Sell-Side Equity Research with Henry Chien (Vol to Valuation)

On this episode Austin & Chris sit down with Henry Chien to discuss a sell-side analyst’s view on the market, price target anchoring effects, transaction structure on how sell-side gets paid, and break down Henry’s personal mental models on investing.

Henry is a former Wall Street analyst where he started his career in market structures at the TABB Group before transitioning to equity research at BMO Capital Markets. Henry has studied a swath of companies related to business services and education. His work has been featured in countless publications as well as being the author of Better Investment Decisions.

Podcast Transcript

Austin: We just got done talking with Henry Chien on sell-side equity research and his thoughts on the market. I thought it was very informative. I learned a lot about his mental models on approaching companies as well as how soft dollar transactions work and the whole structure with price targets. I thought it was pretty interesting.

Chris: I think a big thing for me is when we bring these guests on, they all have their own way of how they started and how they’re here, and hearing how he started to where he is now, and how he’s developed his framework for his own personal investing, I am taking a lot out of that and going to apply it to even my own strategy, the way he’s developed his framework. I think that was my favorite part.

Austin: I agree. He essentially approaches investing or valuation from a combination of multiples and then these mathematical models. Like in the episode, we will talk about S-curves and how he applied that to MSCI and ESG investing in capturing market share.

Chris: You could tell he’s a very sound thinker. He’s what makes a great analyst because you look at all angles. You have to look at all angles and the companies that he follows in the sector that he was in, that’s a huge difference from what we’re used to as well. Being able to hear how he broke that down and came to the conclusion on his price targets and his mental models and valuations, that’s huge. I think it’s a big value-add.

Austin: Yes, especially from his background, for him to be successful, he needs to boil down so much data and research into a five-page sell-side report with a price tag in. Just having that, you could tell when we were talking to him that he’s very put together in connecting the dots.

Chris: I think that’s what makes a great analyst at the end of the day, being able to condense tons of data into a couple sentences.

Austin: Yes, for sure.

Chris: Get your point across right there and then.

Austin: Yeah, for sure. Let’s get into the episode. We’ll talk with Henry about sell-side equity research. Hope you enjoy.

Speaker 4: All opinions expressed by Austin and Chris or any guests on the podcast are solely their own opinions and should not be relied upon for investment decisions. This podcast is for informational and entertainment purposes only. Austin and Chris or any guests on the podcast may maintain positions in the securities discussed.

[music]

Austin: Welcome back to another episode of Vault Evaluation. We have a very special guest on today, Henry Chien. Henry is a former Wall Street analyst where he started his career in market structures at the TABB Group before transitioning to equity research at BMO Capital Markets. Henry has studied as well other companies related to business services and education services, and his work has been featured in countless publications as well as being the author of Better Investment Decisions. Welcome, Henry. Glad you could join us.

Henry: Thanks, guys. I’m looking forward to talking with you guys. Thanks for having me on.

Austin: Thanks for being here. I think this will be a very interesting episode for our listeners, getting into the background of a sell-side analyst. Tell us a little bit more about your career and how you came into the industry.

Henry: I’ll tell you guys a quick intro. I started randomly. TABB Group was a capital markets research firm, and right out of college. It was an interesting job because it was very high level, right off the bat I was interviewing heads of exchanges, heads of trading firms to study market structure, and basically help other executives understand trends. I was a 20-year-old college kid telling the head of Credit Suisse electronic trading how he’s going to move to electronic trading and bonds. It was pretty fun to get a high-level view of market structure.

Then eventually, I was getting a little bored. I wanted to trade and get involved in the market. I actually tried doing that for a few months, quickly realized I didn’t know exactly what I was doing. I started calling around and I interviewed at a couple shops. I ended up at BMO. I just started out pretty fresh. It was funny, the first month on the job, a stocks down 20%, PM is calling me and it’s like, “Hey, what’s going on?” I’m like, “Expectations?”

[laughter]

Then that was seven years of just following the stocks every day. As you said, there was like a lot of sectors, which was a great background. We did education, followed the boom and bust of that, and moved on to ed tech and a lot of industrial cyclical stuff like waste and staffing as well as like data and software stuff with information services in payroll companies. It was great. That’s how I started. That was seven years of just grinding it out and eventually just got better and better and became more and more senior and started pitching stock calls and talking to press and making recommendations. That was the coolest part.

It was a decade of following markets, and now I’m just putting it all, condensing it into a guide, and trying to help other investors make that journey.

Austin: That’s really interesting. I don’t think I’ve ever met anyone that’s worked in market structures before. I guess you got a glimpse into what the underpinnings of how all of it’s connected from like a system perspective and everything that just goes into that.

Henry: Yes, exactly.

Chris: I was just going to say like talking to all the heads, being a 20-year-old, 21-year-old, you’re like thrown into the water with the sharks right off the bat. That’s a really good way to learn because it forces you to do your job right and learn. You were basically thrown in the water with the sharks and that seems like a good way to learn.

Henry: Pretty much, yes. I guess the good thing is it’s funny, when you get to those executive levels. They’re really just trying to figure out what’s going on a lot of times. It was very high level, like, “What’s the impact of Dodd-Frank on my business?” It’s like really, really high-level stuff.

Chris: Got you.

