Last updated on July 27, 2023
With Chris Bemis, X-Cubed Capital
Chris Bemis, co-founder of X-Cubed Capital, joins the podcast to discuss his views on regional bank credit risk, the nascent bubble in AI tech stocks (and AI more generally), problems in commercial real estate, and how he applies his mathematics background to investing.
Content Highlights
- Last September, X-Cubed’s credit risk signals flashed red over regional banks, causing the firm to put on some trades to profit. That opportunity has now run its course and the other side may hold more interest… (1:21);
- AI tech stocks: It’s not 1999 but more like 1995 with the beginning of the seeds of a bubble. The firm is more bullish on mid-cap stocks in general (12:11);
- Background on the guest (15:40);
- The differences between a multi-manager approach to investing and multi-strategy and why the latter has advantages right now (19:46);
- The bearish argument on office space and why he’s bullish homeowners (26:20);
- Making use of mobile phone data and other ‘alternative’ data sources (30:00);
- AI, artificial intelligence, and why it falls short in many investment approaches (33:18);
- Some advice for math students from a mathematics academic (38:37).
More Information on the Guest
- Website: x3cmllc.com
Quick Highlights From our YouTube Channel
Transcript
Nathaniel E. Baker 0:35
Chris Bemis of X Cubed Capital in Minneapolis, Minnesota, that you started with Andrew Redleaf, formerly of Whitebox, and I believe you were formerly of Whitebox as well, right?
Chris Bemis 0:48
I was.
Nathaniel E. Baker 0:49
And we were going to talk about that in a bit. But I want to start here with a view that you have on markets that can maybe has to be viewed as contrarian. And that is your view of regional banks. And we’re not talking that stocks of regional banks. We’re talking credit. But this the premise is the same, which is that you view the risk in this trade, no longer towards default for these regional banks. Is that correct? And if so, tell me how you got there because this goes back a couple of months.
Chris Bemis 1:23
Correct, with the qualifiers that I’ll add at the end?
Nathaniel E. Baker 1:25
Yeah, go ahead.
Chris Bemis 1:26
So we’ve been interested in the space since September of 22. At the time, when we looked at smaller banks in general, we identified that they carried significant interest rate exposure risk, that we felt the market was under appreciating. We put on a trade long big banks where we could really do the deep dive and and hone in on exactly this. This mispricing this interest rate, exposure mispricing. We are long big banks, short small banks through options. At the time, again, slightly contrarian in that it wasn’t part of the general discussion wasn’t part of the gestalt of how we were understanding markets as a collective. That trade was profitable through the end of 22, but really saw its biggest gains, as we moved into March of 23. In Silicon Valley Bank’s failure, we did not have the view that a Silicon Valley Bank would be insolvent. That wasn’t really part of where we thought the trade was. But it worked in our favor at the time. We, after the failure of Silicon Valley Bank, the market did what I generally refer to as started reading the labels of every package off the shelf.
Nathaniel E. Baker 3:00
Ha, yeah.
Chris Bemis 3:01
And fear kind of took over as the what risks may be under the hood. We kept our powder dry, from March to April as the dust settled around analysis on regionals in general. What we found, though, going into May of this year, was that credit was extremely mispriced relative to a triangle of credit, volatility and equity. In particular, in our analysis, a lot of what we ended up looking at is informed by a structural model approach where you see a bond as being shorter put in long some cash. So, and that’s overly simplistic, but I think it’s a good guideline for how to understand credit in general. Prior to the failure of Silicon Valley Bank, we noted that the mispricing between credit and volatility in equity was about $14,000 per million, if you just restricted yourself to fins. After the failure, that number blew out to about $45,000 per million, which you find very attractive. However, we still noted and we’re aware of the potential jump to default risk and a single name. So the attractiveness is not in necessarily one name in particular, I think I think that would strain credulity even maybe even today, but especially back in May. So what our analysis job was to was if you could diversify your exposure in regionals hedged with volatility in equity. There’s a ton of money on the table. And that’s that’s been a trade we put on mid May, has worked out really well within the first few weeks. And we expect that there’s a lot of runway left there as well.
Nathaniel E. Baker 5:12
Very interesting. Can you talk to us a little bit more about the mispricings that you saw? And you’ve noticed some you quoted some numbers there?
Chris Bemis 5:21
Yeah
Nathaniel E. Baker 5:22
yeah. How exactly does that work between vol and credit and equity?
