Long-Term Investing Ideas in a Volatile Market
Simon recently spoke with a $35 billion global asset manager about how they're navigating the market volatility. The key takeaways are to think long term, tune out the noise...
Pre-commercial drug developers don't have traditional fundamentals to analyze, but investors can estimate the likelihood of clinical development. 7investing Lead Advisors Simon Erickson checks in with Maxx Chatsko on his new quest to build accurate, risk-adjusted net present value (rNPV) models for drug developers.
July 6, 2021 – By Samantha Bailey
It’s often said that biotech stocks trade on binary events, such as public disclosures of clinical trial data or regulatory approvals. That’s true to some extent, but there’s a bit more nuance for investors to consider.
Pre-commercial drug developers don’t have recurring revenue, earnings, or operating cash flow to interrogate when determining fair value, which forces Wall Street analysts and savvy investors to lean on other valuation tools. Net present value (NPV) and risk-adjusted net present value (rNPV) models can be built to account for the lack of traditional financial fundamentals and the high-risk nature of drug development. Although imperfect, these tools provide a rough, quantitative tool to properly value drug assets.
For example, rNPV models can estimate the probability of success (POS) that a drug candidate in a phase 1 clinical trial will reach the market. That percentage can then be slapped onto future expected cash flows and adjusted for the time value of money to determine what the asset might be worth right now. Of course, no one knows the true POS of any single drug candidate. Wall Street analysts often plug in historical averages for specific therapeutic modalities (ex: monoclonal antibodies or kinase inhibitors) in specific therapeutic areas (ex: oncology or neurology).
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This modeling imperfection and crude estimation is what causes the perception of binary events. When a biotech stock craters or soars on a clinical data readout, it’s often because Wall Street models are being updated all at once. Similarly, when a large drug developer pays a hefty premium to acquire a smaller peer, it’s often because they’re applying a much higher POS to the pipeline being acquired — likely due to a deeper technical understanding of the science involved.
In other words, there’s a tremendous amount of value residing in POS differentials. If investors can build relatively accurate rNPV models that incorporate a technical understanding of the underlying drug candidates, then they should be able to stay one step ahead of Wall Street by discovering undervalued companies sooner and avoiding overvalued drug developers altogether.
7investing Lead Advisor Maxx Chatsko is learning how to build rNPV models and incorporate them into his research frameworks. In this episode of the podcast, 7investing CEO and Lead Advisor Simon Erickson chats with him about how to value biotech stocks, a high level explanation of what metrics go into these models, and how they could potentially be applied to new investing areas such as synthetic biology and industrial biotechnology.
Publicly-traded companies mentioned in this podcast include Alnylam Pharmaceuticals, Intellia Therapeutics, and Johnson & Johnson. 7investing Lead Advisors may have positions in the companies that are mentioned. This interview was originally recorded on July 6th, 2021 and was first published on the same day.
00:54 – 10,000 Foot Level of Determining Valuation in Biotech
03:11 – Explaining Volatility in Biotech Stocks
04:43 – A Deep Dive into the Probability of Success
17:11 – Companies Maxx is Drawn to as an Investor
20:12 – How Should Investors Approach Investing in Biotech?
Simon Erickson 0:00
Hello, everyone, and welcome to this episode of our 7investing podcast. I’m 7investing founder and CEO Simon Erickson, and today we’re going to be talking about drug developers. There’s a term it’s developing out there right now personalized medicine. And there’s a flood of interest in companies that are creating new drugs based on innovative new technologies. But as an investor, how can we actually hope to value these companies that are so early in their lifecycle often before they even have any revenue whatsoever? And to answer questions like those, I’m so glad to be joined by my 7investing colleague, our Lead Advisor, Maxx Chatsko. Maxx, I know that you follow the life sciences industry pretty closely. It’s kind of nice to be chatting with you here on the podcast.
Maxx Chatsko 0:41
Yeah, thanks. I just started digging into all this stuff. But this is a good way to maybe quantify, you know, what is the drug worth? What’s his pipeline worth? And you know, there’s still models, but I think it’ll get you pretty close to a good answer.
Simon Erickson 0:54
Perfect. Well, maybe my first question is that started that 10,000 foot level, right? Because so much of investing, at least in mature industries and mature companies is valuation will rise price of revenue, price of cash flow, price, earnings, whatever it might be. That doesn’t really apply when you’re dealing with a current before pre commercial drug developer. What is this space like? What are the companies in the areas that you look at tend to look like?