Henry: It was good to see that high level because right off the bat, from studying market structure, I could see that most trading was essentially high-frequency trading. It was a good wake-up call. A lot of the price action is literally computers. [laughs] A lot of the stuff that I read about people reading the tape, it doesn’t exactly apply to today’s markets in my view.

Austin: That’s really interesting. Chris and I were actually talking about that last night where we’ve read a couple studies on the average daily volume, about 80% to 85% of it is driven by a high-frequency trading or algorithmic trading. Definitely, a shift if you think about under the Buttonwood tree in the early 1900, just going to a brick-and-mortar place to exchange actual shares the whole way to 80% to 85% being driven by mathematical models and catalyst-driven events and things of that nature. It’s definitely changed over the last 100 years.

Henry: For sure, yes.

Austin: You also touched upon that you transitioned from Wall Street to being more of this entrepreneurial spirited, which I think you and Chris share this in common. What was that transition like and what pushed you to go out and help investors understand the dynamics of the industry?

Henry: I was always just curious and interested in random stuff and digging into things. I wanted to create stuff and be creative. The thing is with working at a bank, I wasn’t super comfortable because day to day as an equity research analyst, you direct your own thinking and your research. That’s great but you’re still part of a big giant machine. It’s constraining in terms like if you want to create a new product, or create a new direction, it makes it a little difficult. I appreciated the skills that I learned from communication and learning about stocks. I wanted to just be more self-directed essentially.

Part of when I reflected over I guess the decade of studying markets, there was so much that I had put together and learned, and I thought the first step, I was like, “Okay, I really should just put this all together and just make it accessible and help a person like me 10 years ago, who is studying and trying to figure out the markets to save them a lot of time and show them how it’s done on Wall Street and how professional investors invest, because I think there’s a big disconnect, especially between books and anything, a lot of mainstream media of how they recommend looking at stocks, there’s a big disconnect I think in terms of how people actually do it on Wall Street or hedge funds or mutual funds.

I like it a lot better. It’s a little bit more challenging because I have to set my own priorities and think about product and manage my time. I think it’s more meaning. There’s more uncertainty.

Austin: You get to add your own view into that content that you’re putting out there, which can be very powerful because you could bring something to the table that maybe if you were employed under some corporate umbrella, you might not have that amount of free range to make that happen. I appreciate that a lot.

Henry: Yes, what you’re really doing, you’re really just helping another rich guy’s hedge fund get richer, essentially. [laughs]

Austin: Pretty much.

Henry: It’s not exactly the most meaningful thing. [laughs]

Austin: Yes, that’s a good point. Now you can help the everyday person who was you 10 years ago.

Henry: Exactly. Which I identify more with.

Austin: That’s great.

Henry: That’s my motivation.

Austin: Yes, that’s great. What is it like being a sell-side Equity analyst? What separates a good sell-side analyst from a bad sell-side analyst? Obviously, you’re a good one. What separates you from the rest of the pack?

Henry: I’ll start off with what sell-side analyst is like. It’s essentially, you’re, at an investment bank, you are an information hub. You talk to corporates, you’re talking to CEOs, and CFOs, understanding their business, following their financials, and telling them about what investors think there. They use you as a way to– It’s almost like marketing. You’re there to help transmit the message and to give them feedback, and then obviously you talk to investors, you’re talking to every kind of investor. You got the traders, you got the hedge fund guys, you got the long-onlys. You even talk to advisors on stocks.

There you’re pitching stocks and telling them about the stories of these companies. Usually, if you’re at an investment bank, you also have the banking side too. They are focused on the private companies and you’re keeping them smart on what’s going on with investors, and just helping them stay smart to get deals and get in front of companies. Companies look to research analysts to help them tell the story to investors, the investment community. That’s how it is. I guess it’s abstract. You’re just always just talking, always studying, and always listening. When you’re really good, they call you the axe because you’re just a center of information. I guess this is a trading term.

Austin: You’re like a sponge and you’re sucking in all the information pretty much. My boss used to call me grasshopper and stuff my first three months. He was like, “You are a sponge right now and you are going to soak up all the information, everything I say, everything you read, all the people you talk to, you’re a sponge. Soak it up.”

Henry: I guess sales side analyst’s like your professional sponge.

Austin: Yes. There you go.

Henry: Traders would call themselves experts in stocks and they would be like– The analysts are the Ph. D.s in the stock. What differentiates a good one versus a really great one, first it’s the knowledge, and that’s the baseline. I would say a big thing is– It took me a long time to learn that too. I still have to remind myself. There’s a difference between knowledge and insight. Knowledge is, I covered MSCI, I understand how the index business works. I can tell you what indexes are, how they’re being used, what the business is like, but that doesn’t matter. That’s just information. What’s insightful is I need to tell you what makes MSCI stock move, what’s going to make their business grow.

Whether it’s ETF assets are going to have this X percent impact on their EPS, this is what investors are thinking, this is why expectations are where it’s at in terms of estimates, and here’s the upcoming catalyst. There’s these new businesses and this is going to have a certain amount of impact on earnings. It’s taking knowledge, but you have to translate it into what matters for the stock and have to anticipate what’s going to change in terms of expectations for the stock. The other part is, I guess communication. Hopefully, I’m good at it. It’s also a service job.

