Table 8 Descriptive Statistics based on A ge of Inventory
Related Figures (13)
The sample firms used in this study came from same industries. Due to the controlled sample volume for steel industry, the research focuses on the private sector industry sector. After some investigation, steel Industry has been singled out for research in the present study. This is definitely the backbone of economic growth in any industrial country. A thick relationship has been found between the level of economic growth and the quantum of steel consumption in developed as well as developing countries. Table 1. Name of Successful Firms The used of a preferred sample of private sector might introduce a potential firm’s success bias (Bhunia, 2009). It is claimed that the potential for success is overstated using this techn: ique. However, it is worried that the bias may or may not be important depending on the usage of the model. If the model is used to rank the firms for the potential success in order to perfo used rm a more detailed analysis, then the bias is not important. However, if the model is to identify investment portfolio selection, then the bias is significant. Table 3 above, disclosed the Kolgomorov Smirnov tests (altered for Lillifors). All the variables are almost normal out of seven variables were tested. Accordingly, we exclude the hypothesis null that all of the financial ratios examined are normally distributed. In order to enhance the normality, data transformation processes (natural Log, Square Root, Square and Inverse. Natural logs and square roots) may be implemented. But in this study, only descriptive statistics and multiple regression analysis were utilized. Table 3. Raw Data of Normality Tests Table 4. Descriptive Statistics based on Current Ratio Table 5. Descriptive Statistics based on Liquid Ratio Table 6. Descriptive Statistics based on A bsolute Liquid Ratio Table 7. Descriptive Statistics based on Short-term Debt-Equity Ratio Table 9. Descriptive Statistics based on Age of Debtors Table 10. Descriptive Statistics based on A ge of Creditors Age of creditors gives an indication of efficiency of the credit an and liquidity position directly depends on this period. Higher longer is the age of creditors as well as better is the managemen the age of creditors shows inefficient and poor payme current liabilities (credit) burden and suffering co nt policy creditors of operating four private sector steel compan d payment policy of the firm the credit payment period the t of liquidity whereas shorter that is accountable to decrease ndition of liquidity position. Age of ies is furn ished in Table 10. Source: Statistical results computed from Annual Reports of the selected enterprises Table 11. Multiple Correlation and Multiple Regression Analysis of Tata Steel Ltd. The stre independen profitabili one unit; level. W statistical th he y ty are give e ROCE n LR i ROCE of very sign signifi the compan ificant. decreased by 94.083 units though the influ Cal However, wh three important in increased by 3.143 units and dicators of | ngth of the relationship between n in Table 11. It was ob increased by 55.506 ncreased by one unit, the nt at 1 per y increased by 40.476 units though cent level. un However, wh en DER increased by on en ce of DER o .066 units in case of AO units in case of AOC respectively which was statistically at 1 per cent level. en ALR increased by o the influence of ALR on e unit, the ROCE of th n ROCE was very significant. A gain, iquidity, AOI, AOD and AOC, increased by one the dependent variable, ROCE and all the t variables taken together and the impact of these independent variables on the served from the above that an increase in CR by its that were statistically significant at ROCE decreased by 230.482 units, 1 per cent which was ne unit, the ROCE was e company unit, ROCE and AOD and decreased by 4.017 Source: Statistical results computed from Annual Reports of the selected enterprises Table 12. Multiple Correlation and Multiple Regression A nalysis of Lloyds Steel Inds Ltd. Table 12 shows the strength of influen iquidi 0.077 the independent vai profitability. It was units that were stati ROCE increased by 88 However, when ALR in company though th increased by one unit, ce of DER ity, AOI, AO e infl observed that creased uence of on by one unit, 5803.831 units also inc increased by one unit, ROCE decreas ALR on ROCE was very significant. 22.861 units also increase the ROCE of the company though th ROCE was very significant. Again, three i D and AOC, relationship between the dependent variable, ROCE and al riables taken together and the impact of these independent variables on th CR increase by one unit; the ROCE decreased by 588.34 stically significant at 1 per cent level. When LR increased by one unit, th 1.221 units, which was statistically significant at 1 per cent level rease the ROCE of th However, when DE mportant indicators o ed by 24.842 units an units in case of AOI and AOD and increased by 0.180 units in case of AOC respec tively which was statistica ly at 1 per cent level. Source: Statistical results computed from Annual Reports of the selected enterprises Table 13. Multiple Correlation and Multiple Regression Analysis of Kalyani Steel Ltd. Source: Statistical results computed from Annual Reports of the selected enterprises Table 14. Multiple Correlation and Multiple Regression Analysis of JSW Steel Ltd. Hypothesis 2 Ho: Solvency position has no impact on Profitability Hj): Solvency position has a significant impact on Profitability.
