Factors of Stock Return and Carhart Model: The Case of Dhaka Stock Exchange (DSE) of Bangladesh
Bibliographic record
Abstract
The objective of this study is to find factors of stock return by testing validity of Carhart model in Dhaka Stock Exchange (DSE) of Bangladesh. For this purpose, this study uses monthly excess return of portfolios, size, book-to-market value, market return, and price momentum data of 109 sample firms to calculate return factors such as market risk premium, size premium (SMB), value premium (HML), and momentum effect (UMD) for the sample period of 2005 to 2014. Then a total of ten portfolios, six based on size and book-to-market value and four based on size and price momentum, are constructed in this study. Excess return of each of these portfolios are calculated and regressed on the above four factors. Results of this study reveal that in DSE, market risk premium is positively and significantly related with the excess return of all portfolios; Size premium is found positively and significantly related with the return of small size portfolios; Value premium is found negatively and significantly related with the returns of all portfolios except one big portfolio (B/H); momentum effect is found positively and significantly related to the excess return of up (U), big (B), and small (S) size portfolios. It is also evident from R2 value, F statistic, and robustness test of this study that four-factor model is valid and it can predict portfolio returns accurately when there is no abnormality such as market crash occurs in DSE.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".