Market frictions and the geographical location of global stock exchanges. Evidence from the S&P Global Index
Bibliographic record
Abstract
Purpose We examine the impact of market frictions in the form of trading costs on investor average holding periods for stocks in the S&P global 1200 index to examine constraints on international portfolio diversification. Design/methodology/approach We determine whether it is appropriate to pool stocks listed in the USA, Canada, Latin America, Europe, Japan, Asia and Australia into investigations using the same empirical specification. This is very important because the pooled effects may not provide consistent estimates of the average. Findings We report overwhelming econometric evidence that it is not valid to pool stocks in all the underlying regional equity indices for our investigation, indicating that the effect of frictions varies between markets. Research limitations/implications When we pool the stocks within markets, we discover that for companies listed in the USA, Europe, Canada and Australia, market frictions do not significantly influence holding periods and hence are not a barrier to portfolio rebalancing. However, companies listed in Latin America and Asia face market frictions, which are significant in terms of increasing holding periods. Practical implications We ascertain that taking into account the properties of stock markets in different geographical locations is vital for understanding the limits on achieving international portfolio diversification. Originality/value Unlike prior research, we overcome the problems caused by contemporaneous correlation, endogeneity and joint determination of investor average holding periods and trading costs by employing the Generalized Method of Moments (GMM) system panel estimator. This makes our empirical estimates robust and more reliable than the previous empirical research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".