An analysis of the corporate insolvency resolution process as a route for acquisitions in India
Bibliographic record
Abstract
Abstract In India, the Corporate Insolvency Resolution Process (“CIRP”) takes place under the Insolvency and Bankruptcy Code, 2016 (“IBC”). It involves a Resolution Professional inviting resolution plans for the corporate debtor undergoing insolvency. These plans are submitted by various Resolution Applicants and the best resolution plan is approved by the Committee of Creditors and sanctioned by the National Company Law Tribunal. Thus, from an acquisition perspective, the potential acquirer of the stressed asset is required to provide the best bid (in the form of the resolution plan) for the stressed asset which would be able to garner the approval of the Committee of Creditors. The CIRP route has led to successful acquisitions across variegated sectors from steel (Essar Steel) to textiles (Alok Industries) and has become a new and effective tool to undertake acquisitions for prospective acquirers providing a simpler and faster way for acquisition of stressed assets. However, acquisitions through this process are not yet free from their imperfections and there are certain problems, which if addressed, could pave a way for fruitful investments in the Indian economy. This would bolster an investment and acquisition friendly regime in India improving our nation's ranking on the ease of doing business index even further. This article seeks to analyse CIRP as an effective route for acquisitions and identify such problems which are relevant from an acquisition perspective, with the objective and hope that a discussion on these issues can help strengthen and stimulate successful acquisitions and investments in the Indian economy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".