Problems with Financial Leveraging

Apart from equity capital, companies also use debt capital to expand their business. This process of adding debt to the firms funding for the purpose of acquiring more assets is called financial leverage. Generally, having some amount of debt is considered good for the company, as employing only equity can be expensive for the company. By including debt in their capital structure, the firms can substantially reduce their cost of capital.

However, financial leverage brings with it some problems that must be handled carefully by the firm’s management. Debt can be a burden for the company. As long as the company is profitable, it is easy to manage the debt and payoff the required interest. However, in case of a year of loss, it becomes a burden and can eat into the firm’s equity. The debt holders have first lien on the assets of the company. Should any problem arise, they are the first ones to have a claim over them. This has a multiplier effect in losses when compared to other firms who employed lesser debt.

As a company increases financial leverage, its capital structure changes. Taking too much debt increases risk for equity because debt holders have preference over equity holders. The increased equity risk increases the expected rate of return by the equity holders making the equity option more expensive. This also reduces the firm’s valuation.

Improper use of debt can lead to financial distress for the firm, especially if the firm does not have a sufficient equity capital. For example, a firm may use debt to invest in risky assets. In such a case if these risky investments turn into losses, the firm’s equity base may erode leading to a state of insolvency.

A firm while looking at financial leverage should therefore aim for an optimum level of debt that maximizes the shareholder value.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.