Clustering in key G-7 stock market indices: an innovative approach
Bibliographic record
Abstract
Investment in stock market requires taking risk. Investors typically buy several stocks to create a portfolio which targets to maximize the return while keeping a certain level of risk. In today's information-rich financial markets, one of the main challenges for individual investors in particular is to allocate the scarce sources appropriately within the wide range of investment alternatives that grouping the multiple assets based on their similar characteristics would be useful to take it out. In this paper, stock markets of the Group of 7 (G-7) countries consisting of France, United Kingdom, Germany, Italy, United States, Canada and Japan are examined over the period of 2011 and 2016 and some hierarchical clustering methods are applied on key indices namely, CAC 40 (France), FTSE 100 (UK), DAX (Germany), FTSE MIB (Italy), S&P TSX Composite (Canada), S&P 500 (USA), NIKKEI 225 (Japan) to identify the groups based on risk and return characteristics.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".