Pricing in Oil Market and Using Probit Model for Analysis of Stock Market Effects
Bibliographic record
Abstract
The paper proposes the pricing in the oil market and the impact of oil prices on world stock indices. There is analysis of major trends in the oil market. We develop a model to predict the impact of oil prices on stock market indices for developed economies and Russia. This work propose a probit-model for forecasting capital markets trends using Brent pricing, three-month interest rates of the money market, consumer price index, GDP growth rate as a binary dependent variables. The trends of pricing in different stages of development of the oil market make the on stock indices, as developed countries and Russia. The practical significance of this work lies in the structuring of existing knowledge on the applicability of the probit-models in the context of the Russian economy. The paper also outlines the macroeconomic trends of supply and demand in the oil market and the characteristics of the modeling in the conditions of unstable economic situation in Russia. This work fills a gap in the use and implementation of the probit-models for the Russian economy. We make the forecast of supply and demand in the oil market in the next 1-3 years. We believe that oil prices are not likely to go up. The effect of oil prices on the stock markets is generally asymmetrical, except of the Russian and Canadian stock markets, because Russian and Canadian economies depend on oil export indeed. Keywords: oil price forecasting, stock market returns, probit-model. JEL Classifications: E37, F20, G15.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".