#40 Rajesh Amin - From Data to Performance

The Nalu Finance Podcast

Join us in this Nalu Finance podcast episode as we speak with Rajesh Amin, Founder of 2RSquared, on how to harness the overwhelming amount of growing data to create efficient and effective investment solutions.

• Learn how advanced front office systems enable smaller and medium-sized firms to compete.

• Discover how technology enables personalized investment products on a large scale.

• Understand how to choose different risk metrics based on the client's utility and risk perception.

• Realize why human involvement in research remains crucial.

🎧 Listen Now On: Apple Podcasts | Spotify | Youtube | Podomatic

🎙️ Transcript:

Stefan Wagner: 00:42

Good morning. I'm here with Raj, co-founder of 2Rsquared. 2Rsquared is quite an exciting company, I believe. That's why I asked him to come on to the podcast. 2Rsquared basically helps investors and investment managers to achieve better performance. That's a very easy thing to say. So I think, Raj, over to you. What is 2Rsquared and how do you actually help your typical investor to do this?

Rajesh Amin: 01:11

Okay, perfect. Well, Stefan, thanks very much for the opportunity to be on the podcast. So 2R Squared essentially is a technology company. And what we're trying to do is to offer the investment community a new front office toolkit, which is a SaaS toolkit for the digital design, the implementation, and the lifecycle management of investment strategies and indices. The objective of why we've done this work is to be able to make personalized investment products both possible and practical on the very large scale. And that's not just for sophisticated investors, it's also for the wealthy client base, so a whole spectrum of customers. Now, I'd like to stress, people often get confused. We are providing the technology for our clients to build these investment propositions. We're not building those indices or strategies ourselves.

Stefan Wagner: 02:04

And when you say clients, who are sort of your typical clients that you have found you can add value to?

Rajesh Amin: 02:11

So right now, our focus has been on investment banks in terms of the custom index space, very much for their production of, let's call it, personalized investment strategies delivered via certificates or actively managed swaps. but also in the wealth market in terms of providing a personalized aspect to the different, let's call it model portfolios, but also in the higher net worth space, the design of, let's call it thematic ideas or concepts which are being driven by what the client is finding interesting.

So those are the two main areas basically where you have a direct relationship with the end investor and you can actually interact with the investor in order to actually build or design or customize an existing product.

Stefan Wagner: 02:59

Thematic is definitely something we see constantly, is something that helps if the investor understands what the theme and the logic, either because of his own personal preferences, but also what he believes and what he sees out there. That definitely builds a lot of stuff. Let's go a little bit more into what are sort of the three top benefits for using 2Rsquared.

Rajesh Amin: 03:23

Yeah, so we're a technology company, and what we've seen in other industries is that technology is having a profound, massive impact on how those industries are evolving and how they're serving their customers. Now, in our context, the key kind of technical innovations are to do with data. It's the ability to rapidly store data, and you can access that data very quickly. And importantly, you've now got the compute power, the sheer workhorse, to be able to use the algorithms, complex algorithms, analyze and utilize that data, plus of course you've got the internet and you've got all the digital channels which you can use then to get the analysis through the computation of the data to then personalize the customer experience.

So these three components, the capacity to store and retrieve data, the computational power, and the digital channels can really change how the investment industry can actually move forward. My view is that this is definitely changing what people want, especially when you have this transfer of wealth from the old generation to the young generations. So I think that the industry hasn't really taken this on board yet. I mean, of course, there are very large players in the industry that are able to harness technology, no doubt. They've got the resources, they can build the stuff, they can get the infrastructure.

But so far, what we've seen is that that focus of that technology has been very much in the business as usual, using it to try and help the investment process to try and generate alpha. Of course, that is a critical use case, but it's a use case in the current model of delivering commingled funds where the investment process is one design fits all. It has not been used to evolve a personalized digital service.

