Long Term Dynamics of Indian ADRs Market: The Case of Persistence and Irregular Cycles
Bibliographic record
Abstract
The focus of this study is to understand the previously ignored return generating dynamics of American Depositary Receipts (ADR) markets. The main objective of this study is to investigate the nature of the return generating process of the Indian ADRs market. Specifically, the study addresses following interrelated research questions: Do returns series of Indian ADRs market exhibit random walk behavior or rather depict persistence and nonlinear dynamics? Is there any cyclicity in the returns series of Indian ADRs market? Rescaled Range (R/S) method on daily and weekly return series of Bank of the New York Mellon Indian ADR index (BKIN) from 2002 to 2016 has been applied to address the above questions. Empirical findings revealed that returns series of Indian ADRs market: (a) do not exhibit random walk behavior and rather depict both nonlinear behavior and persistence (long range dependence); (b) possess non-periodic cycles of 0.793, 2.38 and approximately 7 years. The findings can work as crucial inputs to forecasting, risk-management and market regulation processes. The knowledge of the average cycle length and persistence will enhance preparedness to handle the opportunities and risks at all levels in the market.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".