#29 Richard Waddington - Enhancing returns through data-driven decision-making

The Nalu Finance Podcast

In this episode of Nalu Finance, we sit down with Richard Waddington, founder of Sherpa Funds Technology, to explore how data-driven decision-making can enhance investment returns.

Richard dives into the mechanics of systematic portfolio improvement, including:

• Why optimal risk sizing is essential to long-term outperformance
  

• How data can define and refine portfolio exposures
  

• The importance of understanding model and process limitations
  

He also shares lessons from advising multibillion-dollar asset managers across Asia, Europe, and the U.S.

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

🎙️ Transcript:

Richard Waddington: 00:01 When a portfolio manager has to build a product, expecting them to be able to do everything we've just been talking about in their head, and to get a better than 50th percentile output is a kind of ridiculous concept. I mean, it's just not possible.

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Stefan Wagner: 00:52 I'm here with Richard Waddington who is the CEO of Sherpa Funds Technology here in Singapore. I would like to talk a little bit about what you guys do here. You know, you say optimal risk sizing and portfolio management. I think it's one of the subtypes, but can you maybe start with and explain in sort of a couple of sentences what Sherpa Funds technology is and how it, you know, what type, who do you help and how do you, how do you achieve it basically? Just as an intro.

Richard Waddington: 01:20 Yeah, so as an intro, first up, hello, everybody. As an intro, our raison d'être is really helping people with data-driven decision making. And that's quite a broad topic. And of course, it's fairly well known in many other industries. But in investment management, particularly at the portfolio manager side, rather than the asset selection side, data-driven decision making is quite rare.

And so when we talk about data-driven decision-making, I kind of deliberately use that phrase rather than optimization or anything sort of mathematical, because it's not about having some fancy tool or some clever math, both of which we have. It's about helping a client with a process that helps them be better at what they're already quite good at. So data-driven decision-making, allowing them to take decisions around how to build risk-taking portfolios that makes the best use of their skills.

Stefan Wagner: 02:23 Okay, that's quite an interesting best use of their skills. So is there an example how you can maybe showcases a little bit. So it's quite abstract right now.

Richard Waddington: 02:35 Yeah, it's quite abstract. And allegory is the thing I always talk about, which is it's an investment portfolio. And we really talk about institutional investment portfolios. So pension funds, let's take pension funds, mutual funds, this type of thing, rather than wealth product. When you talk to people who manage money, third party money, a lot of them will talk about line item risk in their portfolio. And so you can say, hey, what's going on in your portfolio? And they go, oh, I've got Apple or Credit Suisse or whatever, because this is what interests them. They are at heart analysts, and they have years of experience of analyzing.

So they are thinking, and this is my analogy, they are thinking of the portfolio as a salad A plate on which lots of different things sit next to each other. And they talk about over here, we've got the carrots. And over here, we've got the beetroot. But when you invest or any investor in a pooled asset investment product, you're actually buying a soup, not a salad. What you care about is how the thing has been put together.

Because the experience you have as an investor is the experience of the whole thing at once. It doesn't actually matter what happens to the beetroot or the carrot or whatever. What matters is that the whole soup has been put together properly. So the process of converting them from the salad to the soup is a process of understanding not only how the ingredients were originally chosen and what they're meant to do, it's also what is the thing you've sold to your investor. What is their expectations? What is their expectations? What did you say up front you were going to do?

Stefan Wagner: 04:22 Does it mean you then sort of look at the portfolio then and do you do a factor analysis on it in the sense of how do you define your exposure then?

Richard Waddington: 04:35 At core, different product, different end products, have different attribute requirements. An attribute might be a factorness, and that might be a classical farmer French factor, it might be a barrow factor, or it might be some internal factor. I mean, we tend to talk more about portfolio attributes, which can be a factor or anything else. And so, yeah, some clients will have very specific requirements to minimize or maximize or control particular portfolio attributes. But that's not the end goal. That's a constraint on the end goal. The end goal is the best expression of your view within those constraints.

So you can say, I have a view that whatever these stocks are going to do really well, and these stocks are going to do really badly. And on top of that, I have a requirement that my growth factor should be less than something, and there must be this much small caps or whatever it happens to be. So there's a view, and then there's a requirement, a set of requirements, constraints. And when you want to create the final product, you need to express that view in the best way possible for the constraints that you have. And then you have a final weighing up thing, a balance you have to do between how much you want to express your view and how much you want to insulate against concentration risk.

Stefan Wagner: 06:18 So I suspect when you start with a client like this, there's quite a bit of a process because they first have to come to terms with actually they're going to have to define all these things, their constraints and attributes they're trying to optimize, which some of them are probably subconscious and you have to drag it out of them in a sense.

Richard Waddington: 06:35 A hundred percent. We always say it. the engagement with Sherpa is very front-loaded in terms of effort at our end. And the very first thing that we will do is shine a torch on the decision-making that is going on within the firm. And even that process, actually, managers find that incredibly helpful because we rarely say something they don't know, but there's always lots that they haven't actually formally written down.

Stefan Wagner: 07:07 Yes, I can imagine that one. So is that an iterative process over time? Or I presume constantly you review also to prove that what value you're adding in a sense is it?

Richard Waddington: 07:21 Yeah, the simple prove we're adding value is that's actually the easy part, because we have, you know, clients we've been working with for three or four years, and we can show them the difference month to month returns of our recommendations versus what they were doing before our recommendation. So they will say, OK, this is my portfolio and my views, and this is how I would do it. And we're like, well, actually, you should tweak this and this and this. And then over time, we keep all that data, and we can show that. So that part's, you know, the demonstration of value is easy. The demonstration of economic value is easy.

