118: Manoj Narang – Trading Technology, Alternative Data and Originality

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Ladies and gents, you're listening to episode one hundred and eighteen of Chatwith Traders Podcast. If by chance this is your first time listening, then welcome. I'm your host, Aaron Fifield. Joining me this week is a very special guest. High speed trading veteran Minaj Narang. Minoj worked on Wall Street for the likes of Credit Suisse and Goldman Sachs before venturing out onto his own to found trade work. which became one of the larger trading firms in the US in terms of volume.

He's since parted ways with TradeWorks to start Mana Partners. Mana is a quant fund which raised almost one billion dollars for its launch in January this year, twenty seventeen. As a brief summary for some of the things we got to chat about, the value of the actual technology which drives some trading operations. Capitalizing on the explosion of data and my favourite part from all this, towards the end when Minoj gives advice to aspiring traders.

Now I understand this particular episode may not be for everyone. And if some of the things discussed are a little beyond your understanding, I've actually included a few links for you as further reading in the show notes at chatwithraders.com slash one one eight. Also I'd like to mention I'm heading to New York at the end of April. I'll be attending and also doing an interview at Quantopians event, QuantCon.

So if you're going to be there, please let me know. Or if you're going and don't have tickets yet, just hit me up and I'll make sure you get a discount. I'm also going to be doing a bit of a chat with traders event slash get together while I'm there in New York, which is going to be really cool and a little bit special. I'll have more details around this by next week.

But just wanted to give you the heads up for now, so please stay tuned. And now let's cross over to the conversation I had with Minoj Narang. It's been a long time coming. Let's just hit record and uh get this going. So Minaj I've I've gotta say straight out of the gate you've probably been one of my top five hardest guests.

to get an interview with. So it's good to be finally speaking. Uh you've been busy. What's been going on? Well, turns out launching a uh a a new quant trading initiative is uh It's a lot of work. So you have just launched Mana Partners. That launched in January uh twenty seventeen. What does that feel like? Is it a relief?

Um it's not a relief. It's uh very exciting. We still have a lot of work uh ahead of us, but you know, we're very optimistic. And since you decided that you were actually going to launch this fund, Mana Partners From that moment when you had the idea, when you actually decided this was something you were going to go ahead with, up until the point of when you launched earlier this year.

How long is that that period of time? Like how much work actually went into this? Like give us an idea on sort of the the scope. Well, so I left my previous firm, uh, TradeWorks, in January of twenty fifteen.

And I had a six month non compete. And during that time, you know, I had uh tons of different ideas of what I would like to do down the road and I um I thought about what it was that I wanted to do and I couldn't really I couldn't really narrow it down to one specific thing. And so, um, you know, towards the end of of that year I sort of decided that

you know, quant trading was the way to go. Uh, mostly because I've found quantitative trading to be a very effective means of producing intellectual property um that you know, has a lot of value. And so You know, I've I've always been fascinated by the ability of this business model to generate IT um that could be commercialized in a multitude of different ways. And, you know, I had a lot of success with that. um at tradeworks, uh commercializing various uh kinds of technology that

you know, came out of uh the trading business. So, you know, for me then it was uh basically, you know, experimental, but having seen firsthand uh the the fruits of that effort uh pay off. you know, pretty much all in on that concept uh at this point about using uh trading, uh quantitative trading to

catalyze and inspire the growth of new technology and also frankly to to uh fund its creation and uh battle test it to an extent. And then you know my uh W w one of the things I've learned in my career is that is that, you know, the technology or the IP that you build uh in the course of um operating such a business uh has significantly more value than the PNL that you can extract from the markets. Uh

just from quantitative trading alone. So uh that's really what Mana Partners is about. We are basically a incubator of new businesses. that uh leverage the firm's IP. And in that calculus, certainly um quantitative trading has primacy in that it is the catalyst and the inspiration for uh a lot of the other uh businesses

based on the technology that gets created uh to satisfy the needs of of that core business. So that's that's essentially what we're looking to do. And, you know, in the process we believe we're the first firm to bring uh high frequency trading to the uh asset management investor base.

high frequency trading strategies have been operated by proprietary trading firms using their own capital. And to the extent that they've needed to out access outside capital, they've, you know, uh they've gone the venture capital route or the private equity route. or the angel capital route. Um the business model historically has not been conducive to the asset management sphere and, you know, generally asset management investors are not even familiar with or comfortable with.

some of the key precepts of of high frequency trading. They're not familiar with the performance characteristics. They're not familiar with they're comfortable with the idea of double digit sharp ratios. Uh traditional notions of capacity of quantitative strategies don't apply.

