Sustainable Investing Based on Momentum Strategies in Emerging Stock Markets: A Case Study for Bombay Stock Exchange (BSE) of India
Bibliographic record
Abstract
This research article examines the profitability on the momentum portfolios in the case of the emerging stock market of India, i.e. Bombay Stock Exchange (BSE). Sustainable investing integrates environmental, social and governance (ESG) characteristics into investment decisions. Risk management is one of the most significant ranking factors determining the adoption of corporate strategies based on sustainable investing. A sustainable stock market provides a transparent and effective solution to inherent challenges related to environmental, social, economic and corporate governance issues. The theoretical and empirical analysis conducted in this research article reveals the status of BSE of India in this regard. A company's sustainable market orientation is very important for future developments. The practical significance of this research paper is to investigate the profitability of momentum strategies in Bombay Stock Exchange of India, which is an emergent market. Moreover, the presence of short term momentum effect on Indian stock market is basically an anomaly caused by behavioral and risk-based portfolio construction factors. On the other hand, momentum strategies is a reliable alternative with strong empirical evidence to both fundamental approaches of classical finance, namely efficient market hypothesis (EMH) and behavioral finance paradigm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".