The demonstration of process value is much more softer. But that's clear in the discussions we have that the PMs are, you know, yes, we want one hour a month with Sherpa, where we are going to talk, we're going to listen to Sherpa talk about our portfolio. which is a, you know, it's a very different, it's a very different type of interaction to say a research provider. Whereas a research provider talks about the research provider's own views. Yes, yes, yes. We talk about the client's views. Yes, yes. Like, oh, you say you've got these views, but actually the risk. What you're expressing is a different, slightly different view. Yeah. Okay. Or it's, yeah, it's not, it's not attuned properly.

Stefan Wagner: 08:48 Wow. Okay. And when you then also look back, obviously you can say, okay, we did 200 or 300 basis points better a year. Do you also look at, you know, measure how much, did you add riskiness to the portfolio or?

Richard Waddington: 09:03 Everything in our view is about downside risk. And it's not volatility because we don't look at the kind of linear standard deviation. We look at a, accelerating downside risks. If you lose 2%, it might be minus 1 unit of risk. If you lose 4%, it's going to be minus 6 units of risk. If you lose 10%, it's minus 50 units of risk. So it heavily penalizes downside in all our analytics.

And the point of what we try to do is what we actually achieve doing is creating expressions of risk portfolios, which when the client's views are correct, they get the same return as they would have done with their portfolio. And when they're wrong, they lose much less. That's the core. So by providing the same, if the client is prepared to take an amount of risk,

Stefan Wagner: 10:10 We will tune our- You assume what he already has. That is the risk he's willing to take.

Richard Waddington: 10:14 So we say, this is the risk you're taking. We will tune our solution to have the same upside risk as you. But because we are doing the math in a slightly better way, blah, blah, blah, the downside risk on our portfolio will be less. And then over time, you can see. where on any given day when their portfolio makes, you know, plus three hours makes plus 2.9 or plus 3.1.

So it's around that when their portfolio is down negative two hours is always down negative one. So that you can imagine that scatter graph. And over time you build up that scatter graph and you say, look guys, it's really clear. We're giving you as good or better expression of your view when you're right. And we're controlling your downside much better.

Stefan Wagner: 11:04 And from the clients that using this one, what is sort of the typical client of these funds, certain size and what assets do they operate usually?

Richard Waddington: 11:16 Yeah, so the majority of our client base are equity trading. We have a couple of macro. We can talk about why that is one side versus the other. And of our equity guys, 80% are long only benchmarked. So big pension funds, big mutual funds, and they're big, because this is quite, you know, it's quite an involved process and you have to be able to, you have to have the resources both financial and people wise to spend time with us.

Stefan Wagner: 11:59 Is there any myth in your industry that you would like to debunk?

Richard Waddington: 12:03 I'm putting out there, I have to say this because of course it's my business, but when a portfolio manager has to build a product. Now, that's what a portfolio manager is. He or she is a product builder. Expecting them to be able to do everything we've just been talking about in their head and to get a better than 50th percentile output is a ridiculous concept. It's just not possible. And so, it's not really a myth. It's just something that people don't really think about. You've got, if you're running a book of 60 assets, say, you've got a 60 dimensional problem with maybe four or five different constraints, including a benchmark and risk and sector and all that kind of stuff.

And the idea that a PM can do this and get anywhere near a good result, is just a fallacy. Now, experienced PMs, and all our clients are people who've been running money for 15, 20 years minimum, experienced PMs solve this problem heuristically without a real process, but they sort of get there and they're never too far off the median result. Yeah. Experienced ones. I mean, because they are experienced by dint of being good, because otherwise they wouldn't survive. They wouldn't be there.

And the point is that a process, it can never get you to the very best result because you don't know what's going to happen in the future. But you can statistically go from the median, the 50th percentile, to the 65th or 70th percentile consistently by improving your process. The myth is it's the only way to do it and we don't really know how good or bad it is. The reality is we can tell you exactly how well or badly you're doing and we can bump you up into, rather than being median, to being 60th, 70th percentile. Our experience shows it's about 200 to 300 bps per unit leverage.

Stefan Wagner: 14:21 My last question I always like to ask everybody here and that is, what are your up to three, one is enough, but you can go up to three, favorite finance movies and why?

Richard Waddington: 14:31 Finance movies? Does Risky Business count? Yes. I'll go with Risky Business. Excellent. I guess, yeah, I prefer movies more about, yeah, entrepreneurship and a little bit of a little bit less following the rules. But then because movies about finance, where they're not following the rules, they're basically criminals, right?

Stefan Wagner: 14:53 Yes, pretty much everything, if you tell from Wall Street to, no margin callers, not criminals, but let's say The Big Short to Boiler Room, you know, Arbitrage. Yeah.

Richard Waddington: 15:06 I think things like, again, is it fine? Glenn Gary, Glenn Ross, right?

Stefan Wagner: 15:10 Yes, that ABC, Always Be Closing. That is a classic one.

Richard Waddington: 15:14 Yeah. I mean, just showing. Shows your expertise. Show your expertise.

Stefan Wagner: 15:19 Thank you very much. That was fantastic.

Richard Waddington: 15:21 Thank you. Thank you, Stefan.

Outro: 15:23 NALU FM Finance Podcast. Insight into the financial markets.

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