Financial Networks: A Study of the Toronto Stock Exchange
Bibliographic record
Abstract
In this study, filtered network approaches such as Minimum Spanning Tree and Planar Maximally Filtered Graphs, are used to analyse the topological structure of constituents of S&P Toronto Stock Exchange Composite Index for a period of three years from January 1, 2015 till Decemeber 31, 2017. For this purpose, rolling correlation for each pair of stocks was calculated for six different time windows of 1, 2, 3, 4, 6 and 12 months. Based on the topological structure, the stocks were categorized into core and peripheral stocks using network measures such as degree centrality, betweenness centrality, eccentricity and eigenvector centrality. Categorization of stocks into core and peripheral was consistent for both MST and PMFG based networks for all time windows. Financial stocks were found to be core stocks. Topological structure helps to understand the inter-relationship among the stocks. It would aid in interpreting the nature of economic factors affecting similar group of stocks. Identification and categorisation of core and peripheral stocks could be used as a base for construction of portfolios and risk management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".