Chris Bemis 5:26
Yeah, so I know this is jumping the gun a little bit. But I’ll give some insight into why I think this way, my background is as a mathematician, and the work I’ve done, the majority of my career is in quantitative modeling. And the modeling that we utilize is bespoke, made internally within within our firm. But its main driver is just relating credit spreads to volatility and equity moves, which is, you know, could be overly simplistic, oftentimes, that’s done just by saying, you know, and a lot of capital structure trades. That’s done by saying, I want to hedge down to a recovery, right, so you take a bond, and these, you hedge out some equity down to 40 cents of recovery or something like that. What we do internally, though, is replicate a bond with options in equity, which there’s some math that says that that should be possible. And we think that and have seen that in terms of p&l, that that is exactly the case. So in this specific example, in relating individual bonds to the equity and the cap structure, and the options that point to that equity, we found that lo and behold, spreads were way too wide relative to what equity markets were pricing, and what the volatility or the options market was pricing. And so in part, our view was on a relative basis, there’s attractiveness in the single names. We also are a very thesis driven fund. We leverage quant a lot, but we’re thesis driven. And the thesis here is that markets tend to overreact. So this path from contrarian for the namesake of your podcast to success with the trade. So knowing that small banks were under pressure to the market is over pricing. The risk in small banks, especially in relative pricing of the options and equity markets, was part of just process in general. But like I said, we still were cautious, of jump to default risk. So while you may find some error, and mispricing between credit and equity and volatility, those who don’t really hold in a jump to default scenario, so the solution there is not, you know, it’s not one that others might not think of, it’s that you want to diversify away that risk. So we felt the best way to express a trade like that, to mitigate that Trump’s default risk was via basket. So taking 10 to 15 names on the regional side in credit, mapping out our expectation in each name, then aggregating that to the basket. And then hedging within the capital structure. On the other side.
Nathaniel E. Baker 8:42
The other side being the large cap banks.
Chris Bemis 8:44
Well, yeah, in this case, we actually, that’s great question. And in this case, we actually did a mix of s&p, because we actually felt that the market in general was was Miss mispriced in this space. And then some of like an exit left or some of the bigger banks as well.
Nathaniel E. Baker 9:06
Interesting. Okay. And so how, how liquid are those credits, and the bank credits that you train how much of an issue is that?
Chris Bemis 9:16
It wasn’t in this case. So, you know, we had no trouble getting on the sizes that we want in cadex aid, you know, very quickly as well. So these were highly liquid really just priced out of uncertainty, as opposed to, you know, true or fair value.
Nathaniel E. Baker 9:37
That’s really interesting. So what I’m curious what kind of signals that you see that made you going back to September, you touched on it?
Chris Bemis 9:45
That’s great. Yeah. I love that. It helps that our founder owns a bank. So not having the ins and outs of the Community Bank which he But is privy to and involved with motivated the analysis of others. So in aggregating our view, the motivation was from experience, which is always a nice place to start a trade.
Nathaniel E. Baker 10:20
That’s really interesting. Andrew, I believe has been on this podcast before, by the way, in the very early days, and I believe he did talk about regional banks, of course, back then regional banks were, you know, okay.
Chris Bemis 10:32
But then that is kind of how these trades, they get the label of contrary intent to go, it seems.
Nathaniel E. Baker 10:44
No, that’s, that’s fascinating. But now, it’s I mean, regional banks. I mean, just at least the vanilla equities have gone. Pretty. They’ve been rallying pretty strongly last couple of weeks. But you still think I mean, the equity aside, you think there’s still upside more upside? In the credit in the credit in the credit specifically? Yes. Which is a little more protected than the equity. Absolutely. Yeah. Okay. So that is there is that caveat that this is? Yeah,
Chris Bemis 11:10
I would be hesitant to endorse the equity portion. Just that trade.
Nathaniel E. Baker 11:18
Okay. It’s just too risk?
Chris Bemis 11:19
Yeah.
Nathaniel E. Baker 11:21
Yeah, no, fair, fair. Fair. Okay. And this, of course, begs the question, if you’re picking up any other signals now, about this kind of mispriced risk? You know, it’s an interesting time here, we have inflation, we have the Fed standing pat, but only for a bit. We have tech stocks, going nuts, AI stocks. You know, I feel like it’s 1999 here for those of you old enough to remember that —
Chris Bemis 11:48
Maybe 95. It’s like the beginning of the seeds of a bubble
Nathaniel E. Baker 11:56
you think?
