Non-Linear Dynamics of ESG Integration and Credit Default Swap on Bank Profitability: Evidence from the Bank in Turkiye
Bibliographic record
Abstract
This paper investigates the effect of Environmental, Social and Governance (ESG) scores and Credit Default Swap (CDS) spreads on the profitability of Halkbank, one of the biggest state-owned banks in Türkiye, an emerging economy. To this end, we employ Non-linear Autoregressive Distributed Lag (NARDL) and Markov Switching Regression (MSR) methods, taking into account non-linear market risks, using Halkbank’s quarterly data consisting of 63 observations for the period 2009Q1–2024Q3. Moreover, to prevent multicollinearity, we aggregate banking-specific and macroeconomic indicators into a single composite index using Principal Component Analysis (PCA). Our MSR findings suggest that ESG scores and CDS spreads negatively affect bank profitability and that these effects are particularly pronounced during periods of high market volatility. Similarly, NARDL findings suggest that ESG scores have asymmetric effects on bank performance, with both positive and negative changes in ESG performance having a negative impact on profitability, and moreover, negative changes have a more negative impact on profitability. This means that the bank’s sustainability initiatives may be costly and negatively affect profitability in the short run, but these effects will be more negative if initiatives deteriorate. Our findings emphasize the need for banks to adopt a gradual ESG approach that enables them to increase their capacity without compromising financial stability and for regulatory structures to have a flexible and sophisticated risk management framework capable of rapidly adapting to different market conditions. Therefore, our study provides valuable insights to sector managers and policymakers regarding the financial implications of sustainability approaches.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".