- CFA Level 2: Corporate Finance Part 1 – Introduction
- Introduction to Capital Structure and Leverage
- Introductory Capital Budgeting Remarks
- Expansion Projects vs. Replacement Projects and Cash Flows
- Impacts of Depreciation Method Choice on Capital Budget Analysis
- Inflation and Capital Budgeting
- Mutually Exclusive Capital Projects with Unequal Lives
- Equivalent Annual Annuity (EAA) Approach
- Least Common Multiple of Lives Approach
- Stand Alone Risk and Capital Projects
- CAPM and a Capital Project’s Discount Rate
- Capital Projects and Real Options
- Common Pitfalls in Capital Budgeting
- Capital Budgeting Alternatives to NPV and IRR Analysis
- Modigliani-Miller and Capital Structure Theory
- Evaluating Capital Structure Policy
- International Differences in Financial Leverage
- Dividend and Share Repurchase Policies
- Factors Affecting Corporate Dividend Policy Decisions
- Signals from Dividend Policies
Modigliani-Miller and Capital Structure Theory
Modigliani and Miller (MM) are great academics in economics and finance who broadly studied the impact of capital structure on a company’s value.
MM Proposition 1 without Taxes: Capital Structure Irrelevance
Under Prop 1, MM theorized that in a tax free environment, with perfect information and no costs for financial distress, capital structure is irrelevant and changing a firm’s capital structure will not impact the firm’s valuation.
A unique characteristic of Prop 1 is that it assumes that operating earnings are split equally between stock and bondholders; in other words, neither has a higher residual claim on earnings.
MM Proposition 2 without Taxes: Increasing Financial Leverage Increases the Cost of Equity
Under Prop 2, MM assumes that the cost of equity is a linear function of the company’s debt to equity ratio.
A key distinction from the assumptions of Prop 1 is that Prop 2 assumes that debt has a superior claim to earnings than equity, which gives debt a lower cost than equity.
In return for their increased risk, equity holders receive a larger proportion of profit. However, increased equity returns are offset by an increased required return on equity (this is reflected by the cost of equity being a linear function of a company’s debt to equity ratio). Therefore the leveraged firm is valued the same as the unleveraged firm.
MM with Taxes
In most countries, interest is tax deductible.
When MM assumes there are no costs for financial distress, the value of the firm increases as it takes on more debt and under this scenario the firm maximizes value using 100% debt capitalization.
Relaxing MM Assumptions
The scenarios presented by MM are not necessarily reflective of business reality. Additional factors for consideration include:
- Financial Distress: as a firm assumes more debt (i.e. increases its financial leverage), its bankruptcy risk increases. This increased risk should be factored in to any analysis.
- Agency Costs: these are the costs incurred by stockholders to monitor company managers; agency costs are increased when monitoring mechanisms fail and equity value losses are absorbed.
- Asymmetric Information: MM assumes perfect information, but company managers commonly know more about the firm than the investing public. This is asymmetric information. The pecking order theory states that company management prefers to use internal financing (cash on hand, retained earnings) as these sources are not as readily visible to the public as stock and bond offerings, which invite scrutiny. Typically management is biased to debt, unless the equity is considered overvalued.
Static Trade-Off Theory
Outside the MM construct, this theory views capital structure as a decision that balances costs and benefits.
Under static trade-off, the company should continue to capitalize itself with debt until the increased costs associated with financial distress exceed the value of the tax shield.
Data Science in Finance: 9-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)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.