Co-Movement, Portfolio Diversification, Investors’ Behavior and Psychology: Evidence from Developed and Emerging Countries’ Stock Markets
Bibliographic record
Abstract
The issue of co-movements is still crucial and arguable in international finance. An optimum and significant level of co-movement is highly desirable to investors, and it mostly depends on investors’ decisions (behavior and psychology). We use frequency–time bands and multi-scale-based wavelet analysis to investigate the co-movement between developed and emerging countries’ stock markets for better asset allocation and portfolio diversification strategies. The results show that a significant level of co-movement is observed between conventional and Islamic stock markets in developed and emerging countries, and it varies in terms of its time–frequency domain properties. Particularly, the dependency among conventional and Islamic stock markets is strong at 4–512-band scales. However, the USA Islamic stock market illustrates a higher level of coherency with the UK, Japan and China’s Islamic stock markets, while a relatively lower level of co-movement is detected with the Chinese composite, Malaysian and Indonesian Islamic stock markets. The findings further confirm that the developed countries’ stock markets are substantially influenced by the GFC in 2007–2008 and the European debt crisis in 2012, while this trend is surprisingly not observed in the emerging markets on a similar scale. Therefore, these crises have opened the door for the grabbing of portfolio diversification benefits from the emerging countries’ stock markets. These findings give some interesting insights to policymakers, investors and fund managers for portfolio diversification and risk management strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".