Home Biasness & Internationell Diversifiering : Börjar Fördelarna med Internationell Diversifiering Sina?
Bibliographic record
Abstract
Is home biasness common among modern investors? To which extent do Swedish investors diversify their investments on an international level? Does home biasness negatively affect the investors performance? To answer these questions, correlation tests of various international indices ranging over four different time periods are conducted, in order to see if correlation between markets are stronger today than before, as stronger correlation would render diversification less useful. To enhance the study, the holdings of the top ten Swedish funds, measured in fund capital according to Morningstar, is reviewed, based on data collected per 2014-12-31 from the Swedish Financial Supervisory Agency (FI). This gives an overview of how the funds diversify their investments internationally, these funds will in turn represent the average Swedish investor in the thesis. By constructing a bullet curve from a set of international indices, the author will analyse to which grade international diversification is useful. The results are that international diversification isn’t as beneficial as theory suggests it is. The reason for it may be due to stronger correlation between international markets in the past 15 years. Most of the Swedish funds tends to be rather home biased in their investments, as about a quarter of the holdings usually are placed in Swedish assets, and in accordance with the results of the indices development, the more home biased they are to Sweden, the better they tend to perform.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.021 |
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".