Chris Bemis 11:58
Yes, but I’ll qualify that with that. Not so much as something I would make a trade on. We actually are more bullish on mid cap, then mega cap and have a handful of equity portfolios that reflect that.
Nathaniel E. Baker 12:17
Hmm, okay. Any particular sectors in mid cap?
Chris Bemis 12:21
No, actually, we think it’s a more broad based approach. So, you know, within within some of the equity portfolios that we run, we’re diversified across names really not trying to identify sector picking. But the names that we find interesting tend to be more in the mid cap space.
Nathaniel E. Baker 12:43
Hmm, that’s fascinating. Okay. Okay, let’s, why don’t we take a quick break here, and I want to come back and you touched on your background a little bit. But I want to ask you some more about that. And more about this firm X Cubed Capital because there’s a strong quantitative element there, which is technically something that’s hard to discuss on a podcast, but we’re gonna get a try. But we’ll be right back. If you’re a premium subscriber, don’t touch the dial, you do not get the break.
Welcome back everybody. Here with Chris Bemis, co founder of X-Cubed Capital out there in Minneapolis. So this is the segment of the podcast where we talk about our guest’s background, how they came to arrive at this at this stage of their career. You touched on it, you have a maths background. So take us back and talk to us how you came about investing in the first place, and how you came to start this firm.
Chris Bemis 13:35
Yeah, so my background is in the in the bonafide mathematician category, have a PhD in applied math, always interested in a financial application in naively when you study a lot of math, you don’t know exactly what that might turn into someday. As a quick aside, at the time, when I was finishing my PhD, the space of Math Finance, which I teach at the university here, presently in was not so codified as to identify, you know, a direct path for somebody with my background. So I took a somewhat circuitous route, did some work for GMAC RFC, you know, wrote a thesis that, you know, as mathematician felt was interesting for finance, and ended up working with Andy directly after my PhD actually slightly before I defended that and did the gamut ransom Long, short equity portfolios, built out a quantitative group within my past firm was ahead of that. And then towards the end of my tenure there, really got in into is in systematic credit, pricing credit, both in the cross section meaning like how is one credit price relative to another, and within its own capital structure? And I particularly like that I think I think it’s really interesting. I think it’s a place you know, it’s one of the things that gets you out of bed in the morning. And so it’s it’s a place with a lot of opportunity in the coming years. You know, if you if you want to find nickels on the street, it’s it’s a great area of interest. So ran ran a quant group went down the systematic credit direction, we were running a pretty big portfolio up through the pandemic. And like a lot of people at the time, took that time to evaluate what I wanted to do with my life and left my past firm. And, you know, slight interjection thought, I’m going to have a really nice garden leave and relax and the universe said, How about get cancer. And so I got cancer for like six months. And then Andy had been out of whitebox for two or three years. And his his contractual obligation not to have a fund was ending. And we started having conversations about what might be interesting. And you know, like you said, we’re very quantitatively informed, we tend to say something like quantitative rigor instead of a quant fund. And that has become what we are today, which is trying to make worldview thesis driven, trading, systematic and scalable. Man, I think we’ve had success in in doing that thing. So presently, I’m our managing partner at x cubed, but also responsible for building our infrastructure and the, you know, the core skeleton of of how we look at the world.
Nathaniel E. Baker 17:17
Interesting. And I mentioned that this is a multi strategy firm. And you have a kind of a unique approach there. It’s not multi manager, right? There’s an important distinction there. So tell me about that.
Chris Bemis 17:29
Yeah, we think that there’s an opportunity in what would have in the early 2000s Be been seen as just the regular way multi strategy. And where we think that there’s an opportunity there is because the success of the multi manager platforms have reduced by design, it’s a hallmark of their success, process and fluency, pods are constructed so that they don’t talk amongst each other, which has a real benefit for the multi manager platforms. But we think that the result of that is that those places, in the scenes between pricings of asset classes have left money on the table. And, you know, quite honestly, I find it aesthetically interesting. This idea of of pricing across asset classes within the firm, we have a particular penchant for pricing through the lens of volatility. And that’s, like I already mentioned, that’s really a creative in any credit analysis, but also finds a home in options as a standalone asset class.
Nathaniel E. Baker 18:53
Interesting. And so the now the trading that you do, it’s like I said, capital structure specific tranches, I guess, of the credit, potentially against each other. Right. And you say that you’re quite bullish on this strategy going forward. And then it’s scalable. Is there a way to replicate this on the retail side? Or is this just something for institutions like you think? It’s a good question. I don’t know if I’m good answer that. Okay. Well, that’s good.
