Behavior and predictive Power of the Different Financial Ratios in case of MSME Failure
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DOI:
https://doi.org/10.69980/bma.v12i1.2582Keywords:
MSMEs, Financial Ratios, Business Failure, Bankruptcy Prediction, Business Development, Financial Distress, Liquidity, Profitability, Leverage, Early Warning SystemsAbstract
The backbone of national economies is made up of Micro, Small, and Medium Enterprises (MSMEs). MSMEs provide income, create jobs, ignite innovations and make crucial contributions to the Gross Domestic Product (GDP). However, MSMEs also face significant challenges. Such challenges include issues with financing, functioning efficiently, also with management. Financial ratio analysis is one of the most famous methods of assessing the viability of a business. Financial ratio analysis summarizes data from accounts, providing necessary information about liquidity, profitability, leverage, effectiveness, and solvency of an enterprise. Consequently, this analysis can send signals to people that something might be wrong with their businesses. This paper presents how different financial ratios function and how successful they are in forecasting the failure of MSMEs. The research is based on traditional bankruptcy predictive theories and contemporary research and studies the functioning of financial ratios several years before failure.. The study examines various types of ratios, such as liquidity, profitability, leverage, activity, cash flow, and financial distress models including Beaver's Univariate, Ohlson's O-Score, Zmijewski's model, and Altman's Z-Score. According to the study, profitability reduces first and is followed by liquidity problems; on the other hand, leverage raises the distress costs. It has also been found out that cash flow ratios and measures of interest coverage perform really well in the later stages of the crisis. Finally, research gaps have been identified as well in the context of MSMEs in developing countries, including India, where characteristics specific to financing of businesses necessitate customized prediction models.
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