People would call in and you need to be able to tell a good story. You’re essentially also a teacher to investors. One sell-side analyst, he had this phrase, “You want to make them think, you want to make them laugh, and you want to make them money.” I think that’s the order. You need to talk about a thesis in 30 seconds because everyone’s just stretched for time. Tell them why MSU has a great stock, what’s the thesis, what’s the push back, and have a view on each of these things. There’s a lot of data. When you combine that insight and then you get some data based off your knowledge, then you can start making stock calls, and then you start pitching it. It’s a constant journey, but that’s essentially– You just keep getting better, better at each of those things.

Austin: That was a great breakdown. You’re basically trying to condense a 50-page report into one page for somebody. What you said with the press for time, I remember my boss, the first time I was giving him a pitch, I went on for way too long and he was like, “You need to be able to do this in 30, 45 seconds.” That’s it. I’m like, “Holy crap. It’s just us two right here.” I didn’t know that. The next time I went to do it, you have to have it boom, boom, boom, done, and then you buy the sock. It’s amazing because you want to have good in-depth research, but then at the same time, PMs want to be able to buy right off the bat in a way. It’s crazy.

Henry: Within this industry, I’ve noticed just from my experiences that if there is one industry where they value their time, it’s when it comes to the money that they’re putting in their investments, and if they feel just for the very second that it’s being wasted, it’s very cutthroat.

Henry: There’s not really any softness in terms of dialogue. Until you develop a relationship, then it’s a little bit more friendly. You got to be quick. Our salespeople were always like that. They were just grilling people all the time. I guess it makes you sharper. It’s good.

Austin: Definitely makes you a better analyst to be able to explain things a lot quicker. From your experience, does each sell-side analyst get to add their own magic when it comes to valuation, or does the firm set its own strict guidelines on which models that you want to use?

Henry: Yes. Hopefully, this is helpful to understand– I think that there is frustration in how price targets are set. I’ll just give you how it works. Valuation, generally speaking, is sector based. That’s just a starting point. Ideally, a good shop and a good analyst, the valuation that they’re using is going to be the same valuation methodology that the investors and corporates are using. In the information services business, it’s an EV/EBIDTA multiple for these companies, and that’s literally how transactions are done, both on the private side and it’s how investors look at it.

It makes sense that you use that multiple. Generally, do each of these analysts have their secret sauce? Most of the time, at least from my experience, it’s because there’s so much churn all the time in terms of new information. Usually, it’s based on some forward earnings and some expected multiple. Usually, I think the multiple is usually a market-based multiple. It’s either a current multiple based off comps, maybe it’s a historical multiple, but that literally didn’t work for the past 10 years because multiple’s just going up.

[laughter]

Then maybe I think if you want to be a little bit more insightful, we have to be careful because if you want to create your own multiple, it has to be founded on something that investors will do as well. If say, the business model is a lot more free cash flow generative, then it becomes quite clear that maybe it could be priced on a free cash flow type of multiple and maybe have a different comp group maybe based on MSCI, lots of free cash flow, it’s very resilient business, very stable. Maybe it should be comped closer to a more resilient software firm as opposed to just general information services. That, I think is where you can add a little bit more insight. Generally, valuations are market valuations.

Austin: That makes a lot of sense. You touched upon a very key thing that Chris and I have talked about in the past where, say for this sector, the market is dominating valuation based upon EV to forward EBIDTA. If you’re projecting intrinsic value based on price, the free cash flow, even if it makes academically more sense or you’re getting to a more accurate intrinsic value, if the market’s not using that in aggregate, then you’re going to be off in what happens in pricing. I think that’s one of the key things is how you said it’s based on sector is you have to match what the majority of investors are doing to see into the same lens that they are.

Henry: It’s important because it’s– If you go with just, “Oh, I’m going to make my own free cash flow, intrinsic valuation”, it’s your just own applied theoretical framework whereas sometimes, it does make sense. A staffing company, because it’s cyclical, its earnings are going to go up and down. It’s going to be an earnings multiple and it’s going to be historical ranges of earnings multiples and that’s how you price it and that’s it’s how the market will price it too. I would definitely, at least from my experience, it’s– The market valuation is not like it gets things maybe wrong I guess, if it’s not pricing in certain developments or information. In terms of how people are actually valuing it, it usually makes sense why it’s a certain multiples being used.

Austin: That makes a lot of sense. Before we transition to the next question. Chris and I did an episode in the past on Michael Mauboussin, who works for Morgan Stanley and also does something on the side called Consilient Observer. He had an article that he published with another person from Morgan Stanley called Dan Callahan and they said everything is a DCF model. At the end of the day, everything, it boils down to how much free cash flow they’re generating, but he referenced in the article a survey that was done on what’s the most common valuation approach and it was priced of forward earnings and EV to forward EBITDA that the majority of respondents were saying in the survey on how they respond. It just goes to show that this multiple approach may actually be the most common use to come up with valuation for a company.

