Profitability and value of firm: An evidence from manufacturing industry in Indonesia
Bibliographic record
Abstract
The purposes of this research were to understand the profitability performance and its influencing factors based on DuPont Analysis and the effect toward the value of the firm. As a causality research, the sample data involved were 20 non-banking and finance companies as listed on LQ-45 of Indonesian Stock Exchange (IDX) years of 2014-2018, which could be classified into two types of industry; manufacture and non-manufacture sectors. The research’s quantitative design as a systematic approach of the relation among the variables focusing on the hypothesis testing done by data analysis tools using GLS Regression test of panel data. Profitability determinants of net profit margin, total assets turnover and financial leverage multiplier showed the result of positive and significant effect toward ROE (return on equity), while growth sales ratio showed the negative and significant effect. In terms of the relationship toward value of firm, the ROE and industry types were proven to have significant positive contributions. This implied that the management must be more efficient and effective in managing the company operational activities and minimizing the operational costs and other costs, both in the assets and debt usage to have maximal product results, to increase sales, net income, profit rate and return of equity where they will affect the increasing of investors’ and the market’s trust toward the firms since the increasing of return on equity for the owners and the shareholders. The different characteristics, traits and features of the industry’s types resulted in the different use of strategies in managing the firms’ operational activities. These all affected the increasing value of the firm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".