Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
ESG Significance in Relation to Corporate Bankruptcy Prediction
This thesis aims to enhance corporate risk assessment through studying ESG (Environmental, Social, and Governance) significance in modeling bankruptcy. Through using artificial intelligence’ natural language processing (NLP), we develop a proxy for ESG scoring based on companies annual reporting ret...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
AUC Knowledge Fountain
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613424083533824 |
|---|---|
| access_status_str | Open Access |
| author | Elkady, Sama |
| author_browse | Elkady, Sama |
| author_facet | Elkady, Sama |
| author_sort | Elkady, Sama |
| collection | Thesis |
| description | This thesis aims to enhance corporate risk assessment through studying ESG (Environmental, Social, and Governance) significance in modeling bankruptcy. Through using artificial intelligence’ natural language processing (NLP), we develop a proxy for ESG scoring based on companies annual reporting retrieved through EDGAR. We integrate the derived ESG score with traditional financial ratios used to calculate Altman’s Z-score (1968) in predicting bankruptcy. Through S&P’s Compustat, we obtain a sample of 108 healthy & bankrupt firms -spanning 14 years of fiscal observations- and match them through time and industry. We use stepwise GLM regression to estimate bankruptcy probability observing that ESG and its interaction terms proved significant in bankruptcy prediction, and that the average marginal effect of ESG is negatively correlated with bankruptcy indicating ESG’s positive impact on firm performance. Our research observes ESG metrics for a balanced sample of healthy and bankrupt firms, which despite restricting sample size protects our study against survivorship bias unlike current literature that considers large datasets without factoring ESG for failed firms. We recommend future research to increase bankrupt firms' data points, consider their ESG metrics, and account for the impact of economic downturns to further understand ESG’s trajectory on firm performance. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-3395 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:55.364Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-3395 ESG Significance in Relation to Corporate Bankruptcy Prediction Elkady, Sama This thesis aims to enhance corporate risk assessment through studying ESG (Environmental, Social, and Governance) significance in modeling bankruptcy. Through using artificial intelligence’ natural language processing (NLP), we develop a proxy for ESG scoring based on companies annual reporting retrieved through EDGAR. We integrate the derived ESG score with traditional financial ratios used to calculate Altman’s Z-score (1968) in predicting bankruptcy. Through S&P’s Compustat, we obtain a sample of 108 healthy & bankrupt firms -spanning 14 years of fiscal observations- and match them through time and industry. We use stepwise GLM regression to estimate bankruptcy probability observing that ESG and its interaction terms proved significant in bankruptcy prediction, and that the average marginal effect of ESG is negatively correlated with bankruptcy indicating ESG’s positive impact on firm performance. Our research observes ESG metrics for a balanced sample of healthy and bankrupt firms, which despite restricting sample size protects our study against survivorship bias unlike current literature that considers large datasets without factoring ESG for failed firms. We recommend future research to increase bankrupt firms' data points, consider their ESG metrics, and account for the impact of economic downturns to further understand ESG’s trajectory on firm performance. 2025-01-31T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/2356 https://fount.aucegypt.edu/context/etds/article/3395/viewcontent/sama_youssef_elkady_thesis.pdf Theses and Dissertations AUC Knowledge Fountain ESG Sustainability Firm Performance Bankruptcy Prediction NLP ANN Finance and Financial Management |
| spellingShingle | ESG Sustainability Firm Performance Bankruptcy Prediction NLP ANN Finance and Financial Management Elkady, Sama ESG Significance in Relation to Corporate Bankruptcy Prediction |
| title | ESG Significance in Relation to Corporate Bankruptcy Prediction |
| title_full | ESG Significance in Relation to Corporate Bankruptcy Prediction |
| title_fullStr | ESG Significance in Relation to Corporate Bankruptcy Prediction |
| title_full_unstemmed | ESG Significance in Relation to Corporate Bankruptcy Prediction |
| title_short | ESG Significance in Relation to Corporate Bankruptcy Prediction |
| title_sort | esg significance in relation to corporate bankruptcy prediction |
| topic | ESG Sustainability Firm Performance Bankruptcy Prediction NLP ANN Finance and Financial Management |
| url | https://fount.aucegypt.edu/etds/2356 https://fount.aucegypt.edu/context/etds/article/3395/viewcontent/sama_youssef_elkady_thesis.pdf |
| work_keys_str_mv | AT elkadysama esgsignificanceinrelationtocorporatebankruptcyprediction |