Maxx Chatsko 1:20
Yeah, it’s interesting, right? If we have a tech company, you have some revenue. And even if it’s not profitable, you can you know, use discounted cash flow models, or look out years into the future and say, you know, what, this is gonna be worth this much, maybe, even if the premiums come down. So this is like has a good value, right? It’s growing quickly, maybe margins are expanding.
In drug development, we don’t have that right. And pre pre commercial drug developers, I should clarify, they don’t have revenue, they don’t have earnings. I don’t have cash flow, they might have revenue from milestone payments, but it’s not recurring revenue, they don’t have product sales, right, every quarter. So the way that these are, you know, it can be it seems like a black box, right? How is this valued at $3 billion? It has a couple assets in phase one clinical trials, one in phase two, and how do we even arrive at what’s a fair valuation? What if none of it works?
And the way that the industry does this, both drug developers and also Wall Street analysts as they build these net present value models, so each, you know, drug in the pipeline will have a value assigned to it based on the probability that it reaches market, which means it has a probability of generating cash flows in the future. So it’s looks very similar to some of these, you know, discounted cash flow models that investors might be more familiar with, but we just tweaked a little bit with some metrics that are unique and inherent to, to drug development. And, and that’s how it works.
Simon Erickson 2:43
So let’s dig a little bit more into those models, because I think that’s important, you know, as a valuation tool for this space. Quarterly earnings probably are not as important for these kinds of companies as a result of data from their from their clinical trials that they have ongoing. We often times Maxx, these companies can pop, you know, 30% 50%, sometimes even more than 100% and their stock prices in a single day. Why is there so much volatility in these? And how does that relate to these models you’re talking about?
Maxx Chatsko 3:11
Yeah, so you know, earnings are not very important in terms of the quarterly updates, right. And a lot of companies now in the header of the press release, we’ll just say business update, yeah, here’s our Q1 business update, because they know, they don’t know, what are their earnings gonna tell you really nothing. And when they do have data, or maybe a partnership or regulatory event, we do see some of these big pops or sometimes if the data are not good, we can see the stock crater as well. And that leads this perception that you know, trading or I’m sorry, investing in, you know, these stocks is, is binary, right? These are binary events. You know, for the seven years it takes to develop a drug, only five days, right really matter for any single asset. And we had these big spikes or big drops.
That’s partly true. But what really is happening is when there’s when data are released, all of those models on Wall Street are getting updated with that new information. So that’s what causes these to be binary. It’s really when the models are updated, right? And that’s because it’s so challenging to really get a good hold on, you know, what are what’s a fair valuation for some of these companies, especially if they’re new areas, right? I mean, think about like gene editing, we don’t have any historical data for what’s the success rate of a gene editing, or, you know, maybe some new cell therapies, we have no idea. So those can tend to be a little bit more volatile. So one of the most important metrics is probability of success. Do you know that is Simon?
Simon Erickson 4:39
I’m sure I’ve heard it before Maxx, tell me more about this.
Maxx Chatsko 4:43
Yeah. So probability of success is really like the most important metric that goes into these net present value calculations. And from what I’ve found, there’s a couple different flavors. So there’s just straight up net present value calculations. And then there’s also something called the risk adjusted net present value calculations. So we abbreviate that with a lowercase r, and then capital NPV. Those tend to be a little bit more accurate during development itself.
Both of these NPV and our NPV can arrive at the same numbers at the end, but that could be years away. So as investors, it’s it’s maybe good to check in, throughout development. So I would like to use risk adjusted net present value. And the difference is risk adjusted net present value takes into account that an asset can get de risked. So it has a higher probability of success as it advances through each stage of development.
So in the last decade, from 2011, to 2020, a drug that entered phase one clinical trials have a 7.9% chance of reaching the market. So that was the overall probability of success for any drug candidate in the entire industry. Now, you can get very granular with this, and this is how, you know, more accurate models are built. So you can look at specific therapeutic areas. So that number changes drastically, depending on the area, the diseases that you’re treating. So for oncology drugs, only 5.3%, then entered phase one clinical trials reached the market in the last decade. But if you look at hematology, so blood disorders, that number jumps to 23.9%. So in different areas, they’ve different amounts of risk or different likelihoods of success. And sometimes, you know, for oncology, I mean, there’s a lot of smaller drug developers as well, right. So there’s more drugs, so and the number of total drugs being looked at and in that calculation is much larger. And some of those maybe aren’t going to be successful to begin with, you know, smaller drug developers really, under a certain market cap, I think it’s under $300 million, rarely get a cancer drug approved. So they might be, you know, closer to 0%. But you can kind of get the idea.
