High Frequency Trading and Systematic Macro Funds with Matthew Hanna of Teza Technology

There are not a lot of Florida Gator fans in Utah, just like there are few firms that move from high-frequency trading (HFT) into the mutual fund space. That’s why Matthew Hanna from Teza Capital Management is here to talk us through what it is like running a systematic macro mutual fund. He reviews Teza’s HFT and prop trading roots, how Teza’s algorithms approach the market, all while mixing his love for UF sports into the conversation.

We also flip the script and ask the lead PM of the Catalyst/ Teza Algorithmic Allocation Fund (TEZAX) to dig deeper by covering some off-colored topics that viewers want to know more about, like the decision to pivot away from HFTs, why the Catalyst TEZA Algorithmic Allocation Fund essentially went flat for the first seven months of 2021, and how is Teza’s strategy doing now? Plus, we put Matt in the hot seat to provide his perspective on topics that nobody is talking about, or everyone is wrongly talking about.
Highlights and topics from this episode include:

 

  • How to determine what volatility is on a forward-looking basis and where risk models come into play
  • The algorithmic allocation and breaking down the equity bucket
  • Why absolute return in the alternative mutual fund space means low volatility and low return
  • Why you need to adapt if stocks and bonds are down together over the next 16 months
  • A closer look into the reality of why your model will work better sometimes than other times and the importance of allowing your volatility to float. Plus, more!

Find the full episode links for The Derivative below:

Check out the complete Transcript from this weeks podcast below:

High Frequency Trading and Systematic Macro Funds with Matthew Hanna of Teza Technology

 

Jeff Malec  00:07

Okay, we’re here with Matt Hanna. And I didn’t ask how to pronounce your last name is Hana.

 

Matt Hanna  01:49

That is correct. I’m pretty sure right.

 

Jeff Malec  01:52

Okay, we’re here with Matt Hannah of Tezza Capital Management to talk through run in a systematic macro mutual fund some of the firm’s high frequency trading and prop trading routes, and what it’s like being a gator fan in Utah. So welcome, Matt.

 

Matt Hanna  02:07

Thanks, Jeff. I really do appreciate you giving me the opportunity to speak with you today.

 

Jeff Malec  02:12

Yeah, so I’m a gator fan. I grew up. My stepdad went to USF. So we drove up from Vero Beach to all the gator games. GALEN Hall and Emmitt Smith when he was there. So love the TiVo Jersey behind it, but it seems to be the wrong in the wrong colors.

 

Matt Hanna  02:30

Yeah, that’s a Jets jersey and I’m certainly not a Jets fan. I’m a Tampa Bay Buccaneer fan. So you know, I shed some tears with Tom Brady retiring and we’re going to go back to the basement pretty soon. Buccaneer legacy but yeah, so that’s jets jersey, but a big obviously big Tim Tebow fan, big Gator fan. I went to school a little before Tim Tebow got to USF. So not quite my era, but I got to enjoy basketball national championship and, you know, as a student, and you know, kind of growing up, follow the Gators and even today try to attend as much as I can. In person, whether it’s football, basketball, baseball, gymnastics, softball, anything. And now you know, with just TV becoming more broad SEC Network. Plus, I’m able to really watch anything I wish I could get my hands on so collegiate sports is definitely my thing more so than than pro sports. But, you know, Tim Tebow is a big part of that. And he’s a great Gator ambassador.

 

Jeff Malec  03:35

I hear you. So the basketball championships that was joking, Noah back to backs. Yeah.

 

Matt Hanna  03:41

Al Horford Corey Brewer, Torian Humphrey. All those guys. Obviously was awesome. I got to see one of the championships live in Atlanta. So that was was fantastic. Great team, I think still highly underrated. But, you know, right now we’re struggling to be honest with you. This is year seven in the Mike White regime. That’s our head coach. And it’s not really going the way I’d like it for basically, best way to put it is just average. You know, I don’t think you can settle for average and anything so I’d rather shoot for the moon. And if it gets worse, fine, but at least you’re given a shot.

 

Jeff Malec  04:20

Yeah. And then that was the football season was super weird, fired their coach halfway through. And whenever we could spend an hour on Gator, Gator sports, and that school is super hard to get into now, I was talking with a buddy in Florida whose kid had straight A’s down in a school in Palm Beach and couldn’t get into Florida.

 

Matt Hanna  04:37

Yeah, I don’t even know if I could get in at that point. But yeah, I mean, it’s definitely becoming a more international school. I know that a big thing this year about becoming like a top five public university. So it’s definitely a highly competitive. It’s a great school, great culture. I love Gainesville, great location. But it’s a little bit different now than it was Yeah, when I was there 20

 

Jeff Malec  05:02

Now, now you’re way away from Gainesville in in Layton, Utah, right, just north of Salt Lake.

 

Matt Hanna  05:09

Yeah, I’m in Salt Lake City, Utah, like I said, Little north in a suburb. Long story short, I moved out here for a job, call it five ish years ago, and love it out here. And my understanding is you’re actually coming out here later today to go skiing.

 

Jeff Malec  05:27

Yes, we won’t this will be released later than that. So I’ll be back in Chicago by the time this is released, but yeah, fine. Make it out there twice a year, probably at least. So love it out there. We got to get a Utah office, RCM Utah office. And so talk through a little bit more of the background. So you came out, you went out there for a hedge fund job, and then ended up here at Tesla run in the the macro mutual fund. So give us a little bit of the personal background.

 

Matt Hanna  05:55

Yeah, I mean, before I jump into that, where I started talking about Tesla, so obviously, you know, all my opinions here expressed here today are my own opinions do not reflect Tesla capital, pair companies, or anything affiliated, you should not treat my opinion as a specific inducement to make an investment in the mutual fund. I’m a portfolio manager which is the Cata catalyst Tezza algorithm Allocation Fund, or any type of fund for to follow a particular investment strategy. My opinions of course, based upon info I consider reliable, but do not warn to its completeness or accuracy. Past performance is not indicative of future results. I don’t guarantee any specific outcome or profit, the listener should be aware of real risk of loss following any strategy or investment. This discussion does not take into account any of us winners individual particular investment objectives, needs and is not intended as a recommendation. As a listener, you should take your own independent decision regarding any investments discussed here and consider whether it’s suitable for your circumstances. And of course, seek advice from your own financial advisor or investment professional. So I am done with that. That is good. So long story short, make sure anything is suitable for your needs, in your circumstances and seek out advice where appropriate.