So it it definitely is an innovation as far as business model goes, but uh it goes much further than just uh bringing high frequency trading to the masses. We we essentially want to do that in a way that allows us to uh you know, turn technology into a profit center rather than a cost center. I'm really keen to talk about Mana Partners and some of the things you're doing there in much more detail very shortly. But before we get into that,

Before Mana Partners, before TradeWorks, how did you first get involved in trading or even financial markets for that matter? Like where did you start out? I started out at first Boston uh before the uh Before it was uh rebranded Credit Suisse First Boston. Uh that was in nineteen ninety one. So I worked on their equity derivatives desk and working on uh program trading systems and so I was a technologist, a front office technologist. uh supporting the tr uh program trading test uh directly.

And um, you know, I floated around Wall Street for a while in a variety of different roles. Uh initially technology and then more on the research side and then ultimately trading. And uh so, you know, had roughly a uh eight or nine year career on the street uh before uh launching into my own entrepreneurial activities. To get those positions that you did have on on Wall Street, I believe you had an education in uh math and computer science.

Was it your motive to get educated in those things purely to get a trading job or how did that kind of work out? No, I didn't know anything about Wall Street the entire time I was in college. I didn't even know it was a thing. Well, I mean I knew Wall Street was a thing, but I didn't know it was a thing that somebody like me uh could have a productive uh career there. And um, you know, in point of fact I never took a class really uh that applied directly

to uh to my work. So, you know, I never took a statistics class I never took a finance class, I never took an economics class. So, you know, it was really all just learning on the job, um, essentially. Okay. So were those things helpful for you in any way? Well computer science definitely was. Uh the kind of math that I studied uh was very theoretical, you know, um things like abstract algebra and and so forth. So those

You know, I haven't really found much of an application for it, but certainly the theoretical computer science aspect of it, algorithms and you know, graph theory and uh things like that. You know, generally speaking, um being able to reason quantitatively. It is the skill.

that that you're after. It's not really any specific technique they they teach you or anything like that. So yeah, I mean I think that regardless of how you get it, the ability to to reason quantitatively, um and to think logically. Um is important. You know, problem solving is is a very important skill. When you say reason quantitatively, what exactly is that referring to? Well, this is a uh a business um which is also known as algorithmic trading and

So, you know, building a trading algorithm, you know, is is a quantitative challenge. It's a uh you know it's it's a puzzle um that involves many moving parts and many m moving pieces and there's all kinds of uh mathematical challenges, there's all kinds of engineering challenges, there's all kinds of algorithmic challenges and

you know, um, people don't naturally have that skill set. That that skill set needs to be incubated and cultivated and you know generally people who pursue science and engineering degrees um have a chance to to learn those types of reasoning skills.

Okay. Minoj, tell us how TradeWorks came to be. You said you did about, I think it was about nine years on Wall Street. Uh then you left to start your own trading firm, which was TradeWorks. What was going through your head at the time? Um, what was sort of the motivation to start TradeWorks and and what did you sort of set out to achieve?

Okay. So before I start, uh just wanna let you know that uh I would like to speak to you as openly uh as I possibly can about trade works. However, I do have a separation agreement there and I'm not allowed to disclose any non public information about the company. you know, I no longer work there and I am bound by um you know, an obligation to protect the company's confidentiality. So uh I can speak to why I started the company. That's not their their confidential um

information. So I'm happy to to do that, but I just wanted you to be aware of that. Um that I may not be able to answer every question you you ask me about them. So Essentially, you know, in the late nineties I was at Goldman Sachs, which was my favorite job on Wall Street. You know, I learned more there in a year than I'd learned in my entire career up to that point. And uh it was just a great place to work with some phenomenally smart people.

And that said, you know, um I was getting to the stage in my career where uh you know I'd learned enough that I started having a lot of creative ideas for how I could apply that skill set outside of just quantitative trading. I sort of had uh gotten a little bit burned out. I was working very long hours at Goldman, you know, I was doing a lot of trades in an era before electronic trading.

And so, you know, there's a lot of manual trade entry and a lot of trade reconciliation, which was also manual, and then at the same time a lot of coding. And um, you know, it was just very, very exhausting. The markets weren't electronic yet and so basically you had to trade manually over the phone and enter your own trades and so And then on top of that, you know, write your systems and and code and so forth. So I was getting a little bit burned out on the business, but also

you know, taking stock of the fact that many other people were becoming very, very successful at West, starting technology companies uh using, you know, the same skill set. So You know, I felt that there were all kinds of problems uh that I could solve using the techniques that I had learned uh in quantitative finance. And what was very exciting about the late nineties was that

It was really the epicenter of innovation around retail finance. So, you know, online brokerage had just come onto the scene in the late nineties. And, you know, in a matter of two years it has gone from nothing to around a forty percent market share of all retail equity uh trades. And At the same time, because of the internet and the advent of all these financial portals. all of a sudden the the uh the public um and the retail investor uh were were awash in financial information that previously

uh was available only to professional or institutional investors. And so I saw an opportunity there. I s you know, uh clearly there's a lot of innovation going around uh going on um on the market access front, you know, kind of leveling the playing field and making making it easier for self directed investors to access the market at relatively low cost. you know, ten dollar commissions and whatnot.