So with probability of success, we calculate that by looking at the phase to phase transition rates. So for any drug in the industry, we’ll go back to just 10,000 foot view. So we’ll say 100 drugs enter clinical trials, right, phase one. Out of those, only 52, are actually going to make it to phase two clinical trials. So it’s a pretty small number. And then, of those drugs, only 28.9% are actually going to reach phase three. So most drugs actually fail in phase two clinical trials. And this makes sense. So it’s kind of like the valley of death, I guess you would say for drug development. Right? phase one, clinical trials, we’re trying to optimize dosing, we’re getting some early signals about safety and efficacy, not too many big hurdles to jump over.
Phase two, we’re asking deeper questions, more more detailed questions, right, we want some clear signals of efficacy, we really want to get a good handle on safety, there’s more patients involved. And of course, you know, if you’re a drug developers not sure about the success, then it might just have a you know, it makes a go, no go decision, do we want to move this into phase three, because that’s gonna be several more years of development, and a lot more money invested as well. So a lot of assets, don’t make it out of phase two. And then if you make it to phase three, it’s about a coin flip overall, for the whole industry, whether or not it’s actually going to be approved. So anyway, you know, proof of, I’m sorry, probability of success is really the one of the most important metrics that goes into these risk adjusted net present value calculations.
Simon Erickson 8:33
There’s a lot of good insight in that Maxx, I’m gonna double click on a couple of those things you said. First, to kind of frame up everything, the NPV net present value calculations are, since we don’t have current revenues, we’d have the promise of something potentially being commercialized in the future. We think that it might be worth as much in sales, we started the future, we say, if we actually get this commercially approved. This is what it looks like. We discount those back to the present value, and can incorporate kind of our probability of success in there in the middle of how likely is it that we actually get there. But then that current value of the asset summed across all of the assets for the company has in development right now is kind of a decent proxy for what we think the company is worth. Is that a fair assessment of what we’re doing here?
Maxx Chatsko 9:19
Yes, and, you know, again, these are imperfect models. And they’re all estimates, no one actually knows the probability of success for any single drug candidate, we can use historical averages all day long, but some are gonna have much higher, so I’m going to have no chance at all. So you know, combining that with maybe a technical understanding of a specific drug class or therapeutic modality, or what makes a company’s approach, maybe have a little bit more de risked compared to the industry average, that can go a long way.
And again, we’re also using you know, what are the cash flows going to be? So we’re kind of estimating peak sales which can have a large Delta, you know, you could be off or Maybe significantly underestimate as well. And again, drug development takes a very long time, right many years, I think we’re starting to see that kind of accelerate. So we’re seeing drugs be developed in shorter timeframes, I think the rule of thumb used to be about 10 years, we’re seeing that come down quite a bit. And maybe five to seven might be more accurate nowadays. And of course, if you have a really good drug asset, and it gets accelerated approval pathways, or breakthrough therapy designation, maybe you can get to market even sooner. But again, that long timeframe in years affects your discount rate. So you need a much higher discount rate to make up for, you know, how long the wait, and all of the risks involved as well.
So actually, one example, though, of, you know, how we can go wrong, or how these are all models. So again, we said like, when data come out, for a company, you know, that kind of a big effect on the share price one way or the other. That’s because all those models are getting updated in a single day. This is also true for when a drug developer like larger drug developers going out and making an acquisition. It might apply a higher probability of success for a pipeline or pipeline asset that it’s acquiring. And that’s part of where that premium comes from. So it’s using like a higher probability of success than maybe Wall Street was factoring in.
A good example, in 2014, Johnson and Johnson (NYSE: JNJ) went out and acquired Covagen and it had an anti-TNF bispecific antibody. So anti-TNF, what Humira is based on right, so big cash cow is right now the best selling drug in the world. So Johnson and Johnson said, give us some of that. And they applied a probability of success to this asset, it was in phase one clinical trials of 26%. So they thought it had a one in four chance of reaching the market. And everyone else basically had about a 15% probability success tagged on to that. So that is an example of why that premium been applied. Right, Johnson Johnson, in their own internal miles said, Hey, you know what, we feel pretty good about this, or better than everyone else, we’re gonna go gobble this up.
