A Comparatative Study On The Co-Movement Of Nifty And Global Indices (DAX, DJI, Hang Seng) During Pre And Post Covid Period
Bibliographic record
Abstract
Nowadays, thanks to the advancement of modern technology, pertinent information may travel easily throughout the globe. If the information is affecting the economy of that nation, the stock markets are also responding to it. Many academics have recently expressed interest in researching the long-term correlation between national stock market indices and other international indices. We have made an effort to research the potential co-movement and co-integration of indexes with comparable market caps, such as the NIFTY, DAX, DJI, and HANG SENG. The study examines the indices' weekly close prices during the ten-year period between April 2011 and March 2021. We also talk about the issues that affected the key indices during the Covid period, which was extremely stressful. The "Granger and Causality Correlation" test was used to study the measurement of co-integration and attempt to analyse collinearity among the chosen indices in order to determine whether there is a long-term and short-term link between them. The outcome reveals a long-term, significant positive correlation between the chosen indices. In contrast to the Asian Market, the correlation has diminished with the American and European markets. Therefore, the study is useful for the investors who trade based on the interdependencies of the indices and understand their co-movement.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".