And at the same time they were uh the the markets were awash with information. So self directed investors, whether they were high net worth or or you know, active traders had all the information that they needed and they had the market access uh to be self directed. But what they lacked was the ability uh to make scientifically sound decisions. So you know, a lot of the processes at automated trading firms are, you know, uh based on uh techniques of

uh scientific disciplines like econometrics or portfolio theory, um, statistics, uh multivariate statistics, uh so forth. So or algorithms or game theory, what you know, however you want to characterize it. Uh but the point is that there are uh scientifically and methodologically sound uh techniques for taking data and transforming it into an optimal decision. And these kinds of techniques are very widespread in quantitative trading.

And you know, I I had started uh using online brokerage on my own. I was a early adopter of the day tech. online brokerage platform, which has since got folded into Ameritrade and then T D Ameritrade um most recently. But I was using that platform, you know, as early as nineteen ninety six.

And it just occurred to me that it would be possible to build uh all kinds of very interesting tools that I didn't see anywhere on the anywhere else on the internet and really, you know, help mainstream investors by running uh you know, but providing tools that, you know, were driven by fairly complex uh methodologies and fairly fairly uh intensive in data.

but, you know, which were presented to them in relatively simple form. So and it, you know, proved to be r very easy for me to to to think of these tools. I would be able to You know, you could lock me in a room and you know, for days on end and I could just keep coming up with cool ideas for tools that would be very, very useful for people. So uh the idea was to to fashion uh a bunch of uh you know dozens and dozens of uh analytical decision support tools.

that would solve very specific and simple everyday problems that retail investors had and you know, then to package these into different verticals that attra uh you know uh addressed different segments of the financial industry.

So that was essentially the idea. And then that was going pretty well uh up until two thousand one, uh when we had the uh you know, the dot com bubble burst and we had nine eleven and uh operating conditions for startups became very, very difficult, particularly in New York and in the financial sector. And so at that point, you know, the company became a hedge fund um in order to stay in business. Pretty much has uh remained that way ever since, although

you know, under my uh under my leadership the goal of the company was not ever to really be the the world's biggest hedge fund. It was really to use hedge fund to uh the hedge fund business model to generate revenue and to generate IP.

which could then be used to to build other businesses. So tradeworks is You know, now the owner of thesis technologies. uh, which has become very successful in a lot of different areas r related to high performance trading technology and uh regulatory technology. So, you know, that that notion

proved to be uh successful and bore and bore fruit. Back when I thought of it, you know, it was a little bit heretical and um a little bit counterintuitive for people to understand why a hedge fund would also be interested in commercializing its technology.

uh, you know, people naturally wondered whether that would cannibalize uh the competitive advantage. But really I viewed it uh from a different lens. You know, I viewed it from the perspective of it would be nice to be able to uh take technology, which is typically a a major cost center at other quantitative trading firms, and turn it into a profit center.

And um, you know, that experiment um proved to work. And so, you know, uh at Mana Partners uh starting with that uh with that premise that you know the best way to incorporate technology into a quantitatively driven and technology driven firm. is to turn the the technology investment into a profit center rather than uh a cost center. So, you know, we look for as many ways as possible as we can to uh monetize the sunk cost

uh behind the inva investments that we're making in technology and R and D and data and so forth. And doing so allows us to really have a much bigger technical footprint than we would be able to afford otherwise. So that's really You know, how this got going. Yeah. So you started TradeWorks in the late nineties and it was a a technol you were a technology company uh providing tools to uh retail

Early two thousands you became a hedge fund. If I understand correctly, it wasn't until around about two thousand eight that you actually uh became active in high frequency trading. Uh how did that come about and why the why the shift? Yeah, I'm not sure how much I'm comfortable disclosing this, but um You know, two thousand eight. Uh it's well known it was a very difficult year for hedge funds. There were a lot of outflows from the industry.

And it became very, very difficult to operate a hedge fund in the fourth quarter of two thousand eight due to the demise of Lehman Brothers and so on and so forth. So, you know, I was looking for other ways to apply our quantitative skill set. And our quantitative pedigree.

