Feedback from Chuck Cain about numbers we can believe in:
“Michael: What do fundamental analysts analyze? It doesn’t make sense to base an analysis on data containing errors. Example? H&R Block’s error in allowance for income taxes was funny, but seriously, think about all the ‘restatements’ that I’m sure you’ve seen, some restating several years at a time. It doesn’t make sense to base an analysis on numbers which are estimates. Numbers that are ‘reserves’ are estimates, by definition. Example? Banks can set earnings in quite a wide range by changing this period’s addition to ‘reserves for loan losses’. Yes, I’ve spent a lot of years working inside banks. You wouldn’t believe what gets done to this poor number when the trial balance bottom line doesn’t look good. The IRS won’t let banks use this number; the IRS requires actual loan losses, instead. It doesn’t make sense to base an analysis on numbers which are made up. Example? Worldcom, Enron, enough said. It doesn’t make sense to base an analysis on numbers which have a high margin of error. Example? Most government statistics. Think about all the ‘revisions’ that I’m sure you’ve seen in the financial press. I have a friend who used to work with this kind of information inside the Federal Reserve. Some of his work went to Chairman Greenspan, who may understand error ranges, but how many other people who read these things in press releases really understand the confidence level? How useful would it be for a friend to give you a phone number for a dream date without telling you that he’s only confident that the area code is correct. How do fundamental analysts know which numbers are wrong, are estimates, have large error margins, or are completely bogus? What is fundamental analysis worth if these numbers aren’t screened out? If they are screened out, what’s left?”
What’s left that we can believe in as true? The market price. Thanks Chuck, great piece!