 

 

Jeff Malec  07:16

Yeah, I think we should just have a whole industry reset. And the default will be anything you hear on TV or social media or read on a website is not investment advice. investment advice is only given in person that right we can flip the whole script instead of everyone always having to say, this is not investment advice. But anyway, out of the way. So we were going back a little bit of how you ended up at Tezza.

 

Matt Hanna  07:42

Yeah, so we can kind of begin on a, you know, nighttime way back in the early 80s. Matt Hanna was born in Tampa, Florida. Again, that’s kind of how I led myself to the University of Florida. So kind of grew up. They’re very middle class parents, blue collar parents, got into University of Florida. My undergrad was actually in political science. So I wanted to be a lawyer until I sat down to take the LSAT. And then I realized I don’t want to be a lawyer. It’s not what they show on law and order. So long story short, I had to find a job. And I applied pretty much everywhere. So I got a very basic operational job at Raymond James, in Tampa, St. Pete Florida, actually was my first kind of foray into finance, and quickly realized, hey, you know, I want to kind of move up the ladder, make a little bit more money provide for my family challenged myself. So I went to night school, got my CFA charter Caya certification, took the frm as well, and got into basically you can think of as mutual fund research and asset allocation at Raymond James. The beauty there is I got to interview see talk to hundreds of investment managers like where I am today, but just think of kind of wide range of mutual funds all trying to raise $1 and get on our platforms. So I got to see what worked what didn’t, not only from an investment strategy perspective, but also just from a marketing sales perspective. What resonates and what where firms like Tesla at this point, go wrong, what’s the hurdles they have to overcome? Over time, basically, late 2016, early 2017, I got a job at a firm called summit global investments out here in Salt Lake City, where I was a portfolio manager on seven different mutual funds. Four of those being quantitative equity strategies and three of those being asset allocation, mutual funds or quantitative we systematically manage, also developed a hedge fund for summit as well then decided to Take the leap and kind of jump to Teza, which, you know, they’re based in a variety of different offices Austin, Chicago and New York. But the primary goal is to manage their alternative mutual fund in terms of research, development, portfolio management, but also spread the word to people like yourself and your listeners.

 

Jeff Malec  10:26

So little on the Tesla background, your firm’s a bit different than most in the mutual fund space, having been a high frequency trading shop, trading proprietary money before venturing into the hedge fund world, and eventually the mutual fund world. So let’s start with that high frequency trading part. It’s not exactly the same as most other Chicago based prop trading firms or options base. So kind of take us through if you can that the history of the firm, like are we talking full on microwave towers and spending billions of dollars to shave a microsecond? What does that high frequency history kind of look like?

 

Matt Hanna  11:01

Short answer is yes. So again, I’ll kind of kind of go back in time, not as early as the 80s When I was born, but our CEO, Misha Malyshev, worked at Citadel and he kind of built out their high frequency trading business. He did fantastic there in 2008, and decided to launch his own firm in 2009. And that’s, that’s the kind of a Tesla start. And that’s our owner CEOs, no background in the high frequency space. So that’s where Tesla began trading as primarily focused on the high frequency space, like you mentioned, you know, telecom assets and trying to shave microseconds, highly competitive business, as you entered the past call it five, six years, seven years in that range. Just profitability in that business kind of shrunk, highly, highly competitive, various edges and trying to shave those microseconds, kind of dissipated. So Teza found, frankly, another better opportunity. And that’s kind of how we met today. So we sold some of our assets in the high frequency space, whether you’re talking those kind of towers we’re talking about, or some other just the IP, you know, as well. So we pivoted and pivoted in a kind of broader direction, where we’re now we’re trying to bring solutions to the public, whether it’s kind of more of your private solutions, or mutual fund solutions as well. And we’re trying to gain alpha in a variety of different methodologies, kind of the, the original pivot is actually very similar to high frequency, it’s utilizing a lot of the same data. So think like order flow data, but instead of trying to win by a microsecond, we’re trying to utilize a lot of that data to help predict where the markets going to go over the next day to three week. So that’s very similar data. But just timeframes a little bit different. Usually, utilization of that data is a bit different. But we also do a lot of cool stuff at Tesla beyond just kind of the microstructure order flow predictions. One, I think that it is quite interesting, more in the global macro, tactical allocation side. But we also have individuals that’s, you know, a lot of cool stuff, whether it’s arbitrage, longshore trading, you know, various power markets kind of runs the gamut, wherever you can find alpha, we’re interested to seek out and then we can kind of deliver those solutions in a variety of different formats, whether it’s mutual funds, or just kind of bespoke solutions for clients.

 

Jeff Malec  13:33

And then, so is there any firm capital put to use as well still or no, so it all deployed for clients?

 

Matt Hanna  13:40

I’m sure Misha has some of his of his own stuff kind of going on as well. So yeah, yeah. But it’s primarily I’d say the focus on kind of raising outside capital.

 

Jeff Malec  13:50

And back in the high frequency days, if they found a new edge, they found a new market. Like what kind of sharp are they looking for? Right? You hear these stories of like, I’m not interested if it’s less than an eight sharp or something. I’m not a huge fan of the Sharpe ratio to begin with, but um, do you have any insight into kind of what what the bar was for being able to be deployed with the firm capital?

 

Matt Hanna  14:12

Not exactly sure. What, what the kind of the expected Sharpe ratio was, especially back then, but it is it is public knowledge that when Misha was at Citadel, he, his group was able to post a profit, not of seven figures, but of 10 figures, which is a lot of zeros. So basically, we did did quite well. So that’s obviously a very high Sharpe ratio. But now I think if you can hover in that, depending on what you’re doing that two to three Sharpe ratio, I think you’re doing a pretty good job.

 

Jeff Malec  14:46

Yeah, for sure. And so talk a little bit about how that high frequency pivot you said like, it was looking at microstructure and saying, Hey, we want to be in this trade for the next millisecond, and then get out now that might be we want to be in it for the next Are we talking minutes or hours or days? What does that look like?

 

Matt Hanna  15:03

Generally days, but at depends on the exact trade. Yeah. And there’s different PMS that utilize that data in different formats, too. So I can’t speak to you know, what everybody does at Tesla different people do different things and try to find different, you know, alpha sources. But, you know, for us, it’s primarily not about the millisecond anymore. It’s more, I think, a day is the is the best kind of, I would say, look, back period, that we’re trying to analyze the book for a period that we’re trying to analyze. But some people certainly still trade, you know, intraday, but some people might not put on trades, you know, every day, it might be every week. So it kind of runs the gamut. And I think that’s part of risk management is not just to have your firm focused on the millisecond, or the day or the week or the year, but have people do a lot of different things that all complement each other at the end of the day.