Uh in late two thousand eight it just was very, very difficult uh to raise money for for a hedge fund. The the fundraising environment was very, very toxic, you know, at the height of the financial crisis. So You know, that was short lived, but you know, in the meantime it became important to try to see what sort what sort of other opportunities were out there. So You know, late two thousand eight was an environment that was marked by outsized volatility.

And uh given that capital was hard to come by, a natural way, uh, you know, to try to to to leverage the quantitative trading skill set was to do high frequency trading because those kinds of strategies do very, very well in, you know, highly volatile conditions and they don't require a whole lot of capital. uh to run so they can be done with, you know, a proprietary trading business model using uh a small amount of capital. So

You know, that's what we did and uh were pretty much successful out of the gates and grew the business um, you know, quarter after quarter and year after year. And um you know, eventually became one of the uh the larger trading firms in the US by volume. So just so we have a bit of a perspective on this, how much volume were you trading on I guess a daily basis?

I don't think I can disclose that. Uh you may be able to find publicly available information on that, but I'm not allowed to disclose financial information of the company. Are you ready to get serious about trading? Then join Tasty Trade, Investopedia's best platform for options trading in 2026. Stocks, options, futures, and more. Tasty Trade has everything you trade all in one platform. Get low commissions, including zero commission on stocks.

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All right, Minaj, well I know we've only got a limited amount of time together, so I want to be a bit selective on uh what questions I ask you. So I just wanna sort of jump past this a little bit more and hear maybe some of your more recent thoughts instead of focusing on uh, you know, your your journey up until this point. So You made an interesting which you made a few interesting comments right at the beginning when you were talking about mana partners.

Uh, one of those things was that you see the technology of your trading business more valuable than the P and L. Um, I'd just love if you could expand on that a little more. I thought I think that was quite interesting.

I can't really go into stuff that doesn't exist yet, but just uh you know, to kind of give you an indication of what this means, going back again to to TradeWorks, uh when I was there we uh really I I really tried to to do my best to extract the maximum economic value that that we could.

um out of the stuff that we had built. So there were a lot of technologies that came out of the trading desk. So for example the the research platform that I used to build uh intraday trading signals uh for the high frequency trading business. You know, uh eventually I ended up selling that to the securities and exchange commission and they called it MIDIS. And um you know it was used for an entirely different purpose.

But it was the same technology uh that I was using to create intraday alpha signals. uh predictive signals, uh, you know, now being used by uh the market's chief regulator to better police uh market structure and understand the impact of electron electronic trading on on the market. So, you know, that's an example. And similarly, we became very, very adept at cloud computing.

being one of the earliest adopters of the Amazon Web Services platform. That's where all of our tick data was was stored and built all kinds of tools uh around that. And um so, you know, Midas and our expertise in cloud computing really uh positioned us to uh compete for an even bigger project uh called Consolidated Audit Trail. You know, there were a lot of large firms like Google and IBM and SunGuard and

you know, uh, FINRA, um, and various others like Blues Alan Hamilton that uh threw their hats into the ring to compete for that that award. Uh You know, this was a very significant uh financial technology project, probably the biggest uh regulatory technology project ever.

And the the goal was to basically build the largest financial database in history and one of the largest ba databases of any kind ever put together. And we threw our hat into the ring as well. Uh and, you know, I felt that uh having been successful uh in deploying the MITAS system. that um you know that would really give us a leg up on on consolidated audit trail.

And also, frankly, just understanding what the regulators want to use consolidated auto trail for um and why the system that it's replacing, which is called Oats uh which is operated by FINRA was deficient, namely because Oates was conceived of uh and developed prior really to to the uh advent of elec of uh algorithmic trading in any meaningful way. You know, when Oates was invented, uh order life cycles and uh trading algorithms were much, much simpler.

Uh and it was it was in the pre Reg N M S era when market structure was much simpler as well. So You know, in today's market the linkages that you can infer from the Oats system are not sufficient to give regulators the uh explanatory power or the investigative powers that they need. Um to to see what's going on in the market. And so the SEC uh came up with this new system called Consolidated Audit Trail. That is

You know, uh you can view if you're so inclined as as as a natural successor to the Oats system. You can view it as a next generation of the Oats system. designed for the modern era of electronic trading. And I felt that, you know, the tools and the and the expertise that we had built up in house as a high frequency trading firm would really uh give us an edge in terms of crafting a solution. that other larger firms um you know didn't have because they don't do any quantitative trading.

