Everything You've Been Taught About How to Value a Stock Might Be Wrong

Tracy Alloway (0:09)

Hello, and welcome to another episode of the Odd Lots Podcast. I'm Tracy Alloway.

Joe Weisenthal (0:14)

And I'm Joe Weisenthal.

Tracy Alloway (0:16)

So Joe, we are in the midst of yet another earning season, which means everyone is spending their time examining all the income statements that big companies are publishing, right?

Joe Weisenthal (0:28)

Yeah, it comes four times a year, and it's one of the most exciting times of the year, especially if you're a stock investor, because it's when the companies reveal all of the stuff that they did in the last quarter, their revenue, how much they made, their balance sheet, highlights of the quarter, and it's when you really have a chance to dig in because you have a fresh snapshot of the state of the company.

Tracy Alloway (0:54)

Right, but there's a lot of work that goes into estimating earnings results even before they come out, right? Like, analysts will be tweaking their models ahead of results season and then they'll be tweaking them after. Everyone is sort of digging into the numbers to try to determine how well or how badly a company is doing.

Joe Weisenthal (1:13)

Absolutely, and one of the things that I think a lot of people don't get if they're not really active in markets is that there's no such thing is objectively good or bad earnings because in markets, everything is about relative to expectations. So you can have a company that doubled their earnings and made a billion dollars this year versus a billion more than last year, but if the market was expecting them to make $1.2 billion more than the stock might tumble, conversely, you can have companies that lose a ton of money, but if people were impressed by their revenue growth or people were impressed that they were expected to lose more, the stock might surge. And as such, it's the classic Keynesian beauty contest.

You don't just try to figure out what a company is gonna earn, but you also try to figure out what the crowd thinks the company is gonna earn and how the actual results match up to expectations.

Tracy Alloway (2:08)

Right, and some people say that things have gotten even more complicated in recent years because you have companies that sort of try to talk down expectations before their results, and so inevitably they end up beating already low forecasts. So there's lots of moving parts to this, isn't there? But Joe, what if I told you that digging into earnings is a useless exercise?

Joe Weisenthal (2:31)

If you told me that it was a useless exercise to dig in like this, I would be completely crushed because A, part of my job is to talk about this, and B, one of my first jobs was doing equity research for a small portfolio management company, and that's what I spent hours and hours going through every one of these lines. So please don't tell me.

Now I'm starting to get scared. Why are you hinting that maybe it's all a waste of time?

Tracy Alloway (2:58)

Don't be scared, Joe. What I mean is it might be useless to dig into those earnings statements in a sort of traditional sense. Basically, there are some people out there who think that the way we use current accounting rules or the way that accounting rules have been implemented doesn't really match the realities of our modern economy or our modern business environment.

Joe Weisenthal (3:25)

Okay, so I'm still sort of waiting for the buildup here.

Tracy Alloway (3:28)

You'll be fine, you'll be fine.

Okay, we're gonna talk to Baruch Lev. He's professor of accounting and finance at Stern School of Business and Feng Gu, associate professor of accounting at SUNY Buffalo. They put out a paper which was fantastic on this exact subject, basically arguing that our accounting standards haven't really kept up with big, big changes that have overtaken the economy in recent years. So let's get over to them. Baruch and Feng, welcome to the show.

Baruch Lev (3:59)

Thank you, thank you.

Feng Gu (4:00)

Thank you for having us.

Tracy Alloway (4:01)

So was that intro accurate? Is the sum of your work essentially that accounting methodology hasn't really adjusted to modern realities?

Baruch Lev (4:13)

I would say the intro is very accurate. It's not just that we claim that earnings don't matter, we actually prove it.

In a recent article we published, we show for all companies that even if you had the dream forecasting machine, meaning that you could forecast, you could identify all the companies that will meet or beat consensus analyst forecast next quarter, you're not going to make any money from this. You're used to in the past big money, but no longer, and most people are not aware of the demise of earnings as an indicator of company performance, evaluator of managers capabilities. So we actually prove it both in a recent book that we wrote and in the article. And this is really, I would say, earth-shattering, but it's a fact.