 

Jeff Malec  15:54

Right? And do you think just generally speaking, a lot of the same firms, it just became too expensive to compete with one another, and the edge just kept getting smaller and smaller?

 

Matt Hanna  16:03

Yeah, I mean, that’s, that’s, that’s business, that’s going to happen every which way. And that’s why a lot of hedge funds really kind of guard their secrets in their IP. Because once that edge gets out there, it’s gonna shrink to the point where it goes away. And you got to find another source of of alpha. And honestly, I think that’s a big issue. Hedge funds have had going to public in a mutual fund is totally different. Just pitch one is very non transparent. And one is, you have to be ultra transparent, just in your approach. And I think a lot of mutual funds that are in alternative space, they made a big mistake, because of it is not built it, you will build it and they will come type approach, you have to really pound the pavement and spread the word. And you know that people know what you’re doing.

 

Jeff Malec  16:53

And it seems like, right, I’ve been at dinners around Chicago here like prop trading firms laugh at the mutual fund industry, right? Like these suckers. They don’t know what they’re doing, just buying and holding. There’s better ways. They’re right, they’re running a one sharp, we have an eight sharp. So it’s a little funny like to see that pivot to see that movement towards that space and say, Hey, no, there’s still smart ways to approach it, there’s still smart ways you can deliver this alpha in the mutual fund wrapper.

 

Matt Hanna  17:20

Yeah, I mean, there’s different clients. I mean, the reality is, your neighbors cross the street, they have their 401k You know, the rich, lawyer, dentists, they have their retirement, you have institutional money, there’s, there’s a lot of different needs for a lot of different people. So just you know, state or you know, prop trading firm, their market is much more narrow. And if you become a, you know, call it a hedge fund trying to raise outside capital, your client base is still I’d say, quite, quite narrow. But from a mutual fund perspective, we’re able to help provide good solutions to a lot of different people. And if you open up your mind, you’re able to really give excellent solutions above and beyond what typically are found in the mutual fund space for those clients. And frankly, I’d argue my neighbor who’s trying to save for retirement has a far bigger need for something interesting different. Produce a decent Sharpe, then. Yeah, raise your, you know, exit founder somewhere,

 

Jeff Malec  18:24

right, XYZ fund to fund. Okay, onto the mutual fund. So you’re the lead. pm right of the Catalyst has an algorithmic Allocation Fund. Yeah, it’s

 

Matt Hanna  18:41

definitely a group effort. But certainly I spend most of my time on this fund, if not all my time on this funding variety of forums. Misha, the individual, the CEO I talked about before, he’s definitely heavily involved as a portfolio manager. And then we have our chief risk officer, Orion holder, he’s heavily involved as well. But, you know, we all talk about exactly the direction we want to go in what we’re trying to achieve. And at this point, I think the fund is rocking and rolling. And we’re excited about the future.

 

Jeff Malec  19:14

And the symbol tz x, right? Yep,

 

Matt Hanna  19:17

te Zax which Tezza X or the I share, which is T easy. I x. I think what’s really interesting and important to think about is what’s in the name. So like, as you mentioned before, the Catalyst has a algorithmic Allocation Fund. I think the key word here is allocation. And, you know, Jeff, what I bring up the word allocation to you, I mean, what do you think about what is what does allocation? How do you view allocation in the kind of the, the broader tufan retail landscape?

 

Jeff Malec  19:47

I like it, we’re flipping the script. Just yeah, diversifying saying I need to have right the basic would be 6040 stock bond, but to me, I want some trend. I want some long volatility. I want some Some stocks. So yeah, just my percentage allocations. I’m using the word in the definition. But yeah, that’s okay.

 

Matt Hanna  20:09

This isn’t. This isn’t like Jeopardy or anything of that sort. But I think you’re you’re right on the button. And especially with your point on what you would prefer is to have some trends, some other stuff. And I think the reason why I think that’s really pertinent, especially these days, is most clients that might be not 6040. I call that just kind of average age average risk. Some might be mostly stocks, somebody young, really aggressive, and some people might be closer to retirement and might have 30% stocks and 70% bonds. But the classical 40 ish passed years thought is when stocks do poorly, bonds help you. That is kind of the whole theory of, you know, asset allocation, and how financial advisors generally set up portfolios. So if someone’s really conservative, then I have 30%, stocks, 7% bonds, their stocks go down, but they’re bronze protect them. But, Jeff, I’ll ask you another question here, in this new paradigm where interest rates are beginning to increase, does that allocation makes sense? I mean, there’s clearly a potential issue there. But I just think,

 

Jeff Malec  21:16

huge issue. Yeah, we’ve had all the all the lines on here, return free risk. It’s the best one, but yeah, you’re in the old days, if I’m going to get paid to hold this thing that should increase when there’s a market crash. So be it today, if I’m going to get paid next to nothing for this thing that maybe won’t protect me in a crash. What is does it make any sense?

 

Matt Hanna  21:38

Yeah, and even worse, if interest rates go up, you lose money. Yeah, it’s not even a performing asset doesn’t help protect you. And long term, your expected return is pretty, pretty crappy. Not saying necessarily, interest rates are going to rise in for a long period of time. But clearly, especially over the past January, we’ve saw that where interest rates went up that caused NASDAQ, especially to go down. That’s, that’s a really rough combination for a lot of clients. So long story short, the Catalyst has a algorithm Allocation Fund, we’re trying to help solve some of that issue. As an allocation fund, we recognize that just being static 6040 3070 8020 might not always be in the best interest of your goals. And knowing that sometimes it makes sense to increase the equity percent, sometimes it makes sense to decrease the equity percent, sometimes it makes sense to do the same for interest rates, as well as other assets. Sometimes it makes sense to increase your overall risk or decrease your overall risk. It’s good to be flexible, and tactical. So our solution here is not something I would say make sense, as you’re only holding for most, say, retirees, you want to be well balanced, and kind of set up long term with your goals. But something that could really help you around the edges. I think that’s where we come into play very, very well, we’re able to help control your risk control, some of those kind of esoteric outcomes in the market in ways your traditional allocation camp.