And I also felt that Midas would give us a sort of uh incumbency advantage, namely that you could view consolidated auto trail just as uh correctly uh as a successor to Midas. uh as you could as a successor to to Oates. So I felt that um you know, we really had a good chance to to to win that. And so You know, I really uh directed a significant amount of resources at the company towards competing for that contract. And um, you know, a few weeks ago it was announced that um

that uh trade works and thesis did indeed win that, uh which, you know, was very gratifying to uh to see. And um real uh vindication of that approach. So hopefully that illustrates how, you know, you can use technology that that comes out of quantitative trading for a whole host of other purposes, you know. But uh there are so many remaining untapped areas. in the financial markets. Uh, you know, for competitive reasons I'm not going to

start enumerating them because we're a startup and we haven't um you know publicly announced our intentions in a lot of these spaces yet. But there's a lot of uh very, very interesting and um big time opportunities in financial technology where, you know, uh people who have built world class systems to do quantitative trading, whether it's high performance trading

you know, electronically or whether it's um you know, big data uh technologies or the combination of those two things. You know, the kinds of the kinds of advanced technologies that you have to build to be competitive. in quantitative trading, which is one of the most hyper competitive zero sum matches of uh of competitive talent there is anywhere in the economy.

You know, if you can if you can build systems that allow you to extract a uh a modicum of an of an edge or an advantage in that market, there's a very good chance that that those technologies have application elsewhere. Uh both in finance and frankly outside of finance as well. What would be an example of an area outside of finance?

Well, we live in the era of big data, so you know it's quite conceivable that analytical tools or analytical systems or methodologies that you built originally on large data sets in finance may have applicability uh outside uh finance, whether it's in healthcare or whether it's you know, uh in e commerce or a host of other areas that that uh are very data intensive. Right, right. Very interesting.

One of the comments you made earlier as well is that you sort of you you sort of said that one of the goals of Mana Partners is to bring high frequency trading uh to the masses. And what you meant by that is by bringing it making it um something that's available to more traditional asset managers and investors. So Why do you think that might be appealing for those more traditional investors to invest, um, have the option to invest in high frequency trading um strategies?

I've been one of the more outspoken uh people in the high frequency trading space over the years and I've really done my best to increase the awareness of the public and the press and the policy makers you know involved in regulating the space. you know, just to inc increase their awareness of what high frequency trading actually is. And, you know, been a major proponent of data driven decision making.

And I've also been uh I feel a um you know major voice of reason when it comes to you know the paranoid conspiracy theories that certain corners of the market have about what high frequency trading is. you know, for for much of the public due to um you know, the way the industry is presented in the media and the way that uh opportunistic grandstanding politicians have characterized the space.

um in the way that it's been d depicted by some uh members of the press corps. Uh, you know, the public um has a somewhat negative um you know, uh opinion of the space. And it's not negative in the sense that, you know, it doesn't work. It's negative in the sense that, you know, uh there's a widespread uh impression by

you know, retail investors, mainstream investors, and institutional investors alike that high frequency uh traders enjoy certain advantages that are not widely disseminated. And so there's a question about fairness. Now, I happen to believe that you know, inequality is a natural is a natural aspect of any any sort of capital system. You know, uh capital uh capitalist systems reward skill.

Um, you know, if if you have competitive games and the market is definitely a competitive game, but if you have a competitive game like chess, even if the rules are well understood uh by both sides and both sides have an equal amount of time on their clock, you know, you can't expect equal outcomes. There's definitely different degrees of skill.

And um you know, people are the analogy is that in the markets people are entitled to study the rules of the markets in as much detail as they want and they're allowed to invest in technology. uh to the extent of their capabilities and they're designed to uh that they're allowed to engineer systems that effectively express their skill set.

And, you know, that should be allowed. That that skill. Nobody really complains that Google has an unfair advantage because it has by far the best search engine of uh you know, any technology company, right? uh virtually in every other uh competitive space in our economy, uh skill is um rewarded. It's appreciated. Um it's lauded. And um, you know, the one exception is in financial markets where

you know, uh if you see a discrepancy in skill among certain market participants, people automatically assume foul play or what have you. So, you know, I've done my best in my in my uh career to kind of counter that perception that there's unfair advantages. Um that high frequency traders enjoy that other market participants don't enjoy. Those are very, very ill founded and um, you know, it's a difficult uh

misconception to dispel just because uh the rules are so comp complicated. And you know, another thing that I've advocated for over the years is just a great simplification of the regulatory regime. I think part of the paranoia and All the conspiracy theories, um, you know, have their origin in the fact that there's just too many rules for most people to understand. But

What's gratifying for me is that I I think the single biggest thing that has not helped the case of the high frequency trading industry is that the majority of the firms that are in it are basically proprietary traders. So the only beneficiaries of what they do economically, uh, the only direct beneficiaries are are themselves. There's obviously a lot of indirect benefit in the sense that a tremendous amount of liquidity is being provide provided to the market.

