Development of Risk Index and Risk Governance Index: Application in Indian Public Sector Undertakings
Bibliographic record
Abstract
The purpose of the paper is to develop a risk measure in the form of a risk index and a governance index as an indicator of the quality of governance structure. Using the Delphi technique, two indices are developed (risk index and corporate governance index (CGI)); subsequently, using the 10-year (2005–2015) data of top Indian Public Sector Undertakings (PSUs) and diff-GMM regression (to deal with endogeneity), indices have been validated. Though the data set may appear old, it has only been used to test the risk index and analyze the results. Empirical evidence on indices indicates that Indian PSUs have ‘moderate’ risk levels and ample scope for improvement in their governance structure. Further, a positive relation between governance index and returns and negative relation between risk index and returns lend credence to the indices developed in the study. Notably, the governance index appears to be a moderating variable in the relationship between risk and return. It is perhaps the first study to put forth a comprehensive measure of risk to measure risk levels of PSUs and prescribe a measure of the quality of governance structure. While constructing the CGI, certain non-compliances were observed, even in terms of mandatory requirements, such as the proportion of PSUs may take independent directors. The new datasets may further check for compliance and its effect on the results. Such infringements call for stringent penal provisions and better monitoring of PSUs. Further, if the normative frameworks are adhered to as per the study by the Securities and Exchange Board of India (SEBI) and Ministry of Corporate Affairs (MCA), more effective and efficient decisions with lower risks, and hassle-free management resulting in better return on assets and return on equity.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".