Bile?ik ?nc? G?stergeler ve Borsa Endeksi ?li?kisinin Uluslararas? Boyutta ?ncelenmesine Y?nelik Bir Ara?t?rma
Bibliographic record
Abstract
Bu ?al??man?n amac? bile?ik ?nc? g?stergeler ve borsa endeksi ili?kisininuluslararas? boyutta incelenmesidir. Bile?ik ?nc? g?stergeler ekonominin gelecektekiy?n?n?n tahmininde kullan?lan ve bir?ok ?lke taraf?ndan referans kabul edilen birendekstir. ?al??mada dokuz Avrupa ?lkesi (?ngiltere, ?spanya, Hollanda, ?talya,Almanya, Fransa, Bel?ika, Avusturya, T?rkiye), be? Asya ?lkesi (Kore, Japonya,Endonezya, Hindistan, ?in), d?rt Amerika K?tas? ?lkesi (ABD, Kanada, Meksika,Brezilya) olmak ?zere on sekiz ?lkede bile?ik ?nc? g?stergelerle menkul k?ymet borsas?endeksleri aras?ndaki ili?ki ara?t?r?lacakt?r. Ara?t?rma verileri 2000:01-2010:12y?llar?n? kapsayan ayl?k verilere dayanmaktad?r. ?al??mada s?z konusu de?i?kenleraras?ndaki ili?kilerin incelenmesinde zaman serisi analizi, panel veri ve panele?b?t?nle?me analizleri kullan?lm??t?r. Analiz sonu?lar?, Almanya hari? t?m ?lkelerdeve t?m k?talarda bile?ik ?nc? g?stergelerin borsa endeksi ?zerinde anlaml? bir etkisininoldu?unu ve bu iki de?i?kenin uzun d?nemde ili?kili olduklar?n? g?stermektedir. The aim of this study is to investigate the relationship between stock exchangeindex and composite leading indicators in an international dimension. Compositeleading indicators is used to estimate the economy's futures direction and accepted as areference index by many countries. In this study, the relationship of composite leading indicators and stock exchange index is investigated in nine European Countries(England, Spain, Netherlands, Italy, Germany, France, Belgium, Austria, Turkey), fiveAsian Countries (Korea, Japan, Indonesia, India, China) and for Americas (USA,Canada, Mexico, Brazil).Research data is based on monthly data covering the period 2000:01-2010:12. Inthis study in assessing the relationships between these variables, time series analysis,panel data and panel cointegration analysis were used. Results of the analysis showthat composite leading indicators have a significant effect on the stock market index inall countries except Germany and in all continents and these two variables associatedwith in long-term.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".