International diversification benefits: an investigation from the perspective of Chinese investors
Bibliographic record
Abstract
Purpose The main purpose of this paper is to investigate whether Chinese investors can benefit from international diversification and where these benefits are to be found. Design/methodology/approach This paper applies an expanding optimization procedure, which is different from the econometric methods or Monte Carlo simulations adopted in many empirical investigations in the literature. The authors' analysis is based on various realized portfolios that are set up at different dates in the sample period. Findings Based on a stream of realized portfolios, the authors show that Chinese investors can gain substantially in terms of risk reduction as they venture into foreign markets, regardless of the region into which they choose to diversify and whether in‐sample or out‐of‐sample performance is evaluated. However, the optimal strategies under consideration cannot achieve higher out‐of‐sample expected returns and risk‐adjusted returns than does the domestic investment. Originality/value In contrast with those in the literature, the authors' analysis is based on the out‐of‐sample performance of a series of realized optimal portfolios. Their method can address time‐varying correlations that are ignored in most previous research. In addition, this method not only allows them to analyze sizes of diversification benefits but also enables them to examine the major characteristics of international portfolios to gauge the effectiveness of different diversification 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 | 0.002 |
| 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".