Motley Fool Hidden Gems Investing - Expectations Investing Part 1

Episode Date: September 7, 2023

How can you use a company’s stock price to give you a window into the future? (00:21) Bill Barker and Deidre Woollard discuss: - Reasons for skepticism when it comes to AI hype. - How long a leash unprofitable software companies may have. - If being located in SIlicon Valley is still an advantage for tech companies. (18:20)  Asit Sharma and Ricky Mulvey break down the basics of expectations investing and give a framework for applying it to individual companies. Companies discussed: PATH, AI, ASAN, NVDA Host: Deidre Woollard Guests: Ricky Mulvey, Bill Barker, Asit Sharma Producer:Ricky Mulvey Engineers: Dan Boyd, Rick Engdahl Learn more about your ad choices. Visit megaphone.fm/adchoices

Stop wasting your nights on a mattress that doesn't get you. Experience the most comfortable mattress in the world. The Sleep Number Smart Bed. At the touch of a button, you can personalize your comfort. Choose firmer or softer. Adjust cooler to warmer. And right now, save up to $2,500 during our massive Labor Day event. Hurry into your local Sleep Number store today.

Because we have your number. How long will we have to wait for business-facing AI to be profitable? Motley Fool Money starts now. Welcome to Motley Fool Money. I'm Deidre Willard here with Bill Barker. Today, we're going to look at some recent results through the window of everyone's kind of favorite subject, AI. How are you doing today, Bill? I'm well, thanks. Well, Bill, we've talked a lot about AI on this show. It's the story of the year. We've got massive profits, massive stock increases. I've been kind of doing a Tech Tuesday, Tech Thursday,

but this week it's kind of different because I could talk a little bit about some unprofitable tech. We've had three companies report recently that I would say they have a valid claim to an AI use case. You've got Asana, C3 AI, and UiPath. So, none of those are exactly household names, right? They're all in the business of selling AI services to other companies. But my concern is, and I'm really eager to get your take on this, I've talked with some analysts about the potential for overspend on both sides, because you've got buyers out there, companies, they feel like they need to spend everything on AI to be kind of like with it. And then on the other side, you've got the companies that feel like they have to offer something or anything AI to be part

of the conversation. Is this a recipe for an AI bubble, an AI disaster? What do you think? I guess I would go back to something you said initially, that there have been some massive profits. There have been relatively few companies that have realized massive profits from AI. There are plenty of stocks that have done massively well this year. Certainly, Nvidia is pocketing real money by providing the picks and shovels of all the AI work that's going on. Where do you have the recipe for a poppable bubble? I think, when the actual profitability becomes completely untethered from the stock movements. The stocks can anticipate profits and price those into stocks. When the profits don't actually appear, then sooner

or later, the air is going to go out of a bubble. That, I would say, is the recipe. It's early to declare that there are overestimated profits for AI or that those profits are expected to be too soon. But that's where the bubble would pop. Yeah. The sooner or later part, I think, is the part that gets... we don't really know where we are in the cycle at this point. No. We're certainly in the hype cycle. Yes. And there's some reason to be in the hype. In the early days of dot-com, the internet bubble, there was plenty of hype. There was plenty that got realized over time in a lot of ways. But the hype came before the profits.

The companies that ultimately delivered a profitable business model are many times the size that they were back then, but there were many others that never got to profitability. That's really the important part here. I want to start with Asana. This is a work productivity platform. AI and work productivity, that makes a certain sense. They have this thing called the work graph. It's their single point of truth, they call it, for work. They say they can add AI to this as a work wrangler. It puts things together, it makes suggestions.

All of that makes sense. But Asana isn't, at its core, an AI company. If you're an investor in Asana, you've got some AI aspirations here, but this, again, it's a company that's not profitable. How do you factor in the AI points? I would start by discounting the application of the single point of truth, which sounds religious to me. I get it as a sales pitch. It would be great if there were some single point of truth in any aspect of life. I understand why that is a goal for them to develop, but I don't think they'll ever get there on that. I think that investors can look at, all right, the AI here, you've got Asana Intelligence as a small part of Asana as a whole.

