Is Socially Responsible Indices Weak Form of Efficient Market? Evidences from Developing Economies
Bibliographic record
Abstract
The study is an attempt to identify the presence of randomness in the socially responsible indices (SRI) of the stock markets of developing countries. Five developing economies are considered for the test of randomness on daily, weekly, monthly, quarterly and semiannual return of socially responsible indices and their benchmark indices. Shapiro-Wilk test is used to test the normality of the data whereas Runs test and Augmented Dickey-Fuller test are used depending on the randomness of the data. It is observed that India, Arab and Egypt show non-randomness whereas Brazil and South Africa show randomness in daily returns. Weekly returns on the other hand are random in Brazil, Arab, South Africa, and non-random in India and Egypt. Monthly and quarterly returns show randomness in India, Arab, Egypt, South Africa and non-randomness in Brazil whereas semiannual returns show randomness for all economies. It is also observed that most socially responsible indices resonate the randomness patterns of their benchmark indices. Most of the non-randomness is seen in short-run indicating inefficiency in the market. However, in long-run, the market goes random or efficient which is an indication that more than average profit can be earned by resorting to technical trading in the short run. Moreover, the similarity in randomness between socially responsible indices and their benchmark indices indicates that similar trading strategy can be applied by traders in both these indices to garner profit.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".