Dissemination of Information on Investor Attention, Firm Size, and Year-End Market Dynamics: An Empirical Study of the Indian Stock Market
Bibliographic record
Abstract
This paper investigates the dissemination and presence of the “Year-End Market Surge”, commonly referred to as the “Christmas Rally”, in the Indian stock market. In developed nations, this phenomenon describes a notable increase in stock prices typically observed during the last week of December and the first two trading days of January. Recent reports in the popular press suggest that a similar trend has been witnessed in the Indian stock market over recent years. However, there remains a lack of systematic research on this subject. Therefore, this study rigorously examines whether this market surge, which poses a potential challenge to the Efficient Market Hypothesis (EMH), is observable in the Indian context. Furthermore, the paper explores the dissemination of firm-specific trading patterns to identify characteristics of companies that have consistently delivered positive returns during this period over multiple years. The findings reveal that larger stock portfolios in the Indian market consistently benefit from the Year-End Market Surge effect, delivering higher abnormal returns compared to smaller portfolios. These results provide important insights into the role of firm size in capturing the benefits of this seasonal market anomaly.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".