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Distressed companies create panic among the investors, and the overall effect comes on the economy and leads to a degraded image and value of the companies. Transparency and disclosure involve disclosing the operational as well as the financial performance and corporate governance practices employed by the firms. A corporation or person is said to be in a financial distress (FD) if they are unable to keep their pledge to make payments on time. The current study seeks to shed light on the effects of Financial Distress (FD) and Transparency and Disclosure (T&D) on the value of non-financial firms operating in India. The study makes use of panel data analysis (PDA). The authors of the study used secondary data of non-financial companies included in the S&P BSE 100 index for five fiscal years, from 2015–2016 to 2019–2020. Altman Zscore for FD measure and Tobin's Q, MCAP, and MTB for the firm's valuation is considered. Our study established that Financial Distress (FD) negatively impacts a firm's valuation because a positive association between Zscore (financial soundness) and a firm's value is found. However, Transparency and Disclosure (T&D) have no significant impact on the firm's valuation. We also find evidence that financial distress significantly impacts the value of firms under the influence of T&D. With the help of information about financial distress provided in our study, companies can analyze and take required steps to minimize the probability of being in a state of insolvency or being bankrupt. Investors can also gain knowledge of the business factors and their effect on a company's valuation so that they can cautiously choose and include healthy companies in their targeted list of companies to invest in. No such study has been conducted till now in any of the developing countries that include finding the impact that (FD) as well as (T&D) have on the value of the firms in the non-financial sector. [ABSTRACT FROM AUTHOR]

Purpose: This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context. Design/methodology/approach: The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019. Findings: The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms. Practical implications: The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India. Social implications: Better bond ratings may mitigate corporate defaults. Originality/value: Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market. [ABSTRACT FROM AUTHOR]

The global economic crisis in 1997 significantly impacted all corporate firms. Measuring valuation is becoming increasingly important in corporate firm analysis. Transparency in disclosures enables a company to meet market expectations while also adhering to regulatory requirements. The study's primary purpose is to measure the impact of transparency and disclosures on the valuation of non-financial firms in India and explore the role of Environmental, social and Governance (ESG) as a moderator variable in determining the firm's value. Panel data regression is the methodology adopted for the data analysis in the study. Panel Data of seventy-six non-financial firms was collected for ten years (2011–2020). Market capitalization is considered as a proxy variable for the valuation. The study results indicate that transparency and disclosures (TD) have a negative and significant influence on the value of the firms. Inferring that a higher degree of TD reduces the firm value. At the same time, the interaction term of TD and ESG show a positive significant association. This finding implies that high ESG reduces the negative impact of high TD on the valuation. [ABSTRACT FROM AUTHOR]