So how I think that we can help is in two kind of angles. So the first one is, yes, we can help smaller and medium-sized firms who cannot invest the required money to move the needle in a tech perspective to improve the alpha. We can help by providing the technology, let's call it the advanced front office system, where effectively what you're doing is pulling resource, right?

Lots of small players effectively pulling resource in us so that we can keep up to date with what the behemoths are doing. So in that process, we're allowing you to keep up with generating the alpha so that you don't, over time, have a drag, a negative drag, which can basically make you, like, you know, investing is a zero-sum game. Whoever wins, it has to be an absolute loser.

And if you have a drag because you can't use data to make the investment decisions in the same way or effectiveness as the bigger players, you will be subject to that drag. So we can help you there. But our belief is, is that, and this is maybe a little bit controversial, that just competing on performance is not a persistent way to compete. There's strong evidence looking at things like the speed of a call card that, you know, persistence of top quartile managers is kind of low. So our belief is, is that we can help companies improve what they do from their own bottom line and for their clients by actually moving to a much more personalized model. And we believe this allows you to have a much more persistent performance vis-a-vis your client.

Stefan Wagner: 06:32

Yeah, that's something I want to jump in here. If you personalize something like this, obviously, it won't be the best performing or beat the benchmark all the time. What are the factors that then come into play? The risk perception or how people measure performance? What are the measurements that are used at that point in time?

Rajesh Amin: 07:00

Well, I mean… There's a whole range of measurements and everybody has their own utility. So in our kind of framework, we have a whole variety of different measurement techniques. And it's very much the client that will kind of decide based upon their utility, what do they give weight to? I mean, in our system, you can design the investment strategy, you can build different kinds of risk management into the system, but in the end what's happening is you're able to then analyze the actual strategy, how it would have performed in the past through back tests, of course, and current composition, and you'll get a whole range of different, let's call it metrics, which you could look at.

So clearly there are things like stability of return, looking at things like rolling returns, rolling volatilities, and so on. You can have measures which are tracking versus benchmarks, so concepts of turnover, tracking error. But you can also have a look at, let's call it the allocations and where performance has come from, sliced and diced by different factors. So for example, reference data properties or time series values like ESG scores.

And, of course, you can look at the portfolio exposure over time to different properties, like third-party data or signals that you yourself have calculated, like factor scores. So there's a whole range of these different metrics that you can use, and the question mark is, well, you know, depending upon if somebody is more risk-averse, they're going to be looking at drawdown more. If people have got a longer time horizon, they're going to be looking at, you know, what the more the potential upside could be. So it's a difficult question in terms of there's no one-size-fits-all, I think, in this concept.

Stefan Wagner: 08:32

Then I probably have another difficult question for you. I wake up in the morning, I have an idea, I sit in front of your tool. What are the typical challenges when you're designing an investment strategy? And maybe you can combine that with an example of a product development you actually thought was a good idea.

Rajesh Amin: 08:49

Again, the technology that we provide is providing the asset manager, the wealth manager, the capability to design their own investment strategy. Now, given it's broad, right, you know, you have a whole bunch of things that you could design. You could design a thematic idea, you could basically be just taking, you know, doing something that's quantum mental, or you could do things like risk premia, all these different things with different methodologies. So how you design and develop these depends upon what kind of strategy it is you're trying to develop. So let's take an example. If you believe in risk premia, then you'll be looking for signals which show some form of long-term outperformance versus the benchmark.

And to do that, then you, to identify which signals might be alpha generative, you're going to definitely be looking at the back tests and I guess, you know, assuming that there is a positive backtest and you're going to take that as an indicator that that is a positive signal. Now, on the other hand, if you're looking at a new theme in a thematic, let's say, that you think is going to drive the industry, there would not have been any historical performance yet because it's kind of been irrelevant. So they're looking at a back test is useless. So, you know, the way that you design these things has to be tailored to what it is you're trying to achieve. Now, our tech is flexible. You can build whatever you whatever investment thesis you wish to.