And trading costs have come way down as a result of this industry being in existence. And it's really the the glue that holds a fragmented market together. You know, our market system uh is highly fragmented. That was by design. The regulators did that to foster competition and lower trading costs. But as a result you have a very, very complex

uh regulatory regime where trading happens on several dozen trading venues, you know, both lit and dark. And, you know, high frequency trading very much is the glue that holds that apparatus together. And so it's an important fixture in the market, but, you know, it didn't really do uh much help to the cause of of these uh trading firms that they're proprietary and trading their own capital.

and uh and so forth. So You know, what's what's gratifying to me is that um you know, our investors in our hedge fund, um, the ones that will uh get to benefit from the returns of uh of our high frequency strategies include, you know, pension funds. They include endowments. So In a very real sense. Mom and pop um are going to directly benefit.

uh if we're successful b by virtue of having uh investments in our fund, uh assuming everything goes well, of course. I'm going to continue to make the case, uh, because I believe it very, very strongly that Uh quantitative trading in general and high frequency trading in particular uh provide a very, very valuable uh social service um to the financial market.

But, you know, the fact that our returns uh partially benefit uh ordinary mainstream investors, you know, is just uh another nice attribute to have. And, you know, the flip side of that is that uh this sort of strategy has not previously been on offer for asset management investors. Like I said, um, you know, typically to the extent that high frequency traders raised outside capital. It was uh venture capital, private equity capital because

you know, this sort of business is very intensive in CapEx and OpEx and so you need permanent capital. And, you know, these firms are much more like technology firms than they are like trade th than they are like hedge funds. And so we've changed things around a little bit and we decided to capitalize our business using uh the asset management business model.

And so for the the first time really, um high quality, high frequency trading strategies capacity is available uh through a hedge fund business, which has been very interesting to uh to investors. But there's all kinds of uh tangible benefits from doing so. Um, you know, it's a very capital efficient combination of strategies. Uh it's also a very synergistic

blend of strategies vis a vis uh exposure to market volatility. You know, high frequency strategies tend to do very, very well in volatile market conditions, whereas those could be pretty challenging for more conventional, uh longer term quantitative strategies. So

You know, they have uh very different risk risk exposures, which makes them both uh very synergistic components to an overall portfolio. So there's a lot of a lot of different reasons why this business model is is uh super interesting to people.

Can you speak to us a little bit about the strategies that you are running uh there at Mana Partners and and some of the things that you're doing differently besides the obvious things which we've just spoken about, but Some of the things you're doing different in regard to your strategies. I know you did an interview with uh Business Insider recently.

And one of the things you really kind of emphasized throughout that interview was how you're making use of big data and the explosion of data and that sort of thing. I I'd love to hear you talk a little more about that and how that's actually driving your strategies.

Yeah, sure. One of the major secular trends that's at play in quantitative investing right now, you know, uh quantitative investing has been very, very popular. It's growing in popularity every year. Uh there's there's more and more assets being committed to the space. But, you know, the problem is that the strategies that quants run as a result have become very, very overcrowded.

And that's reduced their performance. Uh, you know, there's a certain natural maximum amount of capital that can be allocated to strategies like this. And so, you know, the overcrowding in the space really presents some major challenges. And in in my view the the reason the space has become overcrowded is because you know, quants by and large, to this point, uh have relied largely on the same kinds of data as each other. So even if your methodologies are a little bit different.

your end results are very likely to be correlated um to to those of other people if they're if you're using the same inputs as them, right? You know, uh there's only so many ways to transform inputs into outputs. So if you have the same inputs and you're using quantitative techniques to generate outputs, chances are your outputs are going to be correlated as well. And so, you know, one of the biggest ways uh to address this situation is by using non traditional data. And there is a uh

uh a huge amount of non-traditional data uh being generated nowadays uh from uh a variety of different sources. You know, so social media throws off um you know, absolutely gargantuan amounts of data uh through the so called Twitter fire hose and and other other places. uh e commerce activity. um is a veritable fount of uh you know interesting uh information that can be mined uh for predictive signals and and trends. Um there's satellite imagery. uh which is becoming inc increasingly popular.

There's, you know, the Internet of Things. Uh there's, you know, the world is now awash in sensors that that produce information that can be analyzed for for patterns. So, you know, my point is that Lots and lots of quantitative information, uh you know, either explicitly quantitative or implicitly quantifiable is available now that has not traditionally been used by quantum. And um so it's opening up more and more of the traditional process.

uh the tr traditional decision making process used by discretionary investors to quantitative analysis and You know, this is part of the overall broader trend uh that we've seen in force for many decades now of increasing mechanization of investing. So You know, you see this.

um you know, in a lot of different ways. You see this in in terms of like outflows from from actively managed mutual funds and inflows into passive mutual funds and ETFs. You see it in terms of outflows from actively managed hedge funds and inflows into uh you know smart data or or or quants.

