This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Along the way, more people than I ever imagined have found my data of use, and while I still have no desire to be a data service, I have an obligation to be transparent about my dataanalysis processes. Standard Deviation in Equity/Firm Value 2. BookValue Multiples 3. Revenue Multiples 4.
Pros and Cons of Market-Based Methods: Market-based methods offer valuable insights from real market data, but they require careful selection of comparable companies and transactions to ensure accuracy. Liquidation Value: This method assesses the value of the company's assets if they were to be sold off in a liquidation scenario.
For example, I have seen it asserted that a stock that trades at less than bookvalue is cheap or that a stock that trades at more than twenty times EBITDA is expensive. If I save analysts and investors time in their decision making, I consider the time spent on my dataanalysis to have earned its required returns and more.
In this post, I will start with a rationalization of why I do this dataanalysis every year, follow up with a description (geographic and sector) of the overall universe of companies that are in my analysis, list out the variables that I estimate and report, and conclude with a short caveat about 2020 data.
We organize all of the trending information in your field so you don't have to. Join 8,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content