A Comparative Study of Behavioral Finance and Behavioral Economics in Iran and the World
Bibliographic record
Abstract
The aim of this study is to compare the behavioral finance studiea and behavioral economics in Iran and the world. The statistical population is the publication of articles published in accounting, finance and economics journals during the period from April 2003 to June 2013. The reason for the selection of 2003 is that the first paper published with the key word is in 2003. The method of collecting information is based on the meta-analysis. Published researches in the field of finance and economic affairs of domestic journals with a variety of behavioral intrusions with published researches in the field of finance and financial affairs in the United States, Britain, Germany, France (NewZealand), Australia (Canada), China, Spain and Israel are compared over the years 1967-2015. Based on the findings, there is a correlation between the published researches in the field of behavioral economics and the field of behavioral finance (53%) and based on the realm of time (63%) and the share of behavioral financial research in Iran is 42.2% which shows that the Journal of Investment Knowledge is one of the leading journals. The first research on the key word for behavioral finance in Iran is published in 2004 in the Financial Research Journal. The contribution of behavioral economics research is 32.1%, which is the leading asset management and finance magazine, and the first research with the word behavioral economics was published in 2003 in the Economic Research Journal.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".