Now, Johnson and Johnson is the largest company in the world, right? So it has more cash to throw around. And it has more data, of course, right? It has a lot of scientists, a lot of really smart people working there. And actually, this drug failed in clinical development. So Johnson and Johnson acknowledged that right instead of one in four chance of reaching the market, but it did fail due to safety reasons, and later stage development. So this is a good example, that you know how imperfect these are. And this is really just used to maybe help out a little quantification to biotech investing. And it’s a good reminder as well, I mean, there’s calculators that anybody can use online. But if you’re going in there and plugging in numbers that are unrealistically high, and as you might have, you know, reason from the probably the success is 7.9%, for any drug in the last decade. You know, even 20% in the wrong area is like unrealistically high, right? So if you’re just slapping in these, these high percentages, and you get a really high number for what the stock might be worth, chances are, you’re probably going to be wrong. So these can be very dangerous in the wrong hands as well. So just a word of caution. If you’re playing around with some of these online calculators, Johnson and Johnson can be wrong. Most of us are gonna be wrong to most of the time, right?
Simon Erickson 13:13
Fair enough. Yes. buyer beware, when you’re using those online calculators. It’s interesting, though, maximum, you mentioned that there kind of the role of Big Pharma and all of this, you know, j&j and the other guys that have a lot of deep pockets that kind of puts a floor maybe on a lot of these, these new drug developers. I know that Aswath Damodaran who was kind of this legendary professor of finance at NYU years ago, was looking at the returns on investment for big pharma companies, for them to just go out and pursue innovative science completely from the ground up, like you said, Those single digit chances of success probability of success in phase one, versus to let those assets go through trials, let the smaller companies develop them on their own, and then go ahead and purchase them in later stages. And his conclusion was for big for Big Pharma, it actually was more advantageous. From a profitability perspective, and for investors returns perspective, to wait to let these companies grow on their own and invite them up when they’re later. And there’s a higher probability of success. And so I think that’s very interesting.
Maxx Chatsko 14:17
Yeah, yeah. So you know, larger companies, there’s like this weird, you know, once, once a drug developer has enough success, it’s generating a lot of revenue, maybe it has a valuation in the 10s of billions of dollars. At a certain point, those drugs are kind of fading, right, they’re losing market share, maybe they’re losing patent protection. And these successful drug developers kind of hit a wall. And they have to start really nailing their development to replace those lost revenues. And it’s tough to grow further beyond a certain level. So you see them they do turn to acquisitions.
And we saw, you know, in the last decade with remember the patent cliff was a big worry, maybe the maybe 10 years ago now. Geez. And so there’s a lot of big acquisitions, right. All the major drug developers are trying to stock their pipelines with new assets, new pipeline technologies. And a lot of those didn’t really work out so well. So, like you said, with that analysis, maybe we can tweet that out or something, maybe a little dated now, but I think the points are still valid. Yeah, it does help to maybe look at later stage assets, because there they are more de risked. And that’s why later stage drug developers, even if they don’t have a drug on the market, tend to have a higher valuation, right, we have a little bit more assurance that they are going to be successful, or at least, you know, they have a higher chance of success.
Simon Erickson 15:35
So on that note, and while I had you here, Maxx, I want to make sure that we talked about the science, the foundational science of all right. I think that one of my things I really enjoyed with me on the 7investing team that you’re so far ahead of so many other investors and analysts out there and really digging into the science and what’s going on.
And so let’s talk about IP, because this industry is built upon patents, like you said, you get two decades of exclusive rights to certain molecule you develop or certain science in your IP war chest. But you also mentioned that kind of there’s a difference between more established technology that’s out there that people are building upon, and it scientists understand verses and stuff that’s really, really cutting edge, right? We talked just about Humira, right? Rheumatoid arthritis, anti inflammatory drugs, pretty well understood, you know, there’s different variations of that. And even add these developing, it’s kind of next wave of replacements for Humira. Now, versus something that is much more cutting edge, right? We’re seeing all of these kind of oncology drugs, these personalized medication codes.
