Investigating the effect of adjusted DuPont ratio and its components on investor's decisions in short and long term
Bibliographic record
Abstract
This paper investigates the effect of adjusted DuPont ratio and its components on investors' decisions in short and long term. The primary objective of this study is to find the effect of adjusted DuPont ratio and its components on herding behavior of investors in one and several year period. Hence, 85 corporations as the member of Tehran stock exchange over the period 2006-2011 are selected. In order to recognize the herding, by market index consideration, the herded and in order to hypothesis validity SPSS software and multivariable linear regression have been used. As the results of this study indicate, the adjusted DuPont ratio and its components have more effect on investors' decisions in short term but in long the period, the effect of this ratio on herding investors' behavior are reduced. Furthermore, from the two components of adjusted DuPont ratio, profit margin has more effect on investor's decisions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".