Chris Bemis 19:28
Yeah. I, you know, at the end of the day, I was surprised when I left my last firm, just how nascent the space of doing work in systematic credit is. So if, if it is possible, which, you know, there’s really not that much barrier. It’s one of those places and there’s an anecdote that I particularly like about Edison going into Ford’s plant, and spending only a handful of minutes and putting an axon on one of the support beams and saying this is where you need to put your, your breaker for, for their electric build out. And he sends, he sends forward a bill for at the time, like something like $10,000, which was a lot in today’s dollars. And Ford said, like you were only there for like 30 minutes. How can you? How can you charge me this much? He said, I’m not charging for my time. I’m telling I’m charging you for knowing where to put the X? Yeah. So I think this, I think this kind of falls in that same category. In the sense of, if you know, how or where to put the x, it becomes it becomes a different enterprise.
Nathaniel E. Baker 20:45
Yeah, having said that, I mean, where does one even find out pricing on these instruments? It’s not like, you know, a lot of
Chris Bemis 20:51
a lot of the stuff that we look at is, is public. I mean, we’re a highly liquid firm. We’re looking at publicly traded credit in, you know, obviously public equity and and options. It’s really, I think, a question of interest. Yeah, asking yourself the question of, can you price, one of these points on the triangle with respect to the other two? In that area of interest, like I said, I think is somewhat precluded by construction in the current market, especially in the hedge fund market. So, you know, so long as there’s interest, I think it’s possible, and we find it very interesting.
Nathaniel E. Baker 21:37
Like, what kind of things would you would you look for? Is there something obvious? I mean, it sounds like, obviously, there’s a lot of special sauce in here. But in fact, I mean, you talked about credit spreads. What kind of screens? Do you have? Like, yeah,
Chris Bemis 21:50
yeah, I, you know, I built out a, it’s been a bit of manic period, having a startup and building things out over the past year within credit, we look at a really big universe every day of bonds against their volatility and equity sweep screen about 600 names every night, we look in CDs, for the same, we look at basis packages, but basis really in the view of having some Vega or, or volatility, sensitivity and deltas and some equity sensitivity. Meaning that basis in general, is something that people identify as having a liquidity premium, and that that can be priced via options and equity. We’ll look at some relative spreads. So we’re really looking at these things that we think have pardon the the Latin here, but an odd priori reason to exist on a lot of quantitative work ends up having something that feels a bit black boxy, where you don’t necessarily know what the model is saying. What we’ve done internally is trying again, this is part of that intention of building a thematic worldview. firm that’s process driven, and scalable. What we’ve done on the quantitative side is really just try to reflect those views we would have as investors with a lot of gray hair. So the results end up looking very interpretable. I mean, they’re they’re essentially as simple as what was done in the late 60s, early 70s, in the option space of saying, you know, an option has some Vega and some delta and this is how one ought to price an option as a function of some portion of of equity. We’re just doing the same thing in credit.
Nathaniel E. Baker 23:53
So to speak, of your thematic worldview. Is that are there any other themes bigger picture that you can talk to that you’re seeing right now? You
Chris Bemis 24:01
know, similar? You know, we’ve been very bearish. Anything that touches on office space. If you if we had done this podcast 14 months ago, we would have counted as contrary and they’re being short office space against what we think are great credits. In particular, just single asset single borrower credits. We’re bullish homebuilders. You know, in that same vein, if there’s too much office space, there’s too little homes. And then, you know, the rest of what we look at right now is mispricings in a somewhat fearful market, the bullish runs we’ve had very recently in the equity markets notwithstanding.
Nathaniel E. Baker 24:53
Wow, yeah. Okay, so it sounds like you know, I don’t want to put words in your mouth. That could be me be more upside here in these markets.
Chris Bemis 24:59
You Yeah, you know, I, I tend to not prognosticate. And what I what I try to restrict myself to is those places where we find a, an economics statistical or thematic mispricing between asset classes or you know, maybe maybe within a capital structure. And what I will say without qualification is, those are plentiful right now. We have opportunities and SKU opportunities and credit opportunities, calendar spreads in general, the markets pricing of volatility in the general sense. So, credit options is rife with opportunity.
