Assessment of Profitability Based on Reverse Strategy in Companies Listed in Tehran Stock Exchange
Bibliographic record
Abstract
Basically, investors in general and investors in securities including shares or bonds, in particular, are always looking for reliable and reasonable models that can help them choosing the number and time of the transaction of purchase and sale of their investments in order to maximize yields and guide them properly. In the last century with the development of financial markets, especially the stock market and more diversified securities of transactions in these markets, and more participation of larger groups of people in stock, their demands have become more important. Two important and widely used strategies among analysts include reverse and momentum strategies which against each other. They predict future performance using past performance. Momentum strategy believes that recent trends continue, but reverse strategy believes that recent trends will return.In this study conducted in a six-year period between 2009 and 2014 and its portfolio is made up, the results of this study in the Tehran Stock Exchange which has been due to two hypotheses showed that the mean abnormal return of loser and winner portfolios are positive and negative, respectively, and hypotheses have been confirmed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".