Henry: Yes. I like him a lot. I should study some of his stuff more, but I think I like him a lot. Essentially a multiple is just a shorthand free cash evaluation. It’s just 30 multiples, I assume 30 years, it’s going to keep going like this, or growth is compounded, growth makes that 10 years because it’s compressing it in a way. I don’t think that there’s any knock-on multiples. It’s tricky either way. [chuckles]

Austin: It takes experience. At the end of the day, you need to understand as an investor what is that common range for that sector or that company. Each company has their own personality of sorts when it comes to valuation because embedded in it or very consistent I think from a investor psychology perspective who’s investing in it. There’s similarities and personality of the investor and that gets baked into the company, at least in my belief.

Henry: 100%. I remember talking to an experience value guy and he was happy and doing well, so I was like, “Okay, I probably should listen to this guy because value guys are just going out of business,” at least within that 10 years.

[laughter]

He was happy. I was like, “Wow, you’re the first value dude who’s happy and go-lucky.” I was like, “Whoa.” He talked about their analyst would study a sector for, I don’t know, I think he was saying like maybe three-something years just to see how the sector changes and so then they can get a sense of valuation paradox. I saw for staffing stocks, there was very clearly a recession valuation, then there was a peak cycle valuation, when all investors are starting to get scared of the peak and the cycle ending. There was very clear valuation paradigms I could see in some stocks.

Austin: That makes a lot of sense. In the past, we were like he is a deep value or a value investor that really doesn’t follow sell-side research or price targets. Given that they’re coming out very frequently on the same company and there’s a lot of competition and a lot of reports on different price targets, is there any validity in his thinking as to how can an investor trust price targets if they’re that frequent? Say every quarter on a company and there’s also multiple companies putting out their own price targets? Is he just being biased in a sense?

Henry: I looked at his portfolio and he has three stocks essentially. He has Micron, which I’m curious is his thesis on. He ha a position which I assume is because it’s literally beaten down, it’s like 10 times earning to something like that. [laughs] Some real estate company, which I guess is probably some bottom valuation thing.

When I think about that, I don’t know him fully well, but if, when I looked at his transaction history and his portfolio, it looks like– Maybe he does like two or three bets, maybe every few years.

If you’re just following two or three companies or maybe two or three sectors and big trends, you’re a research- I guess you don’t really need equity research. You don’t need that flow of information, if you know exactly what you’re looking for. It makes sense, for his strategy, a value concentration strategy, I think it makes sense. To address the issue that he’s talking about, the frequency of reports, the frequency of targets, I think that is valid as well because the nature of the sell-side research business, it’s for better or worse, it’s transaction-based just because of how the structure of the industry has evolved.

Most of the fees, or at least the growth of the fees come from hedge funds. These are the long-short guys and those are the guys who are doing long short essentially spreads. They’re going longer stock, they’re going shorter stock within a sector and then they obviously have to get paid within a year. They actually want to know– They need to get it right, that spread within a few quarters, minimum. They will need that one-year price target and they need those frequent updates and they’re literally trading off that or maybe trading against the sell-side, against consensus essentially.

I think it’s tricky. It’s have to use it, if you’re looking for a little bit longer term, I would say you’re looking at how the analyst is coming up with the earnings and how they’re coming with the target. I think that’s the more useful thing. Otherwise, it’s also tricky because the analyst is the product, so sometimes if you’re just looking at the research, you can’t really see unfortunately how they’re actually thinking and how they’re coming up with targets if that makes sense.

Austin: That makes sense. It’s almost as if depending on what type of or what style of investor you are, you may leverage it more or less. Even just the business model that your shop operates under, that high concentration under five holdings maybe with a 10-year holding period is going to look a lot different in terms of the research they leverage from sell-side equity analysts versus say a company that is churn in and out of companies and going long-short strategies where they need that constant information flow to stay up to date on how those transactions get put through.

Henry: Yes. We would get value guys every now and then, but it would just be some sector is just blown out. It’s Apollo education or maybe 2U right now or you can laugh at that or 2U maybe, or at one point it was Stericycle. I think what, those value guys that have their own community, so they’ll hear about it and then they’ll start calling us or calling the equity research analyst just to get a quick update on the story. They do use it, but it’s not as a source of–

Austin: It’s another data point they can leverage.

Henry: Exactly.

Austin: Following up on that, from your experience or from people you’ve known throughout working in sell0side, do you think there’s some anchoring effect going on where say, I come out with a report and the price target is $300 per share for this company, come out with a report a quarter later or six months later. Do you think there’s some anchoring effect that goes on where they don’t want to have that next report be, say over 30% their prior target?

Henry: Hopefully, I wanted to– I’ll give you the mechanics of how that actually works, and hopefully that illustrates what’s going on. Say that stock is 300 bucks, or whatever target’s 300 bucks, say it’s like 250 or whatever. What that’s up, I guess what, like 20%, 25%, or whatever? This analyst likes this company, they think the business model’s great, think they address, the market’s great, it’s going to going to grow, and so forth. Great, like long general, “Buy this stock, it’s a good company for the long term.” It goes from 250 to 300 for whatever reason, it gets hot for hedge funds, everyone piles in on the stock, and then earnings beat or something like that. Then it’s at 300, analysts updates their models. Maybe it’s to update earnings up by 10% or whatever.