 

Jeff Malec  23:09

And how do you consider right so part of me thinks, yeah, the old way of like, ooh, China’s doing some saber rattling and oil sigh, I think we should pull back on our equity. Right? Like, the old way would just be some PM, having a feeling about oh, I’m gonna adjust my allocation because this looks risky. So tons of problems with that, when does it unlock risky, right? What if your opportunity cost all that? So juxtapose that with algorithmic, I go on the algorithmic part of the name algorithmic, the con would be Oh, it’s always backwards looking, it’s just going to move into what was working, which, by definition, probably is not going to be what we’ll be working. So how do you kind of weigh those two? Two methods of asset allocation? Where do you guys fall in between those?

 

Matt Hanna  23:58

Yeah, so we’re certainly systematic, all the way through. And I’ll kind of talk about some of the pros and cons with that. So first, if you gave me a call, I know this is going to air after you’re out skiing, but if you give me a call, when we’re out here in Salt Lake, and I got to meet you on the slopes, and I’m a terrible skier, but say I got out of control, and I fell and broke my leg or anything like that. The beauty of something algorithmic is it’s still gonna run, still gonna do exactly as we intended, and there’s gonna be no hiccups, where if it’s purely discretionary, somebody you know, reading the news, the finger on the pulse of the market all the time. If they had that same accident, well, you’re kind of Sol. He’s clearly not going to be able to, you know, function. So there’s certainly a business continuity benefit of being algorithmic but also kind of go back to risk management. And risk management is typically at the core of really most hedge funds but definitely here at Tezza. And I would view risk management as not just a focus on your volatility or your risk of losing capital, but also, what are the causes of potentially losing capital. And one of those causes would be, say bias. And bias comes in a lot of forms. If you’re heavily discretionary confirmation bias is my favorite bias, maybe you can call it my least favorite bias. But generally, you’re hunting out information that for a cause, or a hypothesis that you’re already predisposed to. So you’re just kind of confirming your own opinion. And there’s a lot of issues potentially with discretionary managers falling victim to a lot of bias. Now, quantitative strategies have their own potential bias, blind spots, as well, but a lot less when it comes to pair to discretionary strategies, we’re able to reduce risk that way. And I mentioned before, it’s not just quantitative in a singular fashion, one particular model mentioned kind of looking backwards, maybe trend following in that, and certainly I believe in that, but that’s risky, because it’s, you know, isolated, it’s only one poker strategy. Here. We’re trying to get alpha sources, an allocation framework from many, many, many different evidence, methodologies, timeframes, processes, people, and doing so we’re able to minimize some of those risks that are, can’t really be seen if we just pull up a Morningstar page, but really do exist once you kind of pull back on him.

 

Jeff Malec  26:28

Which leads me to so there’s no mandate to be like a point six beta to the s&p or to bonds or any anything to a blended portfolio. Right. It’s just pure absolute return. And we’re going to get the most return for the least amount of risk, or is there a mandate to say we want some equity exposure, some bond exposure?

 

Matt Hanna  26:47

Yeah, that’s a great point. So I’ll hit on the volatility. So at this point, we try to target something between 13 and 18% annualized volatility, you know, I’d be thrilled if we can long run average around 70%. I think that’s incredibly, incredibly important because you get a keyword absolute return, and a lot of times absolute return. And the alternative mutual fund space means low volatility, and low return, well, when you’re charging a high fee, that’s a really bad value proposition for most clients. And I think that’s a very common pitfall of alternative mutual funds. We’re taking the opposite, opposite approach, we want to give really high volatility, bounced equity, like volatility, and hopefully, you know, returns commensurate with that. But that’s a much better value proposition. So we want to keep that mandate around that 13 18% fall, in terms of how we allocate to get that. Generally, I would say it’s about 50% of our risk is allocated to equities, 30 ish percent to interest rates, and 20% into I would call like an other basket, diversifying basket, primarily commodities, each one of those buckets can go long, short, and each one of those buckets are global in nature, but we are long biased, especially on the equity interest rate side, primarily equities generally go up, you know, you see the big green hammer or the green button, you don’t want to necessarily fight that a lot of models, I think, if they’re geared right would recognize the equity generally go up. Now, you know, hedge funds, if they’re trying to be absolutely trying to orient it can fight around that margin, but we’re an Allocation Fund for mutual fund clients, for potential retirees, we want to make sure we kind of fit into their portfolio overall portfolio. So having a slight long bias, I think makes a lot of sense for us. But we’re able to fight around the edges on a long short basis at times.

 

 

 

Jeff Malec  28:38

guys, so if I’m looking, building the portfolio, maybe more as a equity replacement or a 6040 replacement with a portion of that bucket? Because I should if you have the long bias I should get some of the upside.

 

Matt Hanna  28:53

Yeah, so if you could think like 5030 20 is generally how we are, in theory, we could be a replacement for equities or bonds depending on the client. And that’s where that disclaimer I crumbled up and through makes a lot of sense because your own situation, whether what your risk tolerance is makes a big, big difference on whether you source something like this from your equity portion, your bond portion, or combination, but the beauty is we’re diversifying enough that it can fit really into the most conservative client. But if you are very aggressive and you want equity like returns and risk you can also fit in, in there were most alternative mutual funds with low risk, lower return. That’s really hard fit. You certainly would lose long run. If you sold your passive s&p 500 ETF for an absolute return five vol product, you’re just not gonna get the same return. So we wanted to make something that can fit into most clients and having a higher volatility allows that flexibility, but that’s certainly up to your own needs and your own and specifications in Maryland portfolio.

 

Jeff Malec  30:02

Right? So what would it be fair to say if you if you have the same vol as 6040, and do a little bit better, right? And so the risk adjusted ratio is higher that you’re, you’re happy as a clam as the pm right? Be

 

Matt Hanna  30:16

I’d be happy. I’d put this way. I’d be happy if, if we beat equities. From a return perspective, I think that’s really important. And that also, I would say, buy a decent amount, but also being able to do it in a very diversified way, I think is very, very important.

 

Jeff Malec  30:36

And then let’s break down the equities in the bond bucket for that matter of it’s heavily US centric, are we are we talking s&p, s&p, NASDAQ Russell what uh, what goes inside that equity piece? All of the above. So,

 

Matt Hanna  30:50

kind of think of your kind of broader equity index futures across the globe. So s&p, NASDAQ, Russell, two footsie DAX, UK kind of go down the line. They’re all fair game. And there are times where we prefer one color asset group, perhaps us large caps over something International. But there also could be times where it could be flip flops. And then also, from a long, short perspective, even though we’re long, biased equities, that doesn’t necessarily mean we’re going to be long every equity out there every equity index out there, we could be long, short for many different features to be able to capture alpha the best way we possibly can. What does

 

Jeff Malec  31:32

that look like? So right us is trounced, foreign and emerging over the last 510 years? Right? So is the model going to be more heavily us there? And if that if that narrative flips or not the narrative but if that data flips, if the next 10 years, US gets trounced? Is the model going to say, hey, we’re, we’re more in these foreign markets, we’re more in the emerging markets now.

