Analyzing the impact of financial variables and market characteristics on corporate stock returns in the short and long term after initial public offering
Bibliographic record
Abstract
This study examines the relationship between short-term and long-term stock returns of companies after initial public offering by considering financial variables and financial and ownership characteristics of companies on the Tehran Stock Exchange. The research sample includes 4560 companies that were publicly listed on the stock exchange in the period from 2013 to 2024, which constitute a total of 4560 company-years. Econometric methods and vector regression models have been used to test the hypotheses. First, the statistical description of the data has been discussed and then various tests including ADF and PP unit root tests to examine the stationarity of the data, Durbin-Watson test to examine autocorrelation, Chow test, F test and Hausman test have been used to select the appropriate model. The results of these tests show that the main hypothesis of the study is that there is a significant relationship between short-term and long-term stock returns of companies after initial offering is confirmed. Finally, the results of this study can be generalized with 95% confidence to the entire statistical population of the study, namely active investors in the Tehran Stock Exchange.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".