I don’t think that valuations have gone up 25%. Now here, what am I going to do? Am I going to downgrade this stock to market perform, and then I got to go out to my clients and salespeople be like, “Hey, I’m downgraded to stock.” I’m going to be like, “Why?” “Well, it was like for valuation,” which are usually just terrible calls. You’re not recommending the stock anymore, but the bank, when you’re at 300 and you want to keep it at 300, the bank’s going to be like, “Well, you need to have a 25% or you need to have at least a 10% to 20% target return to justify an outperformance. They have to raise it. Essentially what’s going on, if that makes sense, is you’re anchored to the market, unfortunately, just out of the restrictions of how you have to make a call all times and you have to map it and target it at all times.

Austin: That makes a lot of sense. That’s very interesting because based upon price action in between reports is really going to dictate the upgrade, downgrade, the neutral, even if everything on the underlying fundamentals of the company stayed the exact same.

Henry: Yes. It can get silly sometimes. I remember sometimes because there’s after-hours tradings, sometimes there would be some pretty serious after-hours trading and then we’d price it off the after-hours. Then compliance or the editors would be like, “You don’t have enough upside because they’re pricing it off” after prior close. There’s all these weird things. It’s tricky. I think that how you get to the target, it’s more important than the target itself.

Austin: That makes a lot of sense. We touched upon this a little bit. Trust is a key component, I would think, in the partnership between sell-side analysts and buy-side counterparts. Aside from the accuracy of what actually goes into the report and coming up with your thesis, what does that relationship-building look like? Maybe in examples of going from outperform to downgrades or staying neutral over multiple periods? How’s that conversation play out?

Henry: In terms of like building trust or–

Austin: Yes.

Henry: I can give you an illustration of say a vote that I got or my team got, or we got with JP Morgan, a big small-cap fund. It started out with– All right, these guys are starting to look at the education sector. Then they’ll call in just for like a quick, “Hey, what do you think about the education sector?” I think part of the appeal is we would have a very extensive annual report or a report that we’d put out annually that had a ton of data, data on demographics, student quality, enrollments every quarter, enrollments every year, and trends and a ton of statistics on market share and of different institutions.

Then I think that was the appeal. It’s like, “Okay, you guys have data. Let me call in and ask what you guys think, and so we can give you an update, we like this stock and this stock, we like this sector, we like childcare, for example, Bright Horizons.” It starts there. Then they’ll come away, they’ll take the information, they’ll start doing their own research, their own reading and then an earnings calls happen. They do a quick call-in, check-in. “Hey, what do you think about the quarter?” Then that’s the– “All right, can you give me a quick update and why is this important in 10 minutes or within 10 minutes of the report” thing.

It’s just like, “Are you in the know of what’s going on with Bright Horizons and childcare trends?” Then as they get deeper, maybe they’ll want to do more actual due diligence. Then you help guide them with the due diligence, like, “Hey, here’s demographic trends of childbearing ages, here’s fees, and tuition ranges and how they’re trending for childcare.

These are the major competitors. These are what we’re hearing for private companies.” I think it’s based off that dialogue. It’s just a lot of calls back and forth and eventually, they trust you that what’s going on and that you’re not dumb and you have an actual opinion. Then I guess then you get to that, where the best point, I guess, for an equity research analyst was like, “Okay, so what do you really think?”

I remember we got to that point where I helped him send data. I’m pretty sure it was to put together a presentation for their investment committee and then calls me up. He’s like, “All right, this is great. Really appreciate it. What does your gut tell you about the stock?”

[laughter]

I was like “Wow”. [laughs] I think that was the peak like, all right, so now I really trust my opinion and I can give him not just the data view, not just the actual what’s going on with the business view, but actually a little bit more of a personal, “I’m not sure about this company just because of x reason.” Maybe I don’t think that it’s the highest quality, but I can’t really point to it. That’s how it goes.

Austin: Is it hard to get to that gut moment where they’re relying almost on like your personal take on the company?

Henry: Yes. If it’s like you’re starting out fresh, it takes a while, yes. It’s a little difficult because there’s other analysts out there, it’s quite competitive. People usually will just go to the person who’s just recognizes the acts or the most in-depth person. Sometimes it’s quite random too. I had couple clients who just– I think they just like me and so just talking to me. Sometimes it’s just touchpoints. It’s just being a friend of them. I don’t even know if- it was more personal relationship-driven, to be honest, rather than actual knowledge or insight. Insight was important, but what drove the– I know one analyst, he was really funny. He was the guy that everyone loved to go out with because he’d be really fun. He’d be drinking and he’d just be smart and he’d just be crazy.

Management teams love going out on the road with him. It’s not his official appeal, but I had drinks at one time, I was like, “Man, how do you do it?” He’s like, “I go out to dinners with wives of my clients.”

[laughter]

That’s weird, [laughter] but it’s interesting in retrospect. I’m like, “That’s kind of weird.” It’s a personal relationship with these clients, with the investors, I think that’s the key., and management teams. His appeal is he’s a really fun guy.

Austin: That definitely helps. Sell-side, they need to get paid as well. I think it’d be beneficial for our listeners if you could just touch upon what soft dollars mean and how that works.

Henry: I’ll get, like quick background or quickly how it works. Equity research used to be banking driven. It used to get paid based off deals. Then Spitzer put in the research rules because everyone was recommending thousand dollars price targets for these tech companies. That put in into that. There was a wall put up between research and banking. Then so shifted to trading, the sales model of brokers. We’ll give you research in exchange for sending us trading flow. It used to be commission driven. That emerged this thing called the broker vote where essentially, to make it more like a process, the buy side would vote based on which analysts or which resources they consumed.