You know, what all of these things, whether they're index based products that are passive or whether they're active products that are based on trading algorithms, what they all have in common is that they are mechanical and they can be back tested. And, you know, time and time again, uh, academic evidence but also uh you know, uh empirical results. have shown that managers that make their decisions in a discretionary way, in a non reproducible way,

are really just not worth the fees that they charge. And mechanical trading universe has its own spectrum. There's the ultra low fee uh fee side of things, you know, which is basically uh largely focused on indexing and smart beta. And then there's also some of the highest fee hedge funds in the world, which are quantitative. Um and also some of the most successful in the world. You know, by all accounts the most successful uh

hedge fund ever created was Renaissance Technologies, which is no longer even open to outside investors. Well it is through the through through one of their vehicles, but you know, their their original uh fund is now closed and it is all proprietary and uh operated with employee capital.

you know, while it was open to outside investors, it was it was charging exorbitant fees, but still well justified by by its performance. So um you know I think that the fact is that uh investors have seen that if you're gonna charge high fees you need to be able to empirically show that you deserve them and if not, then you need to charge very, very low fees, which again suggests a heavy degree of automation.

uh at a minimum, or just outright passive indexing. So either way, these are all manifestations of a longstanding secular trend towards greater and greater uh mechanization, uh automation of the investment process.

And that trend, you know, shows really no signs of abating. And the latest manifestation of that is that More and more, you know, longer term uh decisions, uh capital allocation decisions that are made by discretionary investors can instead be made by looking at patterns in non-traditional data. And So, you know, not only do we have a

uh absolutely staggering amount of data being generated from all of these different disciplines. But we also have uh you know simultaneous innovations going on in cloud computing and storage to basically uh used both elastic storage and elastic compute capabilities uh to analyze that data on demand. Um

uh and very, very cost effectively. And then uh simultaneously, you know, we're seeing a uh renaissance in this in the space of artificial intelligence. Artificial intelligence has been around for a very long time. He keeps going through um, you know, various rebrandings. The latest rebranding is of course called machine learning or st or statistical learning. But um there have been some really potent innovations uh that that have been made in that space.

So we have all the ingredients. We have we have the raw materials, which are the data. We have the uh analytical tools uh from statistical learning. And we have the um hardware uh substrate to store and manipulate

and access all of this data at low cost. So, you know, to me this pre sages a revolution and a and a new renaissance, if you will, in in the quantitative trading um world. I think we're gonna see uh you know, a new generation of superstar firms that are born in this environment um that make use of of these non traditional data types.

Now it's clear you're very excited about this this explosion of data. Do you think that more data means more signals or When you have more data, are the signals kind of watered down? Like is there just a lot more noise being introduced? Well statistical trading, quantitative trading is largely about noise. So you accept a certain amount of noise. You want to try to maximize across your entire portfolio. You want to try to maximize your signal-to-noise ratio, but you're very tolerant of noise.

What's important is the orthogonality of the signal. How how uh unique is it or how different it is is it from the existing sources of data that you have? And that's really what the promise is.

of these alternative data sources is that the signals that they generate are highly uncorrelated or in other words statistically orthogonal to signals that you can generate from more traditional sources. So that that's that's roughly how you measure the value. Um that that data, uh that you know, new data can add to to your investment paradigm. Yeah, and when you're developing your strategies at Mana Partners, are these purely data driven or is there

Um what's the right word? Are you are you sort of factoring in human behavior? Um is there something more to it than just being purely data driven? Uh they are definitely powered by data. Um but you know, you can't eliminate humans from the equation. Uh we're nowhere near uh you know, the state of the art is nowhere near uh that being a plausible reality. So, you know, human beings still are the ones who Uh describe the investment strategies. And really what it's about is automation. We are

uh at our firm, very highly seasoned uh investment uh practitioners. Collectively at our firm we have over two hundred years of quantitative trading uh background amongst our, you know, forty five or so employees. And, you know, we've we've uh got people here who are very, very seasoned and accomplished in a lot of different uh uh asset classes, whether it's fixed income, whether it's equities uh domestically or internationally or in emerging markets, or whether it's uh volatility.

or, you know, uh futures or what have you, uh, you know, we're very, very well covered on all these different asset classes. And so um a lot of what we do is essentially attempt to automate the uh investment process of discretionary investors. So Um you know, automating The process comes with a lot of tangible benefits. Um number one, it makes your investment process repeatable, but it also makes it backtestable. We can basically go back and see how our strategies would have done.

uh in historical market conditions and in which conditions they did the best in and uh how to ameliorate the performance in situ in in in cases where they didn't s do so well and how how to address the deficiencies. But you know, ultimately real changes in prices of securities, um, you know, permanent changes in the prices of securities are driven uh almost entirely by human beings that are making investment decisions. They control the vast majority of the capital in the world still.

