The Impact of Global Financial Crisis on Market Efficiency: An Empirical Analysis of the Indian Stock Market
Bibliographic record
Abstract
This paper analyses the impact of the Global Financial Crisis on the informational market efficiency of the Indian Stock Market. In particular, the research focuses on analysing the stock market behaviour in three different sub-periods: Pre-Crisis, Crisis and the Recovery period. Various statistical methods, both parametric and non-parametric tests are employed to check if the market follows a random walk process. This helps in assessing the efficiency of the market. The results of the analysis show that the market is weak form inefficient in all three sub-periods. The informational market efficiency improved marginally from the Pre-Crisis period to the Crisis period and increased further from the Crisis period to the Recovery period. The informational inefficiency is an important criterion for the smooth functioning of the market because in an informationally inefficient market, the securities are not always fairly priced and this provides an incentive to traders to collect and use the relevant information to devise trading strategies which helps them in earning abnormal returns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".