Then that would dictate how much commissions would go in. Then if it happened in Europe and if it’s like no commissions belong to the client, you cannot use it to pay research, you pay for research out of pocket. Now it’s purely a vote. It’s not commissioned-driven, it’s based off a research budget for funds, but now it’s like literally a spreadsheet of each analyst would go in and mark, “I use this analyst, I vote for them, I like this research.” Maybe a quick comment. That’s essentially how research is paid right now. It’s very call-driven at this point.

Austin: That’s very interesting.

Chris: Yes, that’s really interesting. I wasn’t aware of a lot of that, so that was definitely something new that I learned today. Switching gears a bit. What is your personal approach to investing and valuing companies?

Henry: I was quite haphazard in the beginning. I’d read value stuff. I was buying these random companies like FranklinCovie or Barrett, whatever, these microcap companies. I was like, “I got to read the financial statements. I got to be a value investor.” Buy cheap companies, which obviously in retrospect’s like, “That was a terrible decision.” Even how much growth was in some of these extensive stocks and then I even tried some of this technical stuff, like the IBD things, anyway which is like– I was like into Chinese stocks because they were like, “Just look for that cup and handle pattern.”

Anyway, none of those really worked. I even did combinations of the futures because I was reading about Bridgewater and I really liked that. That was helpful to understand how assets worked. That wasn’t that sustainable. What really clicked for me in terms of my development, in terms of how I value and understand companies is I was attracted to the information services sector. That’s like the MSCI, or S&P Global, I think it was just because they just kept growing and they would grow maybe 5-10%, but I was like, “Why do those stocks keep going up 20% a year? They don’t go down.” Then I really just started studying the industry, I was trying to figure out, what it drives valuation here, because there’s not an easy answer. What really clicked in me was like– When I was really racking my brains, “Why don’t you just ask the executives?” I literally just asked the executives, “Okay, how do you value your company? What do you look for?” Then it was like very clear, like, “Okay, we look for like proprietary data, we look for embeddedness, we look for first mover advantage.” That was like three criteria. I was like, “Oh, okay, that makes sense.” Then I could categorize it, look at these different stocks and find, “All right, some of these are proprietary data.” When I did a rough segmentation, I was like, “Okay, this clearly explains why some of these stocks are valued higher.”

So many executives told me, “Investors like this because they’re resilient.” If I can look at a measure of resiliency, which I just made up, but it was like a price to earnings to growth ratio, relative to the S&P, so how relatively valued you are on a peg basis. That also had a range. I was like, “Okay, this makes a lot of sense in terms of history. Let’s just put this into a portfolio.” I was like, “This is still working.” I don’t know, it might be a little abstract, but essentially what I was doing, was just trying to figure out what is valuable in the industry by first looking at, what’s valuable for the companies themselves.

That was quite helpful to just build out a framework of figuring out what’s valuable within an industry. Then I started to apply that to different sectors. It just made things a lot easier I think when you have that framework, like waste companies, landfills sort of thing. Any company with a landfill is going to have a higher valuation. When you think about it, it’s because there’s a limited supply of landfills, and that’s why they have better earnings. Staffing, it’s like are you in the growth, high-skilled areas? That I think has been the evolution, especially with talking to different investors, it’s just figuring out, “Okay, what’s the framework of approaching an industry?”, if that makes sense.

Austin: Yes, that makes a lot of sense. I love that kind of evolution of how you started to where you’re at today. I think I had a very similar approach, read The Intelligent Investor. Was thinking I need to get all of my assumptions correct and discount rates in a DCF model to value a company, and then I realized my performance is lacking, because one, timing, the whole value versus growth dynamic. Two, I’m missing some of that intangible stuff that actually goes into valuation, which brings me to this sub-topic. I don’t know if you’ve ever heard of it, but there’s a ETF, an intangible asset ETF. Ticker is ITAN, and it’s run by Sparkline.

Essentially, what they’re doing now, I think it’s really interesting, is they’re taking price to intangible ways of valuing a company, so they’re doing price to their IP book. How much intellectual property do they have underpinning the company? They’re doing price, the human capital, so they’re scrubbing LinkedIn employees for that company, and they’re doing price to brand equity, price to network effects, and then they’re doing valuation. They’re taking a weighted average of those and based upon who’s best ranked out of all the companies that they look at in their investable universe, that gets baked into the ETF.

It’s really interesting because that stuff won’t pop up in fundamentals, eventually it will, but the direct impact is, it’s hard to quantify. If you look at, say, price to your intellectual property book, how many patents do you have, you start to get very different dynamics of what companies you see as undervalued versus overvalued.

Henry: Yes, that’s cool. That’s pretty interesting. I imagine that’s quite a difficult exercise, but that would ideally be the approach. Especially with tech companies, because all of it is expensed.

Austin: That’s what they’re trying to alleviate is, accounting standards don’t keep up with innovation. These companies have way different structures versus even 20 years ago, and they’re trying to capture that. It’s interesting because it sounds like your evolution of valuation takes an essence away from fundamentals and also looks at the big picture, like how does a company establish first mover in their products and services?

