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An empirical study on the financial health of the main steel producing segment in India: application of factor analysis and multiple regression analysis

DECISION volume 40, pages 47–55 (2013)Cite this article

Abstract

The preset study is an attempt to measure the influence of different financial ratios selected from different categories like liquidity, activity, and leverage on the profitability of the selected companies. For the purpose of the study, three main producers of steel in India—Steel Authority of India, Rashtriya Ispat Nigam Limited and Tata Steel Limited—are selected for a period of 20 years. Initially, the factor analysis is conducted on all the 15 selected ratios and on the basis of inter correlation matrix the variables (financial ratios) which have the correlation coefficient less than ±6 are excluded from the study. The factor analysis is again conducted on the remaining variables and two factors are extracted. The extracted factors are named suitably. To validate those factors cluster analysis is conducted. Afterwards, to estimate the impact of selected variables on the profitability multiple regression analysis is carried on and a model is predicted for such purpose.

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Authors and Affiliations

  1. Department of Business Management, University of Calcutta, Kolkata, India

    Shrabanti Pal & Mahua Bhattacharya

Authors
  1. Shrabanti Pal
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  2. Mahua Bhattacharya
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Corresponding author

Correspondence to Shrabanti Pal.

Appendices

Appendix 1

Result of factor analysis, cluster analysis, and multiple regression analysis (See in Table 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15)

Appendix 2

See Table 16

Table 16 List of different financial ratios and their formulas
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Pal, S., Bhattacharya, M. An empirical study on the financial health of the main steel producing segment in India: application of factor analysis and multiple regression analysis. Decision 40, 47–55 (2013). https://doi.org/10.1007/s40622-013-0009-x

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  • DOI: https://doi.org/10.1007/s40622-013-0009-x

Keywords

  • Financial ratios
  • Factor analysis
  • Cluster analysis
  • Multiple regression analysis