Actively Speaking Podcast

Back to School: An Active Manager's Report Card

August 22, 2019 Epoch Investment Partners Episode 5
Actively Speaking Podcast
Back to School: An Active Manager's Report Card
Show Notes Transcript

Actively Speaking is going back to school. Listen in as guest Wayne Lin discusses the fundamentals of the Sharpe ratio, information ratio and active share how to use them when evaluating active managers. (August 22, 2019)

Speaker 1:

Hello, and welcome to Actively Speaking. I'm your host, Steve Blyk. Join us each episode as we discuss current issues concerning capital markets and portfolio management from the perspective of an active manager. Welcome to another episode of Actively Speaking. I'm Stephen Bber, your host, and my guest today is Wayne Lynn, a colleague of mine here at Epic. Welcome, Wayne. Thanks for joining us.

Speaker 2:

It's a pleasure to be here. Steve.

Speaker 1:

Uh, Wayne is a managing director in our global equities, uh, at Epic, but he really has a focus on what we call product management. Why don't you tell us little about what, what product management is?

Speaker 2:

Sure. Well, within Epic, you know, product management, I think there're, there's sort of, uh, there are two areas that we focus on product management. The first one is just an internal focus, which is reviewing all of the different strategies that we have here at Epic. Uh, doing a number of different types of analyses just to ensure that from the outside perspective, uh, that the strategies are doing what they, they'd kind of promised that they're doing. And we, we review all of those analytics internally. Um, and from an external, or from a, from a distribution focused view, what product management does is helps to take all of the, um, the attribution of the different strategies and tries to sort of explain and package, um, uh, those types of analyses in a, uh, a simpler form to, to be digested and

Speaker 1:

Distributed. Okay. Well, so that's a perfect lead in to introducing the topic, uh, that we're gonna talk about today. So, the name of this podcast, of course, is actively speaking. We thought it would be good to talk about what does it mean to be an active manager? How do you measure how active a manager is? How do you measure whether an active manager is doing a good job? Uh, all those sorts of questions. So let's start off by talking about what are the, the metrics that people use to, to measure, um, what it is active managers are doing. I want to talk first though, about a, a ratio that's probably one of the oldest ones in, in the, the modern Portfolio Theory toolkit, which is the Sharp Race Show, and of course named for Bill Sharp. Uh, who is one of the people credited with coming up with the Capital Asset pricing model over 50 years ago. And I should put out a caveat at the beginning of this, that, you know, we had this white paper last year, the limits of theory, and one of the points in that paper is that modern portfolio view of risk is somewhat limited and it well limits it to basically standard deