Bibliographic record
Abstract
Emerging equity markets have attracted foreign investor by their higher returns and prospect of superior risk diversification benefits. In light of increasing flow of equity portfolio investments into these economies and their subsequent integration with equity markets of developed world, studies have not only shown concern over the reduction in the long term risk diversification benefits, but also there may be less of increase in the original price of securities. Local economy initiates formal financial liberalisation measures to integrate with world capital markets. However, removal of regulatory restrictions may not attract foreign investments in the presence of other indirect barriers and emerging markets specific risks. Also, the process of financial liberalisation is time varying and not one off event. This creates difficulty in pin pointing the exact date of liberalisation. These complexities cause difficulties in the development of dynamic models for pricing securities in emerging markets and measuring the impact of integration. However, with the removal of direct and indirect barriers to foreign investments, these markets are showing greater integration with world markets. With increasing integration emerging markets are becoming more susceptible to global risk factors. Higher degree of integration should reduce cost of equity capital (expected return) and increase the correlation of returns with developed markets. However, empirical works report the reduction in cost of capital to be lower than predicted by asset pricing models. It is also challenging to measure the degree of market integration because of the constant structural changes observed in emerging markets. Countries have even been found to exhibit segmentation over time. Hence, in the context of asset pricing models the findings on the degree of integration are inconclusive and conflicting.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".