So if you really tell me like, you know, okay, what are the what are the very, very, very, very, very basic principles that you can apply? This now comes very much my own personal view, I'm sure others will have a different view. But it comes down to large numbers of small independent bets. And I'm going to say that always because I come from a court investment background. So as opposed to a discretionary background where maybe you think you have a small number of very high conviction bets.

My problem is that I never know what high conviction means because I personally believe most bets in the market are something like 50% plus epsilon correct, 50% minus epsilon incorrect. So you're really on the edge of noise. So I don't believe in high conviction bets as a general concept. My general advice is to make use of computational power. to apply the filters and identification techniques that you think are relevant to the wider set of instruments to get the largest possible set of positive probability names and then try and ensure diversification to ensure your idiosyncratic risk is minimized.

Stefan Wagner: 11:09

I mean, it's funny what you just said because I've asked this quite often in some of the interviews to people, sort of, what is the difference between bets and investing? You're very comfortable using the word bets. I think many people think that's a bad thing and then putting things like high conviction on top of it to say now it's an investment. I don't really distinguish between both sides in a sense, and that sounds like you are the same way. I mean, there's a huge, you touched on this already, there's an exponential growth of available data that basically acquires an efficient. And the question often I get asked then is, is there actually still any added value for human investment in the investment process? Or are we basically looking into something where technology will take care of everything?

Rajesh Amin: 11:57

No, I don't believe technology will take care of everything. I mean, I think it's a big stretch of the imagination to assume that models will replace people. Because fundamentally, markets are not stationary. You interact with them, you change them. And most of the actual models that are employed, you know, in let's call it AI at the moment, they work very well when there are stationary distributions which are matching, but you can train on it. There are some people who will make it, but you're going to have to be spending massively on research to actually get an edge to make this happen.

Stefan Wagner: 12:34

I always like to ask people that one particular who have had experience with managing risk in the senses. What is in your view risk perception? How do you define risk in or can you define risk particularly within your strategies and maybe also personally what you what metrics is you have looked at that you found useful?

Rajesh Amin: 12:56

So I mean, I highlighted a little bit earlier that I think that that perception of risk is kind of very personal. So everyone has their own utility function. So according to that, different people will have a different view. So the way I think about risk is that outcomes are generally uncertain. We talked about how to try and narrow that through diversification and small bets, but also what you can try and do is you can try and put in place like different forms of, let's call it adjustments in your investment process, which try and shape that distribution so that the things that you don't like are less likely to happen, never going to be guaranteed. So for example, in the context of risk management, And what we try and typically do is have risk management there to handle the tail scenarios rather than just, you know, the 85-95% business as usual.

Because in those tail scenarios, usually what happens is everything is correlating. And so to manage that kind of correlation risk, the change in the behavior of the, this is where diversification typically fails, right? So you need to have something which kind of helps you there. And so what you can do is, at least the way we designed it, is that people can incorporate different kinds of risk management into their strategies. So you can have risk management at multiple levels. You can have it at the instrument level, you can have it in the design of the portfolio, and you can have it as an overlay. So for example, at the instrument level, we've seen, for example, with risk parity strategies, people have started to put time series momentum in terms of the underlying allocations. So that's a way of modulating the underlying instrument exposure based upon things like performance.

Of course, risk parity doesn't look at performance, but if you put it into the actual you know, the instrument level, then you've got some risk mitigation at the instrument level. You can also have in the portfolio design. So for example, if you are looking at a kind of direct indexing example, you can constrain behavior versus the index by simply constraining deviation versus the benchmark, you know, active weight, secondality, and so on. You can also have portfolio risk management, like the overlay, by, for example, having volatility caps, recognizing that, you know, especially if you organize that volatility to be looking at downside volatility, which is, you know, of course, we care about here, that, you know, in stress situations, downside volatility increases.