I think they're mostly more in the Me Too category of, we've got some AI, then this is an AI company. They've gotten some of the benefits of going along with it. We're in this group, too. But that's more of a stock movement than the actual company's business, which is growing at a healthy clip, but not as healthy as it used to be. That's the law of large numbers. When you're growing the top line at high teens, which is where they are now, it's just a different valuation multiple than where the company had been set years ago. Yeah. Like other tech companies that we've talked about, they're having the same problem of the slowing macro cycle and it's taking longer to close deals. We've seen that

for about a year. But you mentioned something about the we're-in-this-too thing that I want to talk about. You've got a CEO, Dustin Moskovitz. He's got the cred. He's been in Silicon Valley for forever. He's one of the founders of Facebook. He was talking on The Earnings Call about having this advantage when it comes to AI, because he knows all the players, it's in their backyard. Is that really the advantage that he's making it out to be? I don't know. Not having spent the time in Silicon Valley that he spent, I'm going to speculate. I've put a spotlight on the fact that this opinion should be discounted through that light. That's where plenty of the talent that you would want to hire is, but do you need

and is the talent staying there or going around the country to work? I don't know that you have to do your shopping for the best coders in Silicon Valley today the way you did five years ago. But having frequent interactions with intelligent people who are invested and know the field and know where they think it's going has got to be a bit of a competitive advantage, but a competitive advantage against lots and lots of other companies that can pretty much say the same thing, I would think. Yeah, I think that's true. One of the things I've been thinking about from a real estate perspective and just from

an overall perspective. The previous belief was that you had to be in Silicon Valley, and then it seemed to be less that way. The OGs of tech are saying that that's the case now again for AI. I have my skeptical hat on about that. Yeah. If all the ones saying it are the ones in Silicon Valley, then you need to discount the degree to which that must be true. I'm sure that there are others who are outside of Silicon Valley who are saying, no, we're doing great without being subject to Silicon Valley real estate prices and the troubles of living there. We are out finding talent throughout the world. Look, if I wanted to learn more about AI, I would probably find

myself in Silicon Valley talking to people out there. There's something to it, but ultimately, it shows up in the numbers. If it doesn't show up in the numbers, you can keep selling that story, but that's all it is. Asana also, they said this phrase a couple of times, that they want to move upmarket. I always get a little bit worried about upmarket, because I know it's always harder to go upmarket than it is to go downmarket. It feels like they're going to spend a lot of money. They've had some wins on this. They had a major cybersecurity platform they said is switching to Asana. They talked about some other big wins. But starting out small, how hard is it going to be for them to get bigger and get those bigger and bigger

companies? I'm worried that they're going to spend a lot of time, a lot of money trying to capture those big dogs and get them to switch. Well, ultimately, you're right. It's about how much they're spending in pursuit of this. Now, they are having some success. From the last quarter, the number of customers with annual spend above $5,000 was 20,000-plus customers. That grew 15%. Annual spend over $100,000 increased 20%. That did increase a bit faster to $550-something. That's where they're targeting. If they pull it off, bigger customers, especially a large base of big customers, is great for the business. But, for every customer who's spending $100,000, they've got 40 that are spending somewhere above $5,000. I think it makes sense to pursue it.

But if it doesn't, as we've come back to a few times, if it doesn't show up on the bottom line sooner or later, then it's not the right strategy. They need to get to profitability. Yeah. The path to profitability thing is what I wanted to focus on today. AI is just sort of like... I worry that it's a little bit of a smokescreen for people, and that it's sort of like, well, we're going to be more profitable now. And I want to talk a little bit about C3 AI, because I feel like this is one of those stories. They should have that AI advantage. They've gotten, for good reason, a lot of the AI hype. They were doing AI enterprise before a lot of other companies. I mean, come on, their ticker's AI. Then they've got good contracts. They've got

contracts with the Department of Defense. They're working with the major cloud providers. But this profitability question, they talked about in their earnings that they're going to turn from focusing on profitability, on getting to that gap profitability, to investing in generative AI. Does that worry you? It seems like they're pushed off that date quite a bit. It's not a get-out-of-jail-free card. To the extent that managements there or anywhere else think that it is, they'll learn the lesson over time. C3 AI actually has more of a justifiable story that, hey, we're going to focus on all of the potential of AI than many others. that is their game. What it does when you say, as they have, we're pushing off the profitability

