Akcijų rinka ir ekonomikos augimas JAV ir Prancūzijoje: akcijų rinkos sektorinių indeksų panaudojimo rezultatai.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many scientists have analyzed a relation among economic and financial market variables. One of the purposes of these researches, and probably the most important one, is to find a better prediction of future economic changes. Economic theory suggests a strong link between stock market and economic activity. The question is whether the stock market is a predictor of future economic activity measured as growth of country’s gross domestic product. In this paper we analyze which economic sectors, represented by stock market sector indices, could have most impact on GDP. The aim of this paper is to analyze whether some sectors are more important than others while analyzing GDP change. The study uses data for the period 2000 Q2 – 2012 Q1 of the U.S. seasonally adjusted GDP and Dow Jones indices and data for the period 2001 Q1 – 2012 Q1 of France seasonally adjusted GDP and Euronext CAC indices. In order to find a relation between selected stock market sector indices and GDP, we will use cross correlation analysis. Our findings support the theory that stock market is a leading indicator for economic growth. In France stock market appears to be a stronger indicator for economic growth compared to the U.S. The results revealed that seasonally adjusted GDP growth lagged behind changes in stock market indices four quarters in France and three quarter in the U.S. In the U.S. worst predictive capabilities for GDP growth come from utilities, oil and gas sectors. Industrial and financial sectors gave the best cross correlation results with GDP growth. In France telecommunication, utilities sectors have the worst predictive capabilities, and consumer services and health care sectors gave the best cross correlation results on GDP growth, compared with others.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it