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
In my experience, data is malleable, and if there is enough of it at your disposal, you can screen it and selectively choose the data to support whatever viewpoint you want to advance. Rather than indulge in endless debates, where each side provides anecdotal evidence, data can prove to be the tie-breaker.
In my experience, data is malleable, and if there is enough of it at your disposal, you can screen it and selectively choose the data to support whatever viewpoint you want to advance. Rather than indulge in endless debates, where each side provides anecdotal evidence, data can prove to be the tie-breaker.
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.
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. Tax rates 4. Financing Flows 5. Insider, CEO & Institutional holdings 2. Beta & Risk 1.
We organize all of the trending information in your field so you don't have to. Join 39,000+ 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