Bibliographic record
Abstract
Formålet med denne oppgaven er å undersøke om valutakurser fører til bedre diversifisering av internasjonale aksjeporteføljer.\nValutaer og aksjeindekser fra Sveits og G8 land (Frankrike, Tyskland, Storbritannia, Italia, USA, Canada, Russland, Japan) er satt sammen for tre underperioder mellom 1999 og 2013. To porteføljer (risikominimerende og avkastningsmaksimerende) ble simulert av Markovitz teori. I tillegg er det laget portefølje med like andeler i hvert marked (naiv portefølje). \nFor hele perioden 1999- 2013, samt 4- 5 års underperioder ble det konstruert 3 porteføljer. Avkastning og risiko ble målt ved hjelp av Sharpe Ratio, Value- at Risk (VaR) og Conditional Value- at- Risk (CVaR).\nFørst ble det funnet hvor stor andel av portefølje bør investeres til aksjeindeksene som tar hensyn til valutakursene. Deretter ble det kjørt deskriptiv statistikk for å finne hva avkastning og risiko til porteføljene hadde vært hvis de ikke hadde tatt hensyn til valutaene.\nResultatene fra porteføljeoptimeringene som er simulert ved hjelp av Markovitz teori, viser at porteføljer hvor valutakursene er inkludert, leverer oftest bedre resultater (høyere avkastning, lavere risiko) enn portefølje som tar utgangspunkt kun i aksjeindekser. I de siste fem årene, etter at finanskrisen har startet, observeres det en klar større påvirkning av valutaene på porteføljens resultater. \nKorrelasjoner mellom aksjeindeksene har økt etter finanskrisen, som fører til at diversifiseringspotensial blir dårligere. Derfor oppnår valutaene muligheter til en bedre diversifisering av porteføljene. \nMine resultater indikerer at diversifiseringseffekter fra valutaene i porteføljer har økt gjennom perioden 1999 til 2013, og at diversifisering av aksjeporteføljer er bedre uten sikring av valutakursene. \n\n\n\n\n\n\nThe purpose of this thesis is to examine whether foreign exchanges offers diversification benefits to a stock portfolios.\nForeign exchanges and stock indices from Switzerland and G8 countries (France, Germany, Great Britain, Italy, USA, Canada, Russia and Japan) are analyzed for period 1999 – 2013 and three sub- periods.\nTo portfolios were constructed by Markovitz portfolio theory (min risk and max return) in addition to naivly weighted portfolio. The risk and return are measured by Sharpe Ratio, Value- at Risk (VaR) og Conditional Value- at- Risk (CVaR).\nFirst it was found how much it should be invested in each country when foreign exchanges were taking into account. The next step was to find what risk and return the portfolios had when foreign exchanges weren't taking into account.\nResults from portfolio optimization based on Markovitz theory showed that portofolios where foreign exchanges were included, given better results (higher return, less risk) than portfolios which weren't taking into account the foreign exchanges. Especially the last 5 years, after financial crises begun, it was observed that foreign exchanges have influenced portfolio results even more. \nCorrelations between stock indices have grown after financial crises which results less diversifying potential. Therefore foreign exchanges give possibilities to diversify portfolios. \nMy results show that diversifying effects from foreign exchanges in portfolios have grown in period from1999 to 2013. Diversifying of stock portfolios is better without currency hedging.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".