The Impact of Artificial Intelligence on Financial Ratios Indicating Financial Distress: Evidence from NYSE-Listed Companies
Bibliographic record
Abstract
Integrating artificial intelligence (AI) in financial institutions has transformed financial distress prediction by improving risk assessment and operational efficiency. This study analyzes the relationship between AI adoption and key financial and macroeconomic indicators. A generalized linear model (GLM) with a binomial logit function was utilized to evaluate the impact of AI adoption on financial distress and the reverse. The dataset includes 2,000 NYSE-listed firms from 2019 to 2023, obtained from the FMP cloud database. Statistical techniques such as descriptive analysis, correlation analysis, Principal Component Analysis (PCA), and logistic regression with LASSO and Ridge regularization were employed to enhance model accuracy and control for multicollinearity. Findings indicate that AI adoption is negatively correlated with the Debt-to-Equity Ratio (-0.65) and positively correlated with the Current Ratio (0.85) and ROA (0.77), suggesting that AI-adopting firms have stronger financial health. Regression analysis confirms that liquidity, profitability, and market volatility significantly influence AI adoption, while leverage and macroeconomic indicators show weaker predictive power. LASSO regression identifies Stock Market Volatility (0.87) as the strongest predictor of AI adoption. AI adoption is associated with improved financial stability, reinforcing its role in mitigating financial distress. Future studies should explore sectoral differences and incorporate advanced machine learning techniques for predictive modeling.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".