Dimensions Culturelles, Stratégies d'Investissement et Liberté Economique : preuves Internationales
Bibliographic record
Abstract
Culture, i.e. our values, norms, beliefs, and expected behaviors, has an important influence on all aspects of our social behavior. This thesis focuses on the effect of culture on financial behavior. More specifically, it is examined whether different dimensions of culture, such as power distance, individualism, uncertainty avoidance, masculinity, and long-term orientation have an impact on the performance of style investing portfolios (momentum, value and growth investing portfolios) for a sample of international markets (Australia, Brazil, Canada, Germany, India, Japan, United Kingdom). The results of multivariate regression analysis, Principal Component Analysis, Panel VAR, and Variance Decomposition analysis indicate that portfolio performance is related to cultural dimensions; individualism, long-term orientation and masculinity seems to have a positive significant impact, whereas power distance and uncertainty avoidance exhibit a negative influence. Moreover, motivated by recent research that documents a relation between social factors and the way individuals experience economic rights, in this thesis, we also investigate the relationship between culture and the level of economic freedom presented in a country’s institutional structure. Our results show that the culture of a country is linked to the level of economic freedom enjoyed by its residents. In addition, this relationship seems not to be confined in any particular geographical region but it can be found across the world.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".