I mean, even Alzheimer’s has been talking about with Biogen (NASDAQ: BIIB) the uncurable disease with a very, very low if not near zero probability of success, at least historically. As an investor, are you more drawn to the more established science? It’s a little bit more understood by the industry with a higher probability of success? Or do you want to go out there and swing for the fences, knowing it’s a less chance of actually getting commercialized? But there’s a huge reward because it’s so competitive and challenging to do? Or is it somewhere in the middle of those, and it’s not a rule of thumb one way or the other?
Maxx Chatsko 17:11
I would say it’s not a rule of thumb. But you are correct in that, you know, as we said, I think a lot of the value of biotech investing where you might beat Wall Street or beat the market, is by getting to those opportunities faster discovering those before Wall Street does, right. Again, that’s still challenging. But in terms of, you know, do I want to play around and give more attention to newer therapeutic modalities versus those that are established, I think it’s a little mix of both.
So something that I’ve drawn to is, I think, you know, genetic medicines are gonna have a higher probability of success across the board. So like RNA interference was kind of the first to get there. And they’re picking now well defined targets, at least as far as going to the liver is considered. And Alnylam (NASDAQ: ALNY) has data that their probability of success for an asset in phase one is 60%. So that’s like, you know, almost 10 times the industry average. So RNAi directed to the liver for a well defined genetic target, there’s just money in the bank.
Now, I actually think this is probably going to be true for other genetic medicines, we just saw with CRISPR, gene editing, of course, right. Intellia (NASDAQ: NTLA) has just crushed it on its, you know, preliminary phase one results, but that demonstrated that they can rationally design something that’s going to go in to the liver, knock out a gene of interest with a, it’s actually one of the same ones that Anylam went after. So that’s a validated genetic target, and knocked it out of the park. Right. So I think that kind of proves that we can probably design similar assets for other genetic targets using.
Simon Erickson 18:48
Was, was that pun intended knocking it out of the park for the knockout. I caught on that Maxx might have been subtle.
Maxx Chatsko 18:54
I’m talking to Matt too much lately, I guess, his dad jokes are wearing off. But yeah, so I think you know, and if you design those well, and they’re very selective, you’re going after a specific mRNA transcript or specific gene, then there shouldn’t really be any side effects, right. So that kind of changes the game a little bit where, you know, traditionally, we design these, you know, small molecule inhibitors and cancer, and it might have activity against a specific target. But it might also act like half a dozen other things. And that’s where side effects come from, you know, you’re targeting something that’s the tumors making, but you’re also accidentally having activity against really important proteins, you know, in someone’s gastrointestinal tract. And that’s why they have like nausea or vomiting or diarrhea, and sometimes they have to discontinue treatment altogether.
So it makes sense. They’re like very well rationally designed genetic medicines, because they’re going so far upstream, right? DNA and RNA that’s as far as you can go, are going to have very high probabilities of success. And I think if we make more selective drugs as well, against any target, right, that’s kind of where it is. That’s they should have higher probability of success. So, you know, I don’t care what the therapeutic modality is but the underlying technology platform, you know, that can de risk drug development sooner design more selective drugs. I think that’s really what it comes down to.
Simon Erickson 20:12
And two more questions for your Maxx. The first is, you know, our audience, obviously, individual investors who might be interested in this space, it sounds like there’s a lot of risks inherent with these types of companies. How would you recommend investors approach investing in biotech, knowing that you have to be comfortable with a lot of these risks as mentioned?
Maxx Chatsko 20:29
I’m sorry, can you say that get your quiet there at the end.
Simon Erickson 20:32
How would you recommend an individual investor approach that that investing in biotech company, knowing that there’s a lot of risks that are on the table?
Maxx Chatsko 20:40
Yeah, so Well, my approach is, well, I guess there’s two approaches, right. So that’s how I explain it, you can have a top down approach. So you just say, Hey, you know, you know, gene therapy, I think is going to be big, I’m going to go buy a basket of gene therapy stocks. And that way, you’re spreading risk across a therapeutic modality. And you know, some of those are gonna succeed, very, very successful, be very successful, some are gonna fail. But hopefully the winners make up for the losers, right?
