Impact of Financial Indices and Crude Oil Prices on Daily Closing Prices of Canadian Financial Institutions During the 2008-GFC and COVID-19 Pandemic
Bibliographic record
Abstract
This study examines the impact of fifteen financial indices and crude oil prices on the daily closing prices (DCP) of two major Canadian financial institutions, Sunlife and Manulife, during the 2008 Global Financial Crisis (GFC) and the COVID-19 pandemic, as well as over the period from September 2000 to December 2023. Two Multiple Linear Regression (MLR) models and a Simple Linear Regression (SLR) model were applied to the data for each company. The first MLR included the DCP and fifteen indices, while the second included crude oil prices in addition to the indices. The SLR examined the direct relationship between crude oil prices and DCP. Results indicate that the Call Loan interest rate consistently has a positive effect on DCP, while the Telecommunications Services index exerts a negative impact across all models. The Health Care index positively influenced DCP during the COVID-19 pandemic but negatively during the 2008 GFC. Crude oil prices showed a positive relationship with DCP in SLR models, but their effect was moderated in MLR analyses. The findings reveal that sectoral indices, loan interest rates, and crude oil prices play a crucial role in shaping the resilience of financial institutions during crises, and firms should integrate these indicators into their portfolio management and risk reduction strategies.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".