Bibliographic record
Abstract
ii This thesis evaluates the ability of Wilkie’s stochastic investment models to predict TSX price index yield. In the Wilkie Model, correlated econometric indices were modeled through a cascade structure. The empirical study of TSX price index yield in this thesis shows the following results with regard to the Wilkie Model: • The advised multivariate models by the Wilkie Model do not make significantly better prediction than univariate models do. • The dividend yield model is not suitable for prediction based on recent data ana-lysis. • The suggested new model structure based on vector autoregressive method in this thesis is similar to the Wilkie’s structure, but not a cascade one. There are some significant feedback relationships between different components. Therefore this thesis suggests a multidirectional model structure without dividend yield. The models are used to predict the movement of the components within the structure. The models for those variables is constructed from the linear relationship on their own lagged values and other variables in the structure. iii Acknowledgements I would like to thank in deeply and sincerely to my supervisor Dr. Rohana S. Amba-gaspitiya, for his supervision, instruction, guidance for my thesis writing and relevant course studying, and thanks for his encouragement and friendship to me during my
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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 | 0.001 | 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) | 0.001 | 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".