Evaluating Capital Structure Policy

Analysts can compare a company’s capital structure to that of its primary competitors.

Analysts can also compare a company’s current capital structure to its historical capital structure. If a company is trending toward higher financial leverage, this may signal future bankruptcy.

Company management may never publicly state its target capital structure, but the analyst knows that the company’s primary capital structure goal should be to minimize its cost of capital. Armed with this understanding, analysts can model scenarios that calculate the impacts to debt and equity valuation, if the perceived ideal capital structure is realized.

Optimal Capital Structure: Theoretically this is the mix of financing that minimizes the firm’s WACC.

Debt Ratings and Capital Structure Policy

  • Companies must pay debt rating agencies, such as Moody’s, to rate the investment quality of their bonds.

  • Debt rated at and above a certain mark is considered investment grade.

  • Debt rated below a certain mark is considered speculative (commonly called high yield or “junk”).

  • As a company’s financial leverage increases (i.e., its ratio of debt to equity rises), rating agencies tend to lower the grade/mark assigned to that company’s debt.

  • Lower debt ratings not only show increased risk to bondholders, but also to stockholders.  Therefore, lower ratings increase the cost of both debt financing and equity financing.

  • Company management is very sensitive to the firm’s debt rating, as a lower rating leads to a higher cost of capital.

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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

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.