that we thought we could hit on whatever an adjusted basis, this quarter, fourth quarter, something like that, and we're no longer going to pursue that, is they're telling you, we're raising the ceiling, but we're also lowering the floor on where things could go. If you're profitable and have a predictable continuation of your profitability, you've got a floor under what can happen with your business and your stock. If you never get there, you may go crashing through the floor. But, they're in the AI game. The ceiling's very high. It makes sense to pursue the ceiling. The market, on one day, took the stock down, whatever it was, 15-ish percent or so.

But, stocks almost tripled this year. They're playing with house money. If they can't get to profitability reasonably quickly on the business side of things, with the stock having virtually tripled, there's a path to a secondary. if they need to raise cash, this is a good market to raise cash through an increased stock price. They don't have a lot of debt. They've got runway. They've got time. It's just a question of, at some point, does everybody get a little impatient with it? Certainly, the drop indicates that there is definitely some impatience. There are a lot of people online. You know, the chatter that happens online after results.

There seems to be a lot of impatience with this one. Yeah, some impatience with this one. It's way up from where it was at the beginning of the year. As I say, there's a little bit of house money to play with there in terms of, well, we can take a 20% hit to the stock and everybody's still going to be happy who's been along for the ride this year. Look, over the long-term, C3 AI is still down 70% or whatever it is from its high back in 2001. But in the recent past, they're in the right spot, having AI as their business, in their name,

as their ticker. They've reaped some of the rewards from that. They haven't turned it into a profitable business as of yet, but I think there's a little bit of a leash for all of the companies that are in the AI space right now. It's incumbent upon them to deliver what they have promised over the next quarter, I think, and not to back further away from the profitability picture. Yeah. There's only so far you can push that question off. It's going to come back up. It's interesting, thinking about earnings and results this season, I'm hearing these two things from business to business companies. The one is, we're more responsible now. We're focused on profitability and we're

cutting costs. We've seen, certainly, the impact of tech layoffs and things like that. But I'm also hearing that we're going to spend a lot on AI. We've got C3.ai, Asana, and UiPath. All of their earnings were like, profitability will come, but don't worry about it. all these companies have been public for three years or less. Does it make sense to give them that pass in order to let them really have that runway? And at what point do you think that runway stops? Is there a point in the cycle when all of a sudden those bills come due? It feels like it happened a little bit with other companies. Medic comes to mind on that. At some point, investors just get impatient, right? Yeah. If you're talking about the three-year

horizon that these companies have been around, going back three years ago, two and a half, three years, there was a complete pass on profitability at that point in time. That's when the market peaked in 2001, especially for the Nasdaq companies. Today, a lot of companies have had to find religion on profitability. But AI is a special use case. It's a bit of a fair fight between the promise of these huge piles of gold that somebody is going to land upon in AI, and maybe many companies, but they all got priced at points this year as if all of them would land upon a pile of gold. They're not all going to. I'm not saying that these three won't, in some respect.

But it's still, I think, a lot of hope in all these companies, and they're not being priced on traditional valuation metrics yet. Yeah, good point. And reasons to be cautious. Yeah. You can play this game focusing on the profits. Now, Nvidia's got profits, But the multiple on those profits is rather eye-opening. Profits that are growing fast, you can come up with a mathematical equation to justify that price, and it's still trading quite close to its all-time high. You can focus simply on companies that are profitable, if you want, and still be exposed to some upside. Certainly, in the case of Nvidia, plenty of upside this year, and the

others that are also engaged in the use of AI at a high level. They're not the small caps that we're talking about here, which have a ceiling which is 3, 4, 5, 6 times the stock price. We know the ceiling is at least that high because they've all been 3, 4, 5 times the price they are right now. So, if a sufficient amount of people get excited as we were excited three years ago in unlimited growth, then maybe they get back to those prices someday. Awesome. Thank you for your time today, Bill. Thank you. You may have heard the phrase expectations investing, but what does it really mean?