Nathaniel E. Baker 25:47
So, yeah, talk to me some more about this, this office space idea, because I guess so you’re not buying the return to Office narrative at all, that a lot of firms have been trying, you know,
Chris Bemis 25:57
a, you know, if you look at what we’re doing right now, and, you know, 80, or 90% of the calls I’m on for work, we have moved to a place where the capacity that was needed for office space is not justifiable. If there’s a return, it’s not a return to 100%. In, you know, I did some analysis back in early 22, tracking cell phone data. And it really looks like the usage and office space is leveled out, and about two thirds 70% of what it was pre pandemic. That doesn’t sound that significant. But it is catastrophic to some tranches within say, CDMX. You don’t need that many losses to accumulate to have massive wins on the downside being being short that space. So no, don’t I don’t buy it. And I think there’s still a ton of potential there.
Nathaniel E. Baker 27:00
Yeah. And we’ve seen certain regions, especially San Francisco, kind of having a hard time some headlines here the other day. Yes, it’s very generic. But yeah, yeah. Yeah. So on that, yeah. You talked about? You touched on this cell phone data. Talk to me about that, and how you harness that data to?
Chris Bemis 27:23
Sure. Yeah, so everything we ended up doing, again, like I said, we, we try to focus on quantitative rigor, and there’s, there’s places where a regular way, quant screen doesn’t necessarily apply. So in this identification of the opportunity in being short office spaces, one of the things that we did we think it’s it was one of those worldview driven trades that we think is something was somewhat obvious to us. But again, the market not necessarily agreeing a year ago. But one of the ways we leveraged doing some quantitative analysis was to aggregate geo fenced cellphone data that was identified for for office space, or retail space within some of the series within CMBS. So we knew what the loans were, we know what their addresses were, we’re able to look at a time series of pings of cellphone data’s entering and exiting, spending time in them, et cetera. And the net result of that was something that really solidified our view. And that was that you had recovery across the board in retail space. And really cheap geographically that was across the United States. But within Office Spaces, we could identify those places that were seeing the most stress, seeing the least recovery, and reaching their level in the post pandemic world. And then the next step was constructing a trade that isolated those exposures.
Nathaniel E. Baker 29:12
That’s fascinating. Yeah. Because this I mean, anecdotally, anybody who’s been near a mall, or maybe an outdoor mall, or any retail outlet in the US can say that there’s no slowdown. I mean, the these places are packed. It’s amazing, isn’t it? Yeah. So yeah, it’s interesting to for you to quantify that. And this data is publicly available.
Chris Bemis 29:32
No, this was a partnership that we made with another firm.
Nathaniel E. Baker 29:36
I see. But do you make use of satellite data at all?
Chris Bemis 29:40
I have not. I found some interested in in it historically. But in previous incarnations of my work, I didn’t find it marginally accretive to the other signals that you might get from from the market, where I think our firm now does distinguish itself from my password is that if we take as table stakes having some quantitative analysis for every trade we look at, it opens up the toolbox to having marginal gains for one specific trade. And so this this analysis of cellphone data, I think usually is pitched for cross sectional analysis or broad based trading in general. We found it to be useful for this one instance. And that’s that’s emblematic of our approach in general,
Nathaniel E. Baker 30:36
I have to ask about AI hot topic of the year or whatever. Any ways that you’re using that yet?
Chris Bemis 30:44
No, I have done work in the past. I think my hat as a mathematician guides a lot of my thinking here, I’ll share an anecdote that I think is appropriate for the application of AI in finance. And it’s that if you train something with a neural net, in reinforcement learning framework, to play chess, within a day, given today’s computing power, you can be all humans, handily. Humans can’t compete, playing chess against something trained that way. What’s fascinating, and I think, instructive about that, is that if you then say, let’s let the Knights move, like rooks, and you let that same amazing bit of code, play against humans, a five year old will be at it. Okay, this is so part of the fabric of AI, that it has a name, and it’s called catastrophic forgetting. We think that markets are dynamic enough to give pause to the application of AI. In finance.
Nathaniel E. Baker 32:00
Yeah, that’s really interesting. But yet you still are looking for a link, I guess, between academic fine and in academic math.
Chris Bemis 32:08
Yeah, that’s, yeah, in its I think it’s a great contrast, where in in AI approach tends to say, Let’s build an artificial structure to relate inputs to some output. So some supervised learning approach says, let’s, let’s have the inputs match the match these outputs and the computer can say, how those ought to relate to each other. What that ends up doing, in general is overfitting to the past in finance. And there’s, I think, several examples where you can find that probably the most readily available is looking at something like a Zillow is efforts in pricing homes and then buying them via their modeling. Which film. Where there’s a distinction, then, is this idea that the exercise of math modeling of financial assets, is, in my opinion, best supported by the approach of having a worldview our priori view of what the relationship should be. And imposing that on. That’s not possible in the AI setting. You can’t impose a view in an AI setting readily. There might be some workarounds on that, but not readily. So the contrast ends up being one that says, fit very well to history, given free parameters on any relationships that might obtain versus saying to a model. This is the relationship that should exist. Now tell me if there’s a mispricing. And it’s in the latter place that I think there’s more opportunity for identifying alpha
Nathaniel E. Baker 34:09
So then there, there would then need to be an additional screen of human intelligence?
