THE IMPACT OF MACROECONOMIC FACTORS ON MSCI PRICE INDEX IN INDUSTRIAL COUNTRIES
Bibliographic record
Abstract
The purpose of this study is to analyze the development of the MSCI price index to determine the degree to which selected macroeconomic variables explain its historical movements. Explanatory power is studied across 11 industrial countries so that potential geographic differences can be examined as well. The research period is 1969–2015. The primary motivation for conducting this research stems from previous findings concerning the relationship between macroeconomic factors and different asset classes. The contribution of the study is to provide a novel perspective to support recent research results by using the MSCI price index returns in industrialized countries and macro variables that are selected based on previous academic literature. The empirical portion of this paper examines the research problem using time series multiple regression. The research methodology consists of three parts, the first of which involves choosing the research sample. The sample is quite large, covering research data from 11 selected countries. The second step examines the annual returns from each of the selected MSCI indices, after which the output is studied in relation to selected macro variables. The empirical results demonstrate that all six explanatory variables have statistically significant relationships with the MSCI price index in some of the studied markets. The obtained results suggest that the causality between the macroeconomic factors and MSCI price index is strongest in Canada, which has three statistically significant variables. The determination coefficient of the common explanatory power of variables is also highest for the Canadian markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".