 

Matt Hanna  31:55

I mean, certainly, there’s a lot more that goes into it than just merely say trend or that conversation is heavily momentum focused. And I would generally agree if that is your methodology, looking at momentum trend? Absolutely. No, we want to see what’s worked in the past. And generally, that continues to work, at least for a little bit into the future, or the way we approach this fund, we certainly have elements of that. So if international began to trounce us, that would be a feather in the cap for international. That being said, there could be other things at play that could Trump Trump that affect, for example, if economic data was poor, internationally, the valuations were poor internationally, some of our kind of faster statistical machine learning AI models look negative on international banks, certainly, maybe the final output might not be more tilted international than we are today. That being said, that’s the beauty of a multi model, multi methodology approach is surely some of the evidence, you just don’t want to look at one part, you want to look at all the evidence you could possibly find to make the most informed

 

Jeff Malec  33:09

decision. Right? So I think that’s both like a blessing and a curse, right? So the purpose would be like, Okay, you’re always going to be underperforming one of those sleeves. But if someone’s taking on concentrated risk and emerging markets, God bless them, right, like, go ahead. But that’s been a painful bet for many years. And it’s going to be your portfolio, your whole goal is you don’t want your portfolio to be solely dependent on one sleeve, right? Yeah, yeah.

 

Matt Hanna  33:35

And that’s why traditional allocations for retail clients, you might have an active manager or passive ETF in US large caps, or emerging markets dedicated to that. And I say a lot of hedge fund managers might be very niche, but their edge and a very specific spot. But what we’re trying to solve here, and I think most alternative mutual funds, really should be thinking about is how you fit in that kind of broader, you know, landscape. So the importance is kind of smoothing out that ride. And it’s not just smoothing, I thrive in terms of volatility, but smoothing out thrive in terms of how you perform, right. It’s much better, in my opinion, to be consistently good than occasionally great, and sometimes bad. So long term. I mean, that’s talking about what would make you happy is to be consistently solid. Not necessarily on a day to day basis, but even something on a quarter to quarter year to year, we want to be consistently good. And the best way to do that is to that some of the evidence approach.

 

Jeff Malec  34:39

We’ve talked momentum as I’ll throw out the word factor, you can correct me if you think that’s wrong, but I’m right momentum, some AI models. So there’s a whole bunch of stuff going on behind the scenes, how many? I’ll say factor again, if you think that’s the right word, but how many models what’s going on behind the scenes that’s informing all these different asset classes?

 

 

 

Matt Hanna  34:59

Yeah, I mean, there’s dozens of different methodologies. And that’s something we have a lot of people looking at constantly, whether we want to add something new that has potential alpha, or even if a potential source of alpha kind of lost, lost its ability, and perhaps we need to pull back on it. That’s something that’s ongoing that research, but certainly there is we talked about kind of that that switch gears of order flow microstructure to something longer term, that is something, you know, we utilize a little bit, whether it’s trend, we utilize that we look at the relationships amongst asset classes, with a prime example of stocks going down, and interest rates going up, that’s very abnormal, that influences some of our decision making economic data, you know, inflation, industrial production, etc, that can influence some of our decision making fundamentals, whether it’s, you know, bottom up, or even things like valuation could certainly undue influence a lot of our decision making. So it can be it is a wide net, that we try to cast trying to get alpha from a lot of different sources we do then to kind of sum it up. But that’s just on the Alpha side. On the risk side, we utilize a lot of different risk models. So for some of your listeners, you might just view risk as like I said, capital loss, but also volatility. But the question is, how do you determine what volatility is on a forward looking basis. And that’s where risk models come into play. There’s lots of different ways to predict risk models and predict risk. And some of these alpha sources utilize a very smaller windows of timeframes. So looking at, say 15 minute data intervals and seeing how assets are relating in a short time horizon, and some of our alpha sources, utilize models that are much more long return, looking at how assets behave over the course of a year or two, or even longer. So being able to kind of plan that gives us good chance, hopefully, I’ll be happy if we can be consistently good. But the reality and whether a discretionary or quantitative, like you said, one of those strategies, one of those methods is gonna do well. But what does that also mean? One of those strategies is going to do pretty, pretty poor. And I can, I can tell you, there’s going to be times individual strategies ebb and flow. And you don’t want to be if you have too many eggs in one basket, you can make some bad decisions. If if your strategy is not performing at that given moment. That doesn’t mean it’s a bad strategy is not gonna perform. But just means it’s not as time and that’s why it’s really important to, to cast out wide net. And that’s what we do.

 

Jeff Malec  37:36

And so all these different strategies today operate singly, so each one’s putting in its own orders, headstone risk, and then the sum of all those is the net positioning, are they netting beforehand, and then derive read the kind of like a voting system? Right, this one, this signal is saying this should be three, this one’s saying it’s negative one. And that’s the netting and then you put in an order.

 

Matt Hanna  37:59

Yeah, so it’s, again comes down to risk management and diversification of process. So the answer is both. Some, some of our alpha sources are designed to work interconnected with others, and some are truly independent. So the ones that work interconnected, they kind of learn from each other, and make a position intense, based upon their own positions, working together, and some are very, very different, where they’re not interconnected. And they’re kind of netting out at the end.

 

Jeff Malec  38:27

And then that is there, like an AI machine learning overlay that’s running all this or that’s considering all this.

 

Matt Hanna  38:36

Not at that kind of final netting stage. But step by step before that. Certainly, we have various forms of AI machine learning, some at the risk model stage and some at the alpha stage.

 

Jeff Malec  38:51

And then you call it macro. But as we talked about, there’s no discretionary. So there’s no classical macro view of your saying, we’re an inflationary period. All the risk is coming down 10%, or something of that nature.

 

Matt Hanna  39:04

Yeah, I mean, I guess it depends, like our Morningstar category at this point is macro trading. So, you know, it depends if you view macro as like a discretionary thing. I think a lot of people would understand like tactical allocation. I think that’s a another good way of putting it. But I think it’s kind of six one ways, half doesn’t another mean the overall goal is to produce a better allocation. By analyzing at a high level, both alpha sources globally, risk sources globally, and have that flexibility to ball and shore across the globe.