Henry: Yes. If you remember Facebook when they first bought Instagram, everyone was like, “Wow, that’s the stupidest thing ever, a billion dollars”, but it was like that turned out to be like a huge business. It wasn’t priced off anything financial, it clearly priced on something like metrics I think. It must have been metric. I don’t exactly remember what it was, but it was probably users with some engagement that they were saying.

Austin: Yes, for sure. These companies like Facebook, or Instagram, or Google with YouTube, it’s like you think they’re paying a lot when the transaction occurs, and then you’re “Wow, why didn’t we do this?” Like Yahoo kept declining bids over multiple years, and now they’re half of what those bids were 15 years later. It’s an interesting dynamic because as we get more data, there’s more things we got to pay attention to, which transitions to our next thing we wanted to ask you. We talked a little bit in the past about mental models when you’re thinking about the market. Could you go a bit more into detail on what that means to you?

Henry: Yes, sure. I will start the first one is just at a high level, and then I’ll use MSCI as an example just to make it hopefully clear. The first thing if we touched upon, essentially looking at like patterns of an industry. The second thing as part of that is looking at stocks as essentially a reflection of change. When I looked at MSCI, I wasn’t looking at the business as– It’s a good business, but what I was really attracted to it was ETF assets were growing quite a bit. The initial spark of looking at it was maybe this is a good play for ETF growth. Then later as ESG capital, I noticed, everyone was investing in ESG capital, and they had an ESG business, that could be the start of basically an investment thesis.

Just starting with that model change, there’s this view of I think ETFs are going to be dominant, or passive’s going to be dominant. This is also supported by– I talked to a pension fund guy, and he’s like, “I only use indexes because I can easily justify the committee.” That was enough for me. At ESG, we’re all like millennial, Gen Z, it’s very clear that sustainable stuff is very important to next generation. I think the returns are better. Going back to that, how do I actually turn that? Moving on to another model, one of the things that I use there was something called the s-curve, which I thought was neat.

That’s another model for change of how do you estimate change in terms of growth. I looked at the growth of ETF assets, in terms of market share and also growth, and you could start to see that it is accelerating. Like once it started to hit that 10% share mark, and you could see that it’s starting to pick up like an s-curve. I took that data. Essentially, I was like, “Okay, let’s take that curve, and let’s apply that to ESG.” Then I found some data on market share of ESG assets, as a percentage of total assets. When I did some research on how big ESG could be, I discovered that every asset manager in the world has said they’re going to do ESG, so it’s every asset technically. If I apply that ETF growth rate, or growth curve on ESG assets, I can now have a projection of growth for ESG assets.

Then thinking about MSCI’s rule in that and just calling around and realizing that they were the leader, I made a pretty standard assumption that they’ll probably keep their market share. In that way, I could estimate growth for the ESG business. This is a long-winded way, I’ve estimated the change of the ESG business, discovered that it’s going to accelerate growth for MSCI. Then a lot of times you notice with stocks because everyone is looking from a forward perspective, when a revenue or earnings growth accelerates or decelerates, that’s very powerful for the valuation of stock.

I saw that, I could see that it was coming up. Estimates from the sell-side weren’t incorporating this. I was like, “Okay, this is the call now.” Essentially, that was my call in 2020. Obviously, at that point, it was like 250, everyone thought, “Oh, MSCI is so expensive. How can you recommend it being a double essentially”, what I was recommending it for, because it was like its acceleration is going to go out, there’s a ton of free cash flow, I can price it with a free cash flow valuation, and earnings estimates are going to go up, in a nutshell. That makes sense from a mental model perspective. It’s all just based off like pricing change.

Austin: Yes, that’s very interesting. I’ve never heard of an approach like that, with that s-curve, but it can be very powerful because that’s like the start of eventually funneling into the fundamentals of the company is seeing how much capture they can have from passive investments and ESG investing. It’s a very unique approach. I really like that.

Henry: That’s the change part. I can go into the expectations part, which is similar if you want to hear a little bit about that too. That’s another framework I think is useful.

Austin: Yes. Sure.

Chris: Definitely.

Henry: I can use 2U as a warning example, but [chuckles] here’s another thing. Another mental framework is that all that matters, or not all that matters, but what’s very important is just expectations and how investors and analysts are modeling the stock essentially. When 2U came out, they were pitching themselves as a SAS stock. The general SAS model is, you spend a ton and because it’s all expensed on the income statement, the more growth you have, the worse your earnings will look. The reason is that you’re investing and you’re building out your tech infrastructure. Eventually, it’s going to be super profitable because it’s all technology-based.

2U priced or 2U pitched themselves like that. Got tech analysts, “We’re the only education analyst” and it worked. There’s a bunch of tech analysts that bought it. The problem is if you actually think about the model of 2U like an education business, you have to replace your students. That’s one. I don’t know if you can really leverage marketing expense. Two, it’s like every product that they made, every program that they developed was its own CapEx investment. It’s not like you’re getting synergy or leverage from one investment in technology. That was quite early on and that emerged as potentially an issue.

The stock went from 20 and I think it went all the way to 160 because it started to sign up like Harvard and Yale and every investor was like, “This is the most amazing business, the future of education.” Every time there was a deal, stock would move up. It was literally pricing in perfection, future of the industry. In retrospect, it’s obviously a little easier to see, but you can see that there was a big hole in the story. Whether it can be profitable. They have these targets of like, “We’re going to get EBITDA profitable”, and they just never gave a date. It’s big warning sign.

