Transparency and Disclosure and Financial Distress of Non-Financial Firms in India under Competition: Investors’ Perspective
Bibliographic record
Abstract
Transparency and disclosure (T&D) of information trigger the interest of all stakeholders, including investors in a company. Cognizance of the company’s financial health before investing is very necessary. Disclosure of information in the firm’s financial reports reflects the firm’s financial performance. A firm’s financial health protects investors’ and other stakeholders’ interests and the firm’s long-term sustainability. Owing to the importance of T&D and a firm’s financial health, this paper investigates the impact of T&D on the financial distress (FD) of non-financial firms (NFFs) listed in India. This study examines both linear and nonlinear connectivity of T&D and financial distress (FD). Their association is also investigated in a competitive scenario (under the moderating effect of competition). The panel data analysis is incorporated into the study having 78 NFFs as cross-sectional units with a timeframe from 2016 to 2020. Altman Z-score measures a firm’s FD (higher Z-score means low FD). BOS (Berger, Ofek and Swary) and AC (Almeida and Campello) scores are taken to consider investors’ perspectives of the firm’s FD. The T&D and Lerner indexes are used to assess the level of T&D and competition. The findings reveal that a higher T&D level decreases a firm’s financial stability or increases a firm’s FD. In nonlinear association, it is found that T&D has an inverted U-curved connection with financial stability or U-curved association with FD. It indicates that initially, higher T&D reduces FD, and after a threshold, it increases FD. However, under competition, T&D is not found to be significantly impactful for FD. The study is novel as no previous study has focused on such association under competition and taking investors’ perspective of a firm’s FD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".