Support for P/E Ratio of a Company

We have seen that the P/E ratio is a commonly used price multiple for valuing a company.

However, an analyst needs to analyze the P/E ratio in the larger context of the other financials of the company and need to be able to determine whether its P/E ratio is supported by the other factors.

In terms of fundamental valuation, the P/E ratio can be written as:

P/E ratio=D1EkgP/E \text{ ratio}=\frac{\frac{D_{1}}{E}}{k-g}

Where P0=D1kg\text{Where } P_{0}=\frac{D_{1}}{{k-g}}

Note here that D1/E is the dividend payout ratio, k is the required rate of return, and g is the growth rate.

Looking at this formula, we can say that:

  1. Dividend payout ratio: If a company has a higher dividend payout ratio than the industry average, it supports a higher P/E ratio for the company.
  2. Growth rate: If a company has a higher growth rate than the industry average, it supports a higher P/E ratio for the company.
  3. Required rate of return: A lower required rate of return supports a higher P/E ratio for the company.

We can also infer that if a company has high debt, it indicates a higher required return on equity, which in turn means support for lower P/E ratio.

Using a simple analysis like this, an analyst can see what kind of support is there for P/E ratio.

Related Downloads

Finance Train Premium
Accelerate your finance career with cutting-edge data skills.
Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.
I WANT TO JOIN
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.

Saylient AI Logo

Accelerate your finance career with cutting-edge data skills.

Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.