Does Competition Affect Financial Distress of Non-Financial Firms in India: A Comparison Using the Lerner Index and Boone Indicator
Bibliographic record
Abstract
Firms’ financial distress (FD) is a major issue for smooth business activities. Timely recognition of FD should be a prime concern; otherwise, it may cause a nasty bankruptcy situation. The FD issue is paramount to researchers, policymakers, and investors. Several factors, whether they are financial or non-financial, may be responsible for financial distress. Such aspects specific to the firms have been explored. Exogenous factors such as competition can also be responsible for a firm’s FD situation. In view of this, this study proposes to determine competition’s impact on financial distress in the Indian context. BSE 100 (“Bombay Stock Exchange”)-listed non-financial firms (NFFs) in India, over a timeframe of 2016–2020, are incorporated in this study. Panel data econometrics is performed for hypothesis testing. This study is novel in its approach, employing multi-technique analysis for measuring financial distress. FD is measured using Altman Z-scores, BOS, and AC distress scores variants. The Boone index (BI) and Lerner index (LI) are undertaken for the competition assessment of NFFs in India. The findings have contrasting views based on BI and LI; BI is positively connected to Z-scores; however, LI negatively connects to Z-scores. The findings suggest that competition (reverse of BI) positively affects financial distress (reverse of Z-score), while competition (reverse of LI) has an adverse effect on FD. It is also found that competition as BI affects FD non-linearly (inverted U shape connection). This means that competition (or market power) initially increases financial distress (or financial stability), and after a specific limit, it reduces financial distress. It can also be said that market power improves financial soundness to a specific limit, and after that, it starts decreasing financial stability. The study’s findings provide fresh and exciting evidence for the connectivity of competition and financial distress. This situation has noticeable implications for all stakeholders and policymakers concerned with the survival of Indian listed firms. The significant connection of competition with financial distress implies that all stakeholders should consider competition an essential element for a firm’s financial distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".