Momentum Returns in Tehran Stock Exchange: The Influences of Size and Liquidity
Bibliographic record
Abstract
A study was carried out during 2001 to 2010 to illustrate the profit of momentum strategy in Tehran stock Exchange, and possibility of higher abnormal return based on past performance trends. In this study, the effects of two substantial variables including size and liquidity on profitability of momentum strategy in Tehran stock Exchange were investigated. The study was conducted in two sub-periods: 2005-2010, and 2007-2010. The first is due to a significant change in the number of stock companies after privatization, and the second is to cover the effects of global financial crisis on Iran’s economy. Our sample includes three separate samples of all stock market joined companies before the mentioned related periods that have been traded in at least half of the total trading days in the related period. Results showed that using momentum strategy in Tehran Stock Exchange created negative returns in all periods. Moreover, liquidity factor did not affect the profitability of momentum strategy. Meanwhile, considering size as another fundamental factor, the positive effect of small stocks was identified only in one period, from 2005 to 2010. In other periods, however, it led to negative returns.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".