 

Jeff Malec  39:38

And then just jumping back a second. So all this call it fancy stuffs happening behind the scenes, it’s coming out with this portfolio. At the end of the day, the portfolio is going to look like what’s gonna be, hey, we’re long US growth, US value. We’re short European value. We’re short Japanese, right? So it’ll be kind of this mishmash of these different positions that Create this one. position that fair. Yeah,

 

Matt Hanna  40:04

exactly. So, like Becky said, long, say, s&p futures contracts, NASDAQ, but potentially short, say, various parts of the US interest rate curve, but long on March the Australian interest rate curve. So it’s a it’s a mishmash. Certainly, you know, like I said, long bias. So there’s all these strategies probably have a slight tear to the long side. But overall, without understanding and kind of pulling back the risk, and the potential Alpha sources, I think mishmash of positions makes a lot of sense as a descriptor of what you might see.

 

Jeff Malec  40:42

Yeah, sorry to belittle your these brilliant Alpha sources into the word mishmash, but and then you talked a little bit, you said some of the more advanced models, what what do you mean there? And I think we talked offline about this bonds up stocks, or bonds down stocks down. Just talk through that for a second, if you could.

 

Matt Hanna  41:01

Yeah, so I mean, it’s on the upside, I think we can kind of dive into but on the risk side makes a lot of sense to, like I mentioned before, we’re trying to target that 13 18% volatility. So it’s a matter of, again, kind of that some of the evidence approach on ways to predict volatility, and end up kind of producing in that range. So we utilize a variety of different methods to give us confidence that we’re going to be in that range, but we don’t want to do is undershoot volatility too much. And then frankly, like I said, that becomes a really bad deal for consumers. That being said, there could be times where we don’t want to take on much risk for a variety of reasons. And that could be due to the alpha side. So it’s a good segue to kind of what you’re talking about. A good time, I don’t want to take on risk from an alpha side perspective is when assets are behaving abnormal, and abnormal can be defined a painful, different ways. But a good example, here is January, again, stocks down interest rates up that is quite abnormal market behavior. And typically, your stocks go down and your bonds protect you. So from a quantitative perspective, it’s not just a question of Do I like my model, from a long term back tested historical perspective, that’s great, you and you want to also run it live for a period of time too. But also, when does your model work and when does it not work. And a lot of times, that takes just a quantitative methodologies, but just some knowledge of, of how things are built and why they are built the way they are. And when the markets are behaving abnormal, that tells me that this is not the best time to be taking on a lot of risk, my confidence in certain models would be lower. If if markets are not behaving in a way they typically behave, when things are very normal, then clearly, you can rely on a lot more a lot more of your historical data, then when things are not behaving normal, so So we look at things like that, that that would take risk off the table, just because our off models might not be producing such a strong signal. But it certainly runs the gamut between very sophisticated models and targeting risk to you know, alpha models that kind of gives confidence on other Alpha models to something very simple on, we’re gonna bet on what’s been working.

 

Jeff Malec  43:24

Right. So talk through that a little bit more of the so when I hear normal, but part of me is like, well, maybe that’s the new normal, which is always gets people in trouble, right? But say, say we have 14 of the next 18 months, our stocks down bonds down right rates up, right, which could easily be the case, if we are going to this rate, increasing rate cycle and we’ve had the past 40 years, we can agree right? Anytime stocks were down bonds were generally up rates down. But the next 40 years, if we go from zero to even 7% or something interest rates, you can see that flip. So how to have the models or how do you think about that? Okay, this was not normal, but that normal is based on the last X number of years, not the next X number of years.

 

Matt Hanna  44:11

So but I would say, Jeff, you’d be a great fit on our team, because that’s kind of how we think about it, as well. So it’s it’s not a matter of just looking at one timeframe. One kind of look back period, and slice and dice the timeframes in one way. To your point, if if stocks down bonds or rates up over the next 16 months, you certainly need to adapt. So what we do is not only look at a daily data, going back a period of time, but also daily data going back variety of periods of time, but also weekly data and monthly data and intraday data. So we’re able to glean that information and utilize that information, both on a very small kind of day to day, nuanced view but also more along the lines of a weekly or even a monthly view. So again, kind of goes back diversification of process. So it’s going to hit on your question, if for the next call it month, we continue to see that, that our models begin to learn that, hey, this might be the new normal. And we might not want to punish our other models as much, because we’re consistently seeing this behavior. And then from continuing to see over the course of months, that will become even more powerful, more powerful. So the point where, you know, we’re sitting here in five years, that would be the complete new normal, and that would be the expectation, not that I would expect that to happen. But our models are able to adapt and adapt to different timeframes.

 

Jeff Malec  45:43

And then, while we’re talking about rates up, how do you view, right, if there’s a long bias, you said, both in the equity side and the bond side, so long bias, meaning bond price rates down? So how do you view if we were going into a rate increasing rate cycle? Having that long bias is that you just handle that with duration? Or what are your thoughts there?

 

Matt Hanna  46:03

Yeah, so I mean, we do especially on the collateral side, we own some ETFs, primarily on the fixed income, fixed income ETF. So we have a bias there. On the feature side, again, we can go short. So even in January, you know, rates going up, we made money on US interest rates, because we were short interest rates. So we’re able to adapt depending on what the environment is long term, I would certainly want to lean into even still long interest rates. But that being said, if if rates rise over the next year or two, and I say long biased, that might that long bias might not hold over the next year or two, I’m talking over kind of a longer term cycle. So kind of shorter term, we’re able to be quite flexible. But you know, longer term, I don’t expect interest rates to rise perpetually over the next 510 years, at some point level out. And at some point, the Fed will have the ability to kind of cut interest rates again, and kind of do what they’ve been doing. So

 

Jeff Malec  47:03

yeah, no way of saying

 

Matt Hanna  47:04

if interest rates go up in the near term, we have the tools in our tool belt to be able to combat that.

 

Jeff Malec  47:11

Yeah. And to me, like this whole rates are going back to 15%, or something I just said no way, like the world’s not built for that anymore.

 

Matt Hanna  47:19

Yeah, I mean, demographic trends, certainly don’t favor that. Economic growth trends don’t don’t favor that. So I mean, I I’m not thinking rates are going to go up tremendously. But certainly inflation in your term, can and probably should push it up a little bit higher.

 

Jeff Malec  47:37

And talk to me about that remaining 20% Diversification bucket. I’m assuming there’s some commodities in there some other server? Tell me what’s in that bucket?

