Analyzing the impact of ESG strategies on the investment attractiveness of financial sector stocks
Bibliographic record
Abstract
Subject. The study investigates the investment attractiveness of shares of leading financial companies from the position of the ESG rating. Objectives. The purpose is to determine the investment prospects of shares of financial sector companies that are leaders in terms of ESG ratings. Methods. The study employs methods of regression analysis and financial modeling. Based on model stock portfolios of financial sector companies of the USA, EU, Great Britain, Canada and Japan, created according to the ESG rating of companies over a 3-year period, I performed a comparative analysis of portfolios’ profitability, as well as volatility (?) and the level of possible losses of the investor in case of materialization of risks (Value at Risk). Results. Two model portfolios demonstrated similar profitability indicators, while the ESG leaders' portfolio returns are characterized by a smaller linear deviation. The beta coefficient of two analyzed stock portfolios is close to 1, the portfolio of shares of ‘ESG leaders’ is characterized by slightly higher volatility and higher Value at Risk. Conclusions. Medium-term investments in financial sector companies with the highest ESG ratings will not bring the investor higher returns with a lower risk of losses. Thus, the hypothesis that the ESG rating of a financial sector company currently determines its medium-term investment attractiveness is not confirmed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".