Corporate hedging theories and usage of foreign currency loans: a logit model approach

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The present study has attempted to discuss the association between corporate hedging theories and the usage of foreign currency loans by companies listed in India. A total of 349 non-financial companies were selected, and the data for the financial year ending 31st March, 2018 were considered for the analysis. The descriptive statistics indicate that 55% of the sample companies had borrowed funds in foreign currency. The companies were highly levered and maintained adequate short-term assets to honor short-term obligations. A logit model was employed for analyzing the cross-sectional data. The dependent variable being binary (‘0’ for non-user of foreign currency loans and ‘1’ for foreign currency loan user), the study found the variable ‘industry type’ to have a significant association with usage of foreign currency loans. Companies from the manufacturing sector were likely to use foreign currency loans than companies from the services sector. Debt to net worth, export to sales, revenue (log of revenue) were the variables that significantly influenced the likelihood of companies raising foreign currency loans. Interest coverage ratio had a negative influence on the likelihood of companies opting for foreign currency loans. Hosmer and Lemeshow test showed that the model is a good fit indicating 73% accuracy in predicting the users of foreign currency loans as ‘foreign currency loan users’. Theories such as financial distress, size, and extent of international operations explain why companies raise foreign currency loans.

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    • Figure 1. No. of companies with/without foreign currency loans
    • Figure 2. Classification of companies - industry type
    • Table 1. Variables for the study
    • Table 2. Descriptive statistics
    • Table 3. Results of univariate analysis (difference between means of users vs. non-users of foreign currency loans)
    • Table 4. Number of companies with/without foreign currency loans
    • Table 5. Classification of companies – industry type
    • Table 6. Results of logistic regression
    • Table 7. Confusion matrix and statistics
    • Conceptualization
      Mahadevan Sriram
    • Data curation
      Mahadevan Sriram
    • Investigation
      Mahadevan Sriram
    • Project administration
      Mahadevan Sriram
    • Validation
      Mahadevan Sriram
    • Writing – original draft
      Mahadevan Sriram
    • Writing – review & editing
      Mahadevan Sriram
    • Formal Analysis
      Srilakshminarayana Gali
    • Funding acquisition
      Srilakshminarayana Gali
    • Methodology
      Srilakshminarayana Gali
    • Software
      Srilakshminarayana Gali
    • Supervision
      Srilakshminarayana Gali