Chris Bemis 34:15
Absolutely. Thanks for laying that up. For me. I think our marketing people would have been upset that I didn’t say that at the outset. Absolutely. So if there’s three if there’s three tiers to how we look at things, it’s that worldview, quantitative analysis, and then especially because we’re in the space of credit and options. Having a high touch market facing component is critical. But computers don’t know if prices are wrong as input so garbage in garbage out. Being able to execute efficiently near mids is another piece of real relationship business on the on the credit side, and so on. So, letting your computer do whatever it wants in financial markets, I think as a as a place but not a place where we’re where we built.
Nathaniel E. Baker 35:04
Yeah, I mean, anecdotally, having covered hedge funds. We’ve seen this a lot over the years going back a while, like 10 or 15 years even. Yeah, seems every couple of years is a big story about x firm hiring all these quants, and NLP and AI and all this stuff. And then you never hear from it again
Chris Bemis 35:22
I think there’s a reason for that
Nathaniel E. Baker 35:24
Which is? just what you explained, or something else?
Chris Bemis 35:26
Just what I explained. And if the successes were there, you’d hear more of it.
Nathaniel E. Baker 35:30
Of course, yeah, I know that, as well as anybody
Chris Bemis 35:33
I think I think the most ready contrast is if you look at the publication history, in the late 90s, through the early 2000s, on equity anomalies, and how many researchers got picked up by firms. By way of contrast, you don’t see the same thing happening on the AI side.
Nathaniel E. Baker 35:54
Okay, so for our, you know, students, grad students, or even undergrads, who might be math majors or something, and are watching this, what can you recommend as a course of study that would not be hired by you personally, but to segue into the world of investing in finance?
Chris Bemis 36:15
you know, the thing I always tell people, having had a lot of people come to me asking whether or not they should get a PhD is, ultimately find the thing that’s most interesting to you. On the on the math side, if if finance is interesting, mathematicians have a general tendency to think they can do everything. But for their, their time and effort put in, it’s really, I think, most important to put a little bit of math understanding on the back seat, and understand the market, the language, the approach, because the history of successes in financial markets are not solely the outcome of some analytic ability, in those places are very interesting, especially for what is usually termed an industrial mathematician. So, you know, fine, if I were to give a recommendation would be fine, fine. What’s interesting and learn the language and be a little humble? Yeah,
Nathaniel E. Baker 37:24
yeah. Very interesting. And that kind of segue that also, you know, works with your theme of there needs to be a human element here to this whole investing approach. Markets are rational beasts, as we know, and they can stay irrational longer than a lot of us can stay solvent. Yeah. So really interesting conversation here with Chris Bemis of x cubed capital. In closing, is there how can we? Yeah, how would people go about finding more out about you and the firm anything you’ve published?
Chris Bemis 37:58
Sure. For those interested on the math side, there’s a handful of places you can find a bit of published work. If you just Google my name on something like you know, any of the archive places with respect to the firm. We’ve got an alphabet soup of a website or URL X3CMLLC.com. So that x cubed Capital Management LLC, is is our website.
Nathaniel E. Baker 38:29
But no social media presence.
Chris Bemis 38:31
No, no. I actually, I should note that Andy has a podcast.
Nathaniel E. Baker 38:37
He does have podcast.
Chris Bemis 38:38
Yeah. Which not just talking my book I find interesting.
Nathaniel E. Baker 38:45
Yeah. Okay. I will link to that. I’ll ask him more about that. That’d be awesome. I’m sure she told me at some point, but Okay. Awesome. Well, this has been really interesting. Chris Bemis, thank you so much for coming home to contrarian investor podcast today was awesome to have you. I look forward to having you back here. Again, maybe in a year or so.
Chris Bemis 39:01
Would love it
Nathaniel E. Baker 39:02
markets will be completely different than and we will have some more opportunities, some timely opportunities. Until then we leave you. We’ll see you back here again next week. And without we thank you for listening. See you then. Bye.