The second they came out with guidance that their margin targets were going to be pushed back, they’re not going to be positive earnings next year, boom, stocks down another 20%, 30%. Then they raised some more capital, another warning sign. I think one quarter later they’re like, “Ah, we have to take our growth rate down a little bit because one of our clients wants to hold back growth.” [laughs] You can see destroys the story because if all your models are based off that leverage, getting that growth, and eventually getting EBITDA free cash flow positive, you have lower margins [chuckles] and you have lower growth, destroy the stock. I could see in from the background, that was the reason, but it was also a big trust issue because some of these long-only investors, the 2U was going on basically saying, “We’re going to be profitable, we don’t need capital”, and then would raise capital the next day. They lost a lot of trust too.

Austin: Yes, it looks bad.

Henry: Just to tie that back, that’s a high-level view of like, “Stocks are reflecting expectations in terms of its future model.” I think that’s been approach where it’s starting to look at– I guess I was trained to do this, but looking at forward estimates and seeing the assumptions incorporated in those estimates is sort of a key.

Austin: Have you ever tried– I’m starting to practice this now more recently. Have you ever tried taking models and flipping them on their heads and putting the average estimates of the company’s growth into the model to see if it matches what the current valuation of the company is? That’s Mauboussin’s expectations investing where you flip a DCF model on its head or a multiples model and you see if today’s price actually reflects those estimates or above or below it.

Henry: Yes. I’ve heard about that. I haven’t actually tried that. Have you tried any of this?

Austin: Yes.

Austin: Actually I’m going to try it out.[laughs] In terms of a DCF model, I’ll usually, occasionally, I will take average estimates on EBITDA growth, take a margin between free cash flow and EBITDA, put that into the model, use what the assumptions on discount radar, and see if price today aligns to what the output of that intrinsic value is. Then I’ll do my own and see. I’ll marry up an average valuation and give myself a margin of safety to assume any inaccuracy in the way the modeling’s performed. I like that way because not only do you get to see what price- we know price today, so we know that as the factor. If we just take estimates that the market is having out there, whether it be from sell-side or people that just follow the company heavily and add in your own, and you find that middle range, a top-down versus bottom-up in valuation, I feel like it gives you more insight because then you get to gauge what the market thinks plus what you think.

Henry: Do you have to have your own multiple or discount rate or years, I guess? You probably have to–

Austin: Yes, you have to– That’s really hard, the timelines where it gets tough because eventually, the majority of valuations on terminal growth, you have to estimate the math multiple ways and then say, “Okay, this is the–“. At the end of the day, you have to choose. This is the best timeline to choose and the best discount rate, but the assumptions on growth, you can pretty much find from what the market has out there and all the research for our company.

Henry: That makes sense. I should try that.

Austin: Chris, I’ll turn it over to you before I do any closing remarks.

Chris: I don’t have anything.

Austin: You’re good? [laughs]

Chris: I’m good. Yes, that was awesome. I have never actually spoke to a sell-side analyst or former sell-side analyst one on one. I’ll probably end up reading your book too just so I could learn more. I learned a ton just doing this podcast. One of the best things that I ever did was quit my job and start doing what I’m doing on my own. I know you’ll be successful. You’re going to have a lot of fun doing things on your own because you’ll be able to do things like this. You’ll be able to talk, talk your book, talk to other people and learn. I just learned a shitton about sell-side analysts and how they do the valuations in between quarterly reports.

I had a feeling about the whole one-year thing and the long-short spread and they want to be profitable in that timeframe, but to actually hear it come from a sell-side analyst, that’s huge. That’s really interesting. That’s something I’m going to try to incorporate into my own trading. I’m a big trader. Option trading, three to four-month swing trades. That’s how I make all my money to make a living. I’m going to try to incorporate some of the things that I learned today into my own strategy. We’re always building and we’re always growing. I’m glad you could bring a huge value add at the table, especially for the podcast.

Henry Chien: Nice. That’s awesome. I’m glad to hear it. I wanted to make it hopefully informative and helpful.

Chris: Yes, it was very informative.

Austin: Yes. I found this very informative and entertaining. It’s good to get that perspective of a sell-side who’s actually boots on the ground and get that perspective of how the underpinnings of research and valued companies and mental models and getting exposure that valuation changes by sector. Just hearing that out loud I think will be very beneficial to our listeners. Before we close, I wanted to give you the opportunity to tell listeners where they can find you, what you’re working on, and things of that nature.

Henry Chien: Yes, sure. Essentially what I’ve been doing is taking that decade worth of learning and thinking and writing everything down and putting it into essentially a guidebook to make it accessible and easy to understand for individual investors. It’s called Better Investment Decisions. You can go to www.betterinvestmentdecisions.com, and it’ll just go to my website or you can just look me up, Henry Chien, and you can learn more. I hope it’s helpful. I really want to just help individual investors do their thing. I really like what you guys are doing in the podcast as well. It’s great to have your focus on getting good content for investors to do it well. I think it’s awesome.

Austin: Thank you. Really appreciate that. Also really appreciate you coming on today. Thank you once again.