I do a bottom up approach. So I try to understand an area really thoroughly right at the ground level, I realized scientifically are very boring. Try to understand what are the challenges of a new technology because nothing’s perfect. And then also, what are the opportunities? How can this be used? Where does it make sense? Where does it not make sense? And then I look at the competitive landscape. So I try understand all the companies that are in this space, and then I’ll try to like narrow it down to like, 1,2,3 companies, I’ll read through the SEC filings, you know, see how their commercial strategy stacks up. And sometimes I don’t make any investment or any recommendation. Sometimes there just isn’t anything there. Right. So I don’t force it. But you know, every once in a while you’d like, Oh, this is the company in this specific space. So that’s my bottom up approach.
So yeah, look, inherently it is risky. Even if we have more accurate models in Wall Street, again, probably a success overall, is pretty low. So it’s going to be really hard to avoid the losers and the failures, right? That’s just part of how the space operates. But I mean, this is similar for all investing in any industry. Simon, right. I mean, your winners usually dictate your portfolio’s returns and more than make up for your losers. So there’s ways to constrain risk. And I think my framework over the years has proven that and I’m really happy to dig in, I’m starting to look into these, you know, risk adjusted net present value calculations, a new tool in my toolkit, I think I can increase my success levels even higher, so.
Simon Erickson 22:32
Perfect. And my last question Maxx, well, I have you here is one of those ponds that you’re fishing in, can you give me two developing technologies being used for drug development that you’re really interested in you taking a closer look at
Maxx Chatsko 22:45
Two technologies being used. I really am interested in bispecific antibodies. So traditionally, we have antibodies that go in, they have one target that they are active against. A bispecific antibody can attach to two targets simultaneously. So sometimes it can be used to stimulate the immune system, and also have anti cancer activity as well. So they can attach to a tumor. So they can attach to a tumor in an immune cell, they can bring the immune cell to the tumor.
Now, we’re still figuring all this out. There’s a lot of interest here, but then they can go very wrong. There’s a lot of some when doesn’t work, there’s a lot of side effects. It’s not very good for patients. But we are seeing when they’re rationally designed, they can have a lot of they have similar efficacy and similar safety to some of these cell therapies. Right, like CAR-T or even natural killer cells, they’re a lot easier to manufacture we have a lot of we don’t have to make antibody drugs, right, we easier to make them cell therapies, easier to administer a lot of times. These can be used subcutaneously, where sometimes, you know, you need to go to a have an infusion for a cell therapy is here to go to a special center that can handle that. And right now, it depends again, on on what targets you’re going after. But they can also be dosed multiple times and certain setting. So that’s another potential advantage it has over some of these cell therapies. But cell therapies, I think we just got to engineer those a little bit better. But that would be another area I’m very interested in. There’s a lot of cool companies out there.
I think next gen CAR-T. If we can get around some of those side effects that are worrisome. Natural killer cells look pretty good as well, right? They seem to avoid all of those the three major side effects of CAR-T. And then there’s a lot of newer and different therapeutic modalities and cell therapies using different cells. There’s companies I want to just engineer your cells in your body. So they didn’t want to go into the manufacturer and what does edit them as they are to maybe fix or treat or cure disease. So really interested in how we can affect disease at the cellular level. So bispecific antibodies and a whole basket of cell therapies I’m really interested in.
Simon Erickson 24:56
Sounds like some good stuff to keep an eye on really exciting space here, Maxx. Not only from an investing angle but also in the terms of saving people’s lives and the good that is going to do for society as a whole. I really like your technical approach. I’m really looking forward to learning more about how we value these types of companies. Thanks very much for being on the podcast and this morning.
Maxx Chatsko 25:13
Yeah, thanks love to do an update whenever I see all my models are doing or maybe we’ll we’ll walk through one.
Simon Erickson 25:18
Absolutely part two to come still in the future with Maxx Chatsko. We’re talking about biotech and drug development. If you’d like to hire Maxx to find some really interesting biotech companies and some drug developers out there, you can do so by sign up with 7investing today in fact is this will be published will have less than 36 hours left to lock in at our current rates of only $49 a month or $399 a year. We’re going to be increasing that on July the eighth so take advantage of the opportunity at 7investing.com/subscribe to sign up today get access to all of Maxx’s official recommendations. In fact, you’ll get access to all seven of our monthly official seven recommendation 7investing recommendations each and every month. Thanks very much for joining us on this episode of our 7investing podcasts. We are here to empower you to invest in your future. We are 7investing!
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