Evaluating the Impact of Geopolitical Risk on the Financial Distress of Indian Hospitality Firms
Bibliographic record
Abstract
The study investigates the effect of geopolitical risk (GPR) on the financial distress of tourism & hospitality firms in India. Using two-step GMM, this study evaluates the impact of GPR, GPR Threat, GPR Action and GPR India on financial distress using Altman score for emerging markets as proxy for financial distress. Further, robustness is checked using Żmijewski score and financial distress ratio as proxies for financial distress. The study is extended by examining the impact of GPR specifically on firm life cycle (age) and firm size and on private and public firms. Our empirical investigation demonstrates that all measures of geopolitical risk increase the chances of financial distress of hospitality firms and our findings are robust to alternative measures of financial distress. By considering GPR as an alternate measure of uncertainty in the hospitality industry, this study contributes to the emerging literature on the factors influencing financial distress of hospitality firms. The study also identifies three accounting measures for proxies of financial distress. Policymakers, regulators and management can pre-empt the impact of uncertain external factors by formulating suitable plans and measures as also for post recovery measures to safeguard firms against bankruptcy. Firms can plan their financing decisions and cash management proactively to reduce financial risk.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".