Asit Sharma and Ricky Mulvey kick off a two-part series on the topic. So, Asit, we're going to put some growth cases of companies maybe a bit on trial for the listener, and we're going to do that through this framework called expectations investing. We're first going to give the framework, and then we're going to use that with some practical applications with four companies. So, this segment, you're going to hear the intro, and then over the weekend, we'll get to dive in with some of those case studies. First, Asit, can you just provide an introduction to expectations investing to maybe a listener who's never heard of it before? Absolutely, Ricky. First, I beg listeners, lower your expectations.

This is a style of investing that is most associated with Michael Mobison, who's a very famous investor and analyst, theoretician. He wrote a book with Alfred Rappaport, another academic, called Expectations Investing. This is more the fruition, I think, of his life's work in investing, is how to understand how companies should be valued by the individual investor. I'm actually going to read you an excerpt from the second chapter of the book, expectations investing, which is very interesting. Ask you for reflection. I think that'll be a good jumping off point to understand how this works. Here we go. Traditional discounted cash flow analysis requires you to forecast cash flows to estimate a stock's value. Expectations investing

reverses the process. It starts with the stock price, a rich and underutilized source of information and determines the cash flow expectations that justify that price. I think it's interesting because it assumes that the market is a bit efficient, right? Then a lot of stock investors would like to perhaps admit. And I also think that there's a part of my brain that is, you know, I'm kind of lazy. I don't want to add this extra step. I don't even like doing a discounted cash flow model. So you're adding more work for me? What the heck. Totally. I actually think this suits the lazy personality more than a DCF model. But let's talk about what you alighted on. I think

that's so important. This book posits that the stock price is a rich source of information. There's a lot that's reflected in there. Different investors with different tools have all come together in a marketplace. And as a communal exercise, they've assigned a price in the market to a stock. So, theoretically, if there's a lot of good information out there, and there are a lot of knowledgeable people with good tools who are assessing stock price, and they have a balance of supply and demand, that price should represent some very decent cooperative assumptions. And what this book is saying is that, yes, if you build up a traditional idea of cash flows. Try to ascertain what all those future cash flows are worth and then discount

them back to the present value. That's a worthy exercise, and a large part of the investment community does it. But you can also do the opposite. Start with that stock price, work backwards, and ask, okay, what are the assumptions behind this? How long will it take for the cash flows to justify this stock price? What's driving the stock price, the assumptions that everyone is building in. The book takes you back to some really fundamental basics. It identifies three main value drivers, which are easy for even the most novice of investors to understand. Sales growth, the rate of sales growth, is a driver of value. Operating profit, so the margin, margin percentage, how much money you make off of each sales dollar,

drives value. And incremental investment, the rate of investment, how much do you need to invest in fixed assets and working capital to drive that next dollar of sales or next dollar of profits? These simple concepts, if you understand them, can give you an edge in investing. Why? Because Movison and Rappaport also say that at some point, the crowd is going to revise its expectations of a business based on how these value drivers are changing. And you, as the practitioner of expectations investing, can get ahead of that game by studying what's really moving the business and projecting that there's going to be a revision in the market's expectations and therefore a revision in the price. Yeah. I think what's interesting about that,

even if you don't go into any of the frameworks that Mobison describes, is it implores one to start from a neutral position. Don't seek to say, what's this company going to do in the future? But rather, okay, what is baked into the assumptions by the market right now? Then you can make a judgment, perhaps, about what your rate of return is relative to the cost of capital moving forward. I like that because if you take it the other way, so if you do the discounted cash flow model, you're actually put uncomfortably in the other position, which is you are building inputs and assumptions to try to project cash flows out into the future. You become a non-neutral observer in that exercise. Whether you like it or not,