 

Matt Hanna  47:46

Yeah, primarily commodities at this point. I mean, that’s an edge we have in the quantity space. Again, these are all futures contracts. So long and short, primarily commodities. That’s the structure of this is very distinct. So kind of going back to your question about certain signals talk to each other, before the combining stage or post binding stage. Our diversification bucket is definitely kind of after, that doesn’t necessarily inform although we do have some commodity signals that inform kind of equity and rate positions. But the models here, they come up with a view on whether to be long gold, or short silver, or whatever it might be. And we’re able to kind of reflect that overall portfolio, once we get that that view that we want to be saved long gold or silver, then we’re able to utilize that information and target that overall risk of that 13 80%. But kind of the theme on how we determine which commodities to long, short, very similar, multi model, multi method approach, trying to figure out what drives commodity prices higher or lower, and quite a few things could do that. And kind of that some of the evidence approach that determines whether we want to be long or short. Sometimes it can be on that side of the book can be a bit more combined, right? So there’s obviously you know, decent correlation between certainly some commodities, but also looking at models that look sa gold independently from say, solver.

 

Jeff Malec  49:14

Guy, but then so if, if that those models say, hey, we want to be long oil, but you’re already long stocks, and it said hey, the correlation is trades rather high. We they’re gonna pass on that. And we’re gonna do it in smaller numbers. So that that net correlation that net positioning doesn’t get too high. Yep, that’s

 

Matt Hanna  49:31

a good point. So if those assets are behaving highly correlated at that point in time, that’ll mean, we still want to hit that 13 80% Vol range, which means we’ll have to take a smaller position size to be able to hit that

 

Jeff Malec  49:47

and then talk just made me think of it on this vol targeting which convolved target so what if, what if you’re in all these positions? Everything’s going great, but it’s going over your targets? Will you reduce existing positions?

 

Matt Hanna  49:59

Yeah, I think that’s an edge, we have to just a lot of this kind of comes from my background at Raymond James, where I got to see a lot of different alternative mutual funds, I think a problem a lot of funds face has been they try to target a particular ball all the time. The reality is, sometimes your model is going to work better than other times, and sometimes to be more confident than other times. So allowing your volatility to float, I think is incredibly important. And that’s what we would do here. So our range is 13 18%. But at any point in time, we could actually be lower or higher than that. And that that comes down to, and this is from, uh, you know, ex-ante perspective, not just kind of looking back, that can be driven off of our signals, or just not very confident, we’re not seeing a lot of alpha sources, that case, we want to actually take a little bit less risk doesn’t make sense to just take risk for the sake of taking risk. And that actually gives us the powder that when we are highly confident in what we’re seeing out there, we’re able to take on even more risks kind of end up in that range we want to be at,

 

Jeff Malec  51:01

right and that. I love that because there was a meme going around a year or so ago of like, well, the correlation this that, like a lot of people missed the part like you need a positive expected return, right? So even if it’s not uncorrelated, even if it hits your risk target or decreases you down your risk target, if it doesn’t have a positive expected return, what’s what’s the point?

 

Matt Hanna  51:20

Yeah. Yeah, exactly. That’s, that would be the the opposite of what you want to do is to lever up and target a high risk at something that’s supposed to lose you money. That’s That’s clearly not, not what you want to do. But also, you know, we all say we have an expected return. But like I kind of mentioned alluded to it, it’s not just your expected return, it’s your confidence in that expected return number. And there’s, you know, we both could have an expected return of seven or eight or nine or 10. But if I’m way more confident in that number than you are, then I should take on a lot more risk, knowing I’m much more likely to achieve that number and say what you would do.

 

Jeff Malec  51:59

Right? Is that a little like Kelly bedding there?

 

Matt Hanna  52:04

I mean, it’s it’s all I mean, everything here happens under the hood. But it’s there’s certainly that level of dynamic Ness. So while yes, that’s certainly true. There’s also times where you know, we do, we don’t want to deviate too much from our volatility range. So there are some guardrails. And the beauty of having so many models and so many methodologies, it’s not likely that we’re just going to sit here and say, Well, we have no view at all, therefore, we’re going to take no risk. And then six months down the road, well, we have tons of use. So we’re going to take infinite amount of risk. So there is a little, certainly some boundaries, we want to emphasize. That being said, it’s important to, to check your ego at the door, you’re not going to know everything at all, time.

 

Jeff Malec  52:52

And then switching gears a little I was going to start with this I forgot. But the looking back at the phone over time, so it was rather flat. Little contrary to what you just said it was rather flat, seven months of 2021. So what was going on there, and then you join somewhere in between. So give us the 2021 history, if you will.

 

Matt Hanna  53:13

Yeah, so honestly, just to be frank, I would say the fund at that time fell victim to a lot of those issues that I would have seen in my previous life doing mutual fund research, the vol target, or fall range was probably a bit too low, especially relative to the feed. So one thing we did do is bump that range up when I came on board. And then also, I think a lot of call it hedge funds, prop firms that want to go to the mutual fund landscape. Like I mentioned before, they’re kind of guarded in their IP, their secrets, their methods. So and maybe a little bit of arrogance to that, you know, Joe Public only deserves a little bit of what we can do. Verse, a lot of or everything that makes sense of what we can do. And that was something I’ve felt very strongly, like, if we’re gonna, if we’re going to give something to the public, they got to get there, our best ideas all the time. And they got to get our best ideas, what makes sense here for this particular product, and that’s where Tessa had multiple methods and processes and strategies before I joined, but that’s something I really emphasized, you know, we have to continue to build grow and put more and more things here. Regardless of you know, we can say it’s typically found just for a private hedge fund quiet. Well, if we’re gonna have a mutual fund, we need to make sure those things are available for our neighbors, our friends, our family, as well.

 

Jeff Malec  54:46

Right and that’s how we talk through that a little bit. Are you allowed to just walk into the main firm say, Okay, I want this I want this. I want this. Like for sure there’s some structural things you can’t do some of the trains that they do on that side you couldn’t go into Yeah, nice. Wow. or something like that, right? So, or real estate or there’s some limitations there. But in terms of the strategies, what what’s that delineation look like you have full realm now you have full Roan, what’s the word?

 

Matt Hanna  55:13

I mean, I view Tessa as a firm, I can think of it as a buffet of PMs and strategies all trying to liver. You know, alpha. And if I’m trying to eat, I don’t know, breakfast. I’m trying to find a eggs, patch browns, maybe some fruit, yogurt, etc. I’m not necessarily looking for a flaming yawn. So there’s certain things that don’t fit with what we’re trying to achieve. But if we brought onboard a really smart PhD somewhere, and he makes kind of the best scrambled egg out there, that fits what I’m doing that I do have the ability to say, hey, you know what, that that makes a lot of sense. We can fit that in here and deliver that to the public. But if we bring on somebody that does something totally different, then obviously, it wouldn’t make a lot of sense for what we’re doing for the mutual fund. But certainly, I’m able to kind of have an idea what other people are doing, if it makes sense. If it adds value increases, the Sharpe ratio, hopefully increases the return helps us target risk a little bit better, we are able, and we do want to continue to evolve and add those type of things.