And so if you have volatility caps, you are naturally decreasing your exposure. And you can also have it in the way that hedge funds do it, which is in terms of stop loss, so explicit functions of drawdown. So there are all these different methods of being able to look at managing risk in two different scenarios. Lots of small bets, kind of independent, for business as usual. Recognize that under stress situations, things correlate, so your assumptions are going to shift. And then you can try and put in exogenous aspects that kick in occasionally, not very often, to try and manage that left-hand tail. That's kind of like what the typical pattern is.

Stefan Wagner: 15:45

I mean, from all your answers, you clearly are very passionate about what you're doing there. What was sort of your personal motivation and drive?

Rajesh Amin: 15:59

Just from my own personal finances, and actually now having gone through and talked to many other people, I think a lot of people, the individuals and NASA owners frankly, feel like they've been underserved by the investment community. And we wanted to provide a technology to reverse that. So what do I mean by underserved? Well, in a conversation recently I had with someone, they said that a person knows they need a car, but no one's ever said they need a financial product. But actually, I think they do.

And I think people are aware they need a financial product, the main one being a pension and saving for the longer term. But despite knowing this, there's still a lack of engagement. And I know why I had a lack of engagement when I was working. It was simply down to, you know, you give your money and you get this intermediated from your money.

Stefan Wagner: 16:44

I would definitely agree with that. I think most people clearly spent more time researching their new car than their new investment.

Rajesh Amin: 16:52 But I think that's because that is intermediation. When you hand it over, you're not involved anymore. And on top of that, it was often been hard to get information about what's happening to your money. So that's not a process that makes people interested. There's not the information for them to do the research. So I think, you know, one of the key things that we're trying to do is to be able to solve that information gap, but also allow people to get involved in their investment journey. And we think part of that involvement in that investment journey, to get them interested, to get them motivated, is to actually have them involved in the design, right?

So what are you thinking? What are your objectives? You know, you talked about ESG, that's clearly an angle that's kind of important, that's important. There are, you know, people will have their own views from their own, you know, understanding of the market, what could be an interesting theme, for example. Being able to actually design your investment strategy in a sensible way using proper tools that the asset market industry use, either doing it yourself or in conjunction, of course, with an advisor or a product specialist, this is how you get the clients involved.

This is how you give them the information so they actually spend more time looking at how to actually make the assets that they've worked so hard to accumulate work hard for them. If there's an information barrier, you're just going to be bored and disengage. I think that's the reason why people spend more time looking at their car research than their money research. But what's more valuable? You spend your life, how much time do you spend working in order to build assets? And then you're not working your assets. That's really bad. And that's what we're trying to solve.

Stefan Wagner: 18:23

Now, if people actually want to engage with you, what's the best way for interested parties to get in touch with you?

Rajesh Amin: 18:30

I mean, we have a website 2RSQ.com that has a contact form, or you can contact us directly by email, contact at 2RSQ.com, or I'd be very delighted to have conversations with people via LinkedIn. So I'm on LinkedIn. Rajesh, I'm in.

Stefan Wagner: 18:45

Last question that I ask these days, every of my interviews, and I don't give them any pre-warning, is what's your favorite music right now that you like to listen to?

Rajesh Amin: 18:57

Oh, this is a little bit boring. Okay. I went to an old colleague of mine, and she organizes not so well-known musicians to come to her apartment and invites people. And you have a very nice, cozy environment where you can listen, literally, yeah, very intimate, literally, you know, a few feet away with, of course, she's French, beautiful wine, beautiful food. And it's a really nice surrounding. And the musician was playing Chopin's Etudes. No, I'm not a big classical music guy, but I'm not going back to this every time I'm working and I have a difficult problem to solve. I listen to this and I look at the complexity and listen to the complexity of the piano playing. And it makes me think, well, what I'm doing is not so hard after all. So that's my favorite at the moment.

Stefan Wagner: 19:49

Excellent. Raj, thank you. Thank you very much for taking the time. It was very insightful. Thank you.

Rajesh Amin: 19:55

Thank you. My pleasure. Thanks, Stefan.