical data to do jobs faster and better with the help of technology.This research purposes to verify the model firm's value based on financial ratios, intellectual capital, and dividend policy.The populations in this research are on the automotive subsector companies and components that are listed in Indonesia Stock Exchange (IDX) over the period 2010-2019 and eleven companies met the requirements for sampling.Methods of data analysis is based on path analysis and Sobel test which comprise the classical assumptions test, linearity test, the total coefficient of determination and estimation, and hypothesis test through direct and indirect effect.The results of this research indicate that the firm's modeling value based on financial ratios, intellectual capital and dividend policy with outcome findings of financial ratio's viz.liquidity, solvency and profitability ratio did not significantly influence the dividend policy, while dividend policy had a significant influence on firm's value.Furthermore, financial ratios mediated by dividend policy were only influenced by solvency and profitability ratios while the liquidity ratios and intellectual capital factors were not significant effects.
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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".