Do ESG Factors Prove Significant Predictors of Systematic and Downside Risks in the Russian Market after Controlling for Stock Liquidity?
Bibliographic record
Abstract
This paper reveals the impact of environmental, social, and governance (ESG) scores on systematic and downside risks in the Russian stock market. We analyze the influence of a broad set of ESG factors controlling for stock liquidity, financial indicators of companies, and macroeconomic indicators. The period under consideration is from 2013 to 2021. The methodology of our research is based on regression analysis with multiplicative variables to reveal the changes induced by the COVID-19 pandemic. We obtain several novel results. Social responsibility is one of the most significant non-fundamental factors influencing both systematic and downside risks. The most important environment-related component is the measure of a company’s propensity to environmental innovations. Some dimensions of stock liquidity are also significant. For some factors, such as the COVID-19 pandemic and debt burden, we find an unexpected direction of influence on liquidity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".