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The recent spate of global crises, including Covid-19 pandemic, Russia-Ukraine war and the Sri Lankan crisis, have hindered the economic growth of almost all nations. The prevailing economic downturn has forced many organizations to assess their financial future and the possibility of financial distress. Financial distress is detrimental to all types of organizations and it is always advisable to avoid it because it creates a tendency for firms to do things that are not in favor of debt holders and non-financial stakeholders, impairing access to credit and deteriorating stakeholder relationships. The objective of this study is to predict the financial distress of firms by analyzing their liquidity position over a period of time and by assessing their working capital management strategy. The purpose is clear: the sooner the detection, the easier it will be for companies to take various preventive measures. If it is possible to find out the situation well in advance, appropriate action can be taken to reverse the process before it is too late. The present study is an attempt to determine the possibility of financial distress in sample companies by analyzing their performance using various financial and statistical techniques. It was conducted by taking a sample of ten Indian steel companies for the period 2017-18 to 2021-22. The data has been analyzed using various financial ratios related to profitability, liquidity, solvency and Motaal's Liquidity Assessment Test and Spearman's Rank Coefficient of Correlation to find out whether companies are compromising their liquidity to increase profitability. The findings show that among the ten selected companies, NMDC has the best liquidity situation and is least prone to facing financial distress. [ABSTRACT FROM AUTHOR]

Many research studies related to the impact of monetary policy and macroeconomic variables have already been conducted, but studies on combining this with global factors and its shocks are very few. To fill this research gap, the paper tries to find out the combined effect of global factors, macroeconomic variables, and monetary policy on 10-year Indian government bond yield using Structural Vector Autoregression (SVAR) and Autoregressive Distributive Lag (ARDL) model. This paper is designed to analyze the impact of various variables on 10-year Indian government bond yield, in the context of its continuous exposure to global factors like oil price shocks and changes in macroeconomic variables. The empirical findings, based on monthly data relating to the period January 2001 to April 2021, suggest that monetary policy has had a considerable impact on bond yields over a long-term horizon, which appears to be consistent with the prevailing Keynes theory. However, the output has the least impact on bond yield. This may be because the monthly data that is used in the study restricts to use GDP. Hence the Index of Industrial Production (IIP) data is used. Further, inflation shocks increase bond yields and global factors like oil price shocks have detrimental effects on bond yields for a long-time horizon of 24-36 months, whereas an increase in the 10-year US government bond yield results in an increase in 10-year Indian government bond yield. [ABSTRACT FROM AUTHOR]