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Business failures during the economic recession of 2008‐2010 years were unusually high in the USA. The purpose of this paper is to build a new model to predict business failure, using mostly cash flow statement based measures as predictor variables and discriminant analysis technique.

The authors' data matrix consisted of 100 firms and seven predictor variables. A total of 50 “failed” firms were matched with 50 non‐failed firms according to Standard Industrial Classification (SIC) code and size. Financial statement data for the year prior to failed year were pulled from COMPUSTAT database. Seven predictor variables were selected, namely Operating cash flow divided by current liabilities, Cash flow coverage of interest, Operating cash flow margin, Operating cash flow return on total assets, Earning quality, Quick ratio and Three‐year sales growth. The SPSS‐19 software was used to perform discriminant analysis (DA).

The DA model classified 83.3 percent of original grouped cases correctly. The cross‐validated approach (jackknife or leave‐one‐out method) correctly classified 79.5 percent of cases. The chi‐square test of Wilks' lambda was significant at 0.000 level which means the model as a whole performed very well in predicting business failure.

This study is unique in many respects. First, the sample companies are not industry specific. They come from more than 20 different two‐digit SIC codes, which means the authors' model is very generic in nature. Second, the seven predictor variables (financial ratios) they selected are logically justified; these are not an outcome of step‐wise procedure. Third, most of the predictor variables use operating cash flow information from the cash flow statement. Fourth, all the failed firms in the authors' test sample are from the most recent, 2008‐2010, period.

In a first of its kind, this paper tries to explore the long-run relationship between investors' sentiment and selected industries' returns over the period January 2010 to December 2021.

The paper uses 23 market and macroeconomic proxies to measure investor sentiment. Principal component analysis has been used to create sentiment sub-indices that represent investor sentiment. The autoregressive distributed lag (ARDL) model and other sophisticated econometric techniques such as the unit root test, the cumulative sum (CUSUM) stability test, regression, etc. have been used to achieve the objectives of the study.

The authors find that there is a significant relationship between sentiment sub-indices and industries' returns over the period of study. Market and economic variables, market ratios, advance-decline ratio, high-low index, price-to-book value ratio and liquidity in the economy are some of the significant sub-indices explaining industries' returns.

The study has relevant implications for retail investors, policy-makers and other decision-makers in the Indian stock market. Results are helpful for the investor in improving their decision-making and identifying those sentiment sub-indices and the variables therein that are relevant in explaining the return of a particular industry.

The study contributes to the existing literature by exploring the relationship between sentiment and industries' returns in the Indian stock market and by identifying relevant sentiment sub-indices. Also, the study supports the investors' irrationality, which arises due to a plethora of behavioral biases as enshrined in classical finance.

In recent years, a sizeable amount of research in finance and accounting has been devoted to the issue of bond rating and bond rating changes. A major thrust of these research efforts was to develop and test some prediction‐based models using mainly financial ratios and their trends. This paper tests the ability of statistical decomposition analysis of financial statements to predict bond rating changes. The results show that the decomposition analysis almost does not beat the a priori probability model and is no better than multiple discriminant analysis using simple financial ratios. One important piece of information for participants in debt markets is the assessment of the relative risk associated with a particular bond issue, commonly known as bond ratings. These ratings, however, are not usually fixed for the life of the issues. From time to time, the rating agencies review their ratings of the outstanding bond issues and make changes to these ratings (either upward or downward) when needed. Over the years, researchers have attempted to develop and test some prediction based models in order to predict bond ratings or bond rating changes. These prediction models have employed some variables that are assumed to reflect the rating agency decision‐making activities. Although the rating process is complicated and based mainly on judgmental considerations, Hawkins, Brown and Campbell (1983, p. 95) reported that the academic research strongly suggests that a reliable estimate of a potential bond rating or rating change can be determined by a few key financial ratios. Information theory decomposition measures have received in recent years considerable attention as a potential tool for predicting corporate events, namely corporate bankruptcy (e.g., Lev 1970; Moyer 1977; Walker, Stowe and Moriarity 1979; Booth 1983). The underlying proposition in these studies is that corporate failure, as an event, is expected to be preceded by significant changes in the company's assets and liabilities structure. Although the event of bond rating changes is different from the bankruptcy event in terms of consequences, one can still propose that a bond rating change, as a corporate event, is also expected to be preceded by some significant changes in the company's assets and liabilities structure. Therefore, the decomposition analysis may have a predictive ability in the case of bond rating changes. The purpose of this paper is to empirically test and compare the classification and predictive accuracy of the decomposition analysis with the performance of a multiple discriminant model that uses financial ratios and their trends in the context of bond rating changes.

The global outbreak of COVID-19 and its subsequent varied impacts on different economic activities necessitate to examine its disruptions and impacts on livestock sector in India due to its recently surging potential as an unrivaled alternative to boost farmer’s income.

The studies for review were identified through search in different databases using relevant keywords. Only full text papers written in English language were reviewed. The review was organized and streamlined using Covidence software.

Analysis of the literature reveals adverse effects of COVID-19 on functioning of input and output stages of livestock supply chains. This has resulted in upstream and downstream economic losses that affect livelihoods of the producers.

Scale of unprecedented crisis due to COVID-19 pandemic requires creative policy decisions to make livestock production systems robust, resilient and sustainable. Organized production systems are required to integrate with livestock-tech startups to modernize their supply chains, whereas local supply chains are required to reorient with government’s intervention in terms of developing on-farm production and postproduction processing facilities.

Although there exist some evidence on COVID-19-related impacts on livestock sector of India, but an integrated review of evidence on COVID-19 related disruptions at all the stages (from input supply to marketing) of livestock supply chains was missing.

The purpose of the paper is to conduct a comprehensive bibliometric analysis and systematic review to examine the research landscape of women empowerment through participation in self-help groups (SHGs), identifying the eminent contributors, intellectual communities and future research agenda in the field of SHGs and women empowerment.

The global works of literature related to the theme of SHGs and women empowerment between 1998 and May 6, 2022 were scanned for bibliometric analysis and systematic review. A total of 176 English language documents from the Scopus database were extracted. Bibliometric analysis is conducted using Biblioshiny and VOSviewer software.

This study finds that SHGs are paramount in achieving rural women’s empowerment multidimensionally. Found that India is the most contributing country with 136 documents, and Ranjula Bali Swain and Fan Yang Wallentin are the most cited authors in the research field of SHGs and women empowerment. In addition, the paper proposes a comprehensive conceptual framework to portray rudimentary antecedents of women’s empowerment achieved through participation in SHGs.

This bibliometric analysis, along with a systematic review demonstrating a framework encapsulating the principal dimensions of women empowerment and their indicators, will be helpful to practitioners, government, policymakers and researchers working in the area of SHGs and women empowerment.

This study recognizes numerous significant contributions by eminent scholars and presents a concise review of the literature for novice researchers working in the area of SHGs and women empowerment.