You're making a ton of decisions about what the company will do to build your model up. And that is a worthy exercise, as I said. But in this view of things, you can be a little more imprecise. If you're focused more on what pushes the business, what drives those dollars, and how that impacts how other investors will see the stock price, let's say, a year or two or three from today. To me, again, it's better for people who want to understand why a business should be valued from its resources, how it applies its capital, than the DCF, which, again, you can spend a lot of time on and be very wrong the more inputs you need to build. And we'll get into this. When we talk about some specific companies, we can contrast and compare

are these two ways of looking at life. Okay. Well, one thing that Mobison has said on some podcasts is that investors have to earn the right to use yardsticks like a price-to-earnings multiple or an enterprise value to EBITDA multiple earnings before interest taxes, depreciation, and amortization. How do you think investors earn that right if they want to practice this expectations investing approach? Earning the right to use the multiples is pretty simple. If you simply dig in using the why question, you're on your way to earning that right. And I do agree with him. I have been guilty in the past, earlier in my investing career, of looking at companies in wildly different industries with wildly different balance sheets,

different capital structures, and just assigning one ratio, let's say a forward PE ratio. Take one year's forward earnings, look at the price in relation to that, and assess, is this reasonably valued? Is it expensive? Is it cheap? I've taken old growth industries versus startup-type IPO companies and said to myself, well, this one has such a high PE ratio. It's obviously overvalued. So, I think for most investors, understanding the top of the valuation metric and the bottom is so important. I'll go to my old saw. Ricky, you've heard me talk about this one before. Return on invested capital is posited by lots of investors as being a really simple and grounded and rational way to look at a company. Compare the price to its return on invested capital,

the potential to produce incremental dollars on your investments, I think it's one of the most complicated metrics out there. Understanding why a company has gotten to this point in its invested capital base, or how it's developed that return, what that looks like in the future, isn't as simple as it looks. Just taking that metric and saying, this company has a high ROIC or a low ROIC without context is really hard. Bottom line, when you start putting context around a ratio, you're already earning that right. Never take them in isolation. I think we're seeing this in some cases. Investors in 2023 perhaps become a bit more cranky and impatient than they were maybe before the pandemic. A lot of these growthy

companies may say, don't judge us based on our price-to-earnings multiple or our ability to become profitable, because we're still an early-stage growth company in comparison. And there's now this huge disconnect between the investors that may be saying, you know what? No, the cost of capital is higher, my expectations have changed, and you need to get profitable immediately. Yeah, for sure. That's maybe the obverse case of what I was saying, but it's totally true. Investors look at how you invest your capital. Depending on the interest rate environment, as we've all seen, that has an effect. You're going to require something different out of a company. When the value

of those future dollars decreases because of inflation, interest rates, then you get a little more impatience. That can certainly cut that way as well. Are there any maybe less common metrics that expectations investors like to use in order to get an understanding of a company's value? I think for expectations investing-style investors, it's less about specific metrics and more about just building a very simple spreadsheet. Not too dissimilar, in theory, from a reverse DCF, where you've stacked these components. You look at revenue, you look at the costs that are associated with that revenue, you derive free cash flow, you see

what reinvestment looks like, and then you go to the next year. You're building, year by year, the value of the company, and you're also playing with what's called the price-implied forecast period. Let's break that down. In traditional DCF models, there's something called a forecast period. That's the time that the market expects a company to generate its returns on the incremental capital it invests. You'll see there's always a point in DCF models, five years or 10 years, where the rest is all into perpetuity. Then you lump those cash flows together, discount them back. This is interesting, again. I think that expectations investing is so much geared towards a layperson's idea of how the world works in

investing. My idea, the concept is so much simpler. It's saying, look, all right, there's a price out there for a stock, right? And this company is going to throw off cash flows for many, many years. So, how many years is it going to take for the cash flows to justify the current stock price? When you build a spreadsheet out in expectations investing, more than looking at metrics, that's really your starting point, is to figure out, okay, I'm at seven years here of this company's projected cash flows. When I discount those back for it to justify what I'm paying today, but I also know some other things about this business, I actually think they can get that return faster. They can justify that stock price faster. If I buy the company today,

I've got an edge over competitors. So, this might be the way an expectations investing type personality looks at future cash flows versus maybe pulling a metric for easy use. So, I think that's a good place to stop for the introduction. On Saturday, we're going to have a full show with case studies. We're going to move from General Motors all the way to NVIDIA to see how the expectations investing framework can help investors understand where those companies are currently at. As always, people on the program may have interest in the stocks they talk about, and The Motley Fool may have formal recommendations for or against, so don't buy or sell stocks based solely on what you hear. I'm Deidre Willard. Thank you

for listening. We'll see you tomorrow. Thanks for watching!