 

Jeff Malec  56:25

Right? In the in the past, a prop firm would be like we this strategy, this niche we found can only take on $200 million, or something, right? There were some limiting factor to that we only want to put so much money in there. But you’re talking Exchange Traded futures, you’re talking things that. So in that conversation, like, hey, we can take more, we have more capacity in that strategy. Let’s put it into mutual funds.

 

Matt Hanna  56:48

Yeah. And I think that kind of breakfast buffet examples, is kind of pertinent, like, what I’m looking for here is strategies on the Alpha side, that are generally trading around equity index futures, interest rate futures, and either making a judgment between them or, or a certain particular asset within them, and then finding a way to put that in here. If it’s not in that realm, then it’s obviously a harder fit. Now we have that 20% bucket, which certainly kind of opens things up a little bit more. But generally speaking, we want our particular alpha sources to be in that equity index, or interest rate future, you know, ballpark, but I think a good way to put it is I think a lot of hedge funds or private funds come into mutual fund. World, they might say we make the best, best eggs out there as best scrambled eggs out there. Here we go, Jeff, here’s your scrambled eggs. But a you might not want scrambled eggs, you might not need scrambled eggs and be in the mood for scrambled eggs. Right. So I think the importance is from an ego perspective is to recognize that it’s not about the best things don’t necessarily Rican do. It’s also about what you need and what you want, and what kind of fits into your circumstance. So having that kind of buffet solution, you know what that comes together as a great meal a great, you know, breakfast, if you’re not in the mood for our scrambled eggs today. That’s okay. You don’t have to eat that you can eat something else. And ultimately, you know, you’re gonna have a heck of a heck of a meal either way. And that’s kind of how we approach this is we want to be consistently good knowing that sometimes one sleeve or one process might not work, but the other ones will pick up the slack.

 

Jeff Malec  58:28

I love it. I do hate scrambled eggs, by the way. I think we should rename it. The Catalyst has a alpha soars buffet.

 

Matt Hanna  58:38

There we go. That I think that that actually might resonate to some people.

 

Jeff Malec  58:50

Go well, we’ll finish up here ask you for your hottest take. New Segment we’re doing this year somebody can’t believe nobody else is talking about something everyone else is talking about. But they’re wrong in your opinion, or something like Utah football is better than Florida football. What’s What’s your hottest take?

 

Matt Hanna  59:07

Okay, so you mentioned football. For those that have been paying attention. Brian, for us just sued the NFL and a variety of different teams. And from a hot take perspective. I love it. You know, I certainly think there’s a lot of things that need to be improved upon, you know, hiring, but I from a hot take perspective is that’s very high level not controversial. But for those that know the NFL, what he’s really focused on is what they call the Rooney Rule, which is teams have to interview minority candidates prior to making a hire. And I think what’s the end result is going to be some sort of change to the Rooney rule or just getting rid of it altogether. Because like, Chief Justice John Robert says if you want to stop discriminating against On race, you have to stop discriminating on race. And I think what’s happening here. And I think, for us, I had this issue, just the team already decided they love the candidate, and he was a token interviewer. So it’s not achieving what they wanted to achieve. So ultimately, I think there’s gonna be a decent amount of change in kind of NFL hiring practices. And and that’s somewhat pertinent to that, but I don’t know how hot take you that is?

 

Jeff Malec  1:00:28

Yeah, well, you’re kind of saying like, Oh, it a little bit might backfire on florists because they might even just remove the Rooney Rule altogether, like, okay, they’re just going to interview who they’re going to interview,

 

Matt Hanna  1:00:38

which now, you know, I think that’s where John Roberts is from right on track. If you want to solve the the minority hiring in the NFL or really anywhere, it’s not at the end stage or forcing interviews, you have to start way before that in terms of the pipeline of minority candidates, to minority owners to minority GMs. Not just like, you have to interview. I think that’s that’s kind of crazy. Just like if Jerry Jones wants Sean Payton as his coach, any other interview would be ridiculous.

 

 

 

Jeff Malec  1:01:17

But his mind’s made up some of the other salacious stuff in there, like he was gonna pay him 100 grand a game to tank and so yeah, gonna be fun to watch,

 

Matt Hanna  1:01:27

which is wild, when you think about just how gambling is now becoming a bigger part of our pro sports landscape. You know, owner telling us coach tank? I don’t know. Yeah, if our Las Vegas overlords will like that too much.

 

Jeff Malec  1:01:43

Right? Is that reflected in the line? Should it be reflected online? Should it be public information? Right? They’re trying to take awesome mag any last thoughts before we let you go? Where can where can they find you? Where can they find more info on on the fun? Yeah. So

 

Matt Hanna  1:01:59

then I’ll just kind of bounce around a little bit. You know, again, I appreciate everyone listened to my story, the Tesla story, the algorithmic allocation story. I think, for most clients, the way we risk allocate across assets, utilizing alpha and kind of our risk models was a pretty good fit for most people. If you have any questions, certainly, you can reach out to me, my email is matthew@tesla.com. And the last thing I kind of want to hit on, it’s a little different topic. But none of you probably know this, but I actually have hearing aids and I’ve struggled with my hearing all my life. And especially I usually don’t bring this up. But in the era of masking, if I had to be in school these days as like a fourth fifth sixth grader mask, I would not be sitting here today, due to my hearing. So just be kind to others. You know, it, it might help in terms of spreading disease, but there’s certainly consequences. So speak up, listen to people help people out, you know, behind. You never know what issues other people are going through. And that’s the end of that.

 

Jeff Malec  1:03:07

Amen. Amen. Um, be can speak up. Tell my kids, I’m going to tell my kids that story and say, Hey, this is why I need to speak up.

 

Matt Hanna  1:03:16

Show important. Speak up Look, someone

 

Jeff Malec  1:03:18

in the eye. I met. So good to see you, hopefully next time on the slopes in Utah.

 

Matt Hanna  1:03:23

Yeah, sounds good. I appreciate the opportunity and have fun and stay upright.

 

Jeff Malec  1:03:29

Alright, thanks.

 

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