And so quants as a whole still don't control that much capital in the grand scheme of things. And more importantly, they invest for short timescales. So for example, a quant that's investing with a one week uh holding period just uh by definition, they're buying exactly as many shares of every stock as they're selling over a one week period. And so over a one week period

you know, their net market impact on a on a stock's price is precisely zero because they've taken the same number of shares out of the market as they put into the market. So during that week, you know, they they they're buying

uh may have caused the the price of a stock to go up and their selling may have caused it to go down. But in the aggregate, uh when you look at the sum total of their activity in the aggregate over their holding period, it's zero. And so what that tells you is really w uh very long term investors are the ones that that dictate.

where securities prices are are heading. And so to the extent that you can attempt to understand how long term investors reason about their capital allocation decisions and automate that decision process. To me, that represents a very fruitful approach. Um so although our strategies are driven by data, they have very strong structural priors, if you will. Um to to uh Wh which which allow us to to harness the data.

um in a very structured way. So that's particularly important when you're dealing with these new data types because the fact that this data type is that these data types are unstructured or uh you know very novel does not automatically make them conducive for statistical learning. Statistical learning or nonlinear uh methods in general require mar uh more far more data Uh to calibrate and to estimate

than traditional linear models, which have been around forever. And so you need a lot more data. And you know, the thing about these new data sets that are coming out is they have very limited histories and very limited time series over which you can train these kinds of models. And so It becomes that much more important to have a s a strong uh structural belief system uh in place.

uh for how the market works and to essentially use the data to strengthen or weaken your hypotheses about how the m how the market works rather than f to use the data to build your entire model for you. I think that is

a less fruitful approach and it's also an overcrowded approach. You know, I've made the point publicly before that there are uh are a very large number of people out there with data science pedigrees and data is universally available and so you see a ton of people building models who don't actually know anything about how the markets work structurally. And so they all end up building the same thing. Right. Again it's

uh the same set of inputs and the same set of methodologies, obviously they're bound to produce highly correlated outputs, right? Regardless of of who's doing the analysis. Our approach is a little bit different. Our approach uh incorporates an awful lot of very sophisticated understanding of what kinds of strategies other people run and in what situations. And we basically use data to refine our models and to calibrate our models, much more so than to tell us

how the market actually works. Now Minoj, I know you've got a run in a minute, so let me just ask you this one last question. What advice would you like to leave for aspiring quant traders? Well, I think that there's an awful lot of people out there that I would view as very, very bright who

You know, are very good at learning. They're very good at school. Uh, they've done very well at school. They they they come from top universities and have excelled there. And these are the the sorts of people that quantitative trading firms hire, you know, like um very, very skilled engineer engineers and scientists and mathematicians from from top universities. And success in quantitative trading uh or in investing in general

uh essentially means being willing to buck the trend. It means doing things a little bit differently from how other people are doing them. If you're just trying to copycat, you know if you're trying to learn what other people are doing and just copy it. um you're gonna run into the fact that they've been doing it for longer than you and they understand it better than you and are entrenched and have economies of scale.

And so it's very, very different. Uh it's very difficult to kind of emulate a successful business case. Um and study all these successful business cases and say, you know, I want to start a company exactly like AQR, or I wanna do what AQR is doing, or I wanna do what D. E. Shaw is doing, or I wanna do what Rentech is doing. To me, that's the the

greatest temptation that people have, but it's also uh the least likely to succeed. I think that you're likely to succeed or much more likely to succeed if you have original ideas, if you're willing to think out of the box. if you're willing to look where other people aren't looking. And, you know, that does involve some amount of understanding how um other people do things. But it also involves kind of like being able to think freely for yourself and not focusing so much on that.

That would be my advice, you know, uh definitely learn about the space, learn about the techniques, et cetera. But, you know, uh don't try to copycat what what other people are doing because it's gonna be very difficult. uh especially if they're very, very successful for for you to compete with them in in in their in their wheelhouse.

I really love that advice. That's awesome, Minoj. I just want to say thank you very much for coming on the podcast. You know, I know it's been a little bit difficult to squeeze in and we've been trying to set this up for a long time. So I'm I'm really stoked we got um the chance to do this. Where can listeners go to find out more about you? I don't know. Google. There's a there's a lot about me out there. What's uh Manna Partners website? That might be a good place.

It's uh www. uh dot mana dash partners dot com. M A N A dash partners dot com. Minoj, once again, truly appreciate it, man. Thank you very much. My pleasure. You've reached the end of this episode of Chat with Traders, but rest assured there are more episodes.