Predictive Autoregressive Models of the Russian Stock Market Using Macroeconomic Variables
Bibliographic record
Abstract
This article evaluates the relationship of macroeconomic variables of the domestic market with the stock index on the example of the Moscow exchange and selects forecast specifications based on an integrated auto regression model - the moving average. The methods that have been used are included in integrated auto regression-moving average model with exogenous variables and seasonal component, Box&Jenkins approach, auto-arima in R function, Hyndman & Athanasopoulos approach, and maximum likelihood method. The results demonstrate that the inclusion of external regressors in the one-dimensional ARIMAX model improves its predictive characteristics. Time series of macro-indicators of the domestic market – the consumer price index, the index of the output of goods and services for basic activities are not interrelated with the index of the Moscow exchange, with the exception of the dollar exchange rate. The positive correlation between the Moscow exchange index and macro indicators of the world economy - the S&P stock index, the price of Brent oil, was confirmed. In models with minimal AIC, a rare presence of the MA component was found, which shows that the prevailing dependence of the stock market yield on previous values of the yield (AR component) and thus, better predictability of the yield. It has shown that for stock market forecasting, "manual" selection of the ARIMA model type can give better results (minimum AIC and minimum RMSE) than the built-in auto.arima algorithm in R. It is shown that from a practical point of view, when selecting forecast models, the RMSE criterion is more useful for investors, which measures the standard error of the forecast in points of the stock index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".