Can ESG Integration Enhance the Stability of Disruptive Technology Stock Investments? Evidence from Copula-Based Approaches
Bibliographic record
Abstract
This paper provides an investigation into the dependence structure among different disruptive technology sectors driving the Fourth Industrial Revolution and scrutinizes the impact of ESG integration on shaping investments in different tech stock sectors in the presence of ESG consideration, represented by the ESG stock index, versus without specific ESG consideration, represented by the general stock index. The results show that (i) C-vine outperforms R-vine and D-vine when modeling the dependence structure of tech sectors. Intelligent infrastructure is the most crucial sector, with substantial reliance on smart transportation and advanced manufacturing. (ii) ESG integration reduces dependence, especially tail dependence, between tech sectors and the stock market, which benefits the future security sector the most and future communication the least. (iii) ESG integration mitigates risk spillover between tech sectors and the stock market, particularly benefiting final frontiers and intelligent infrastructure. The decrease in downside spillover is more significant compared to upside scenarios. For downside risk, spillover from tech sectors to stock indices is more reduced than the reverse, while the opposite holds for upside risk. These sectoral findings offer insights for market participants in financial market investments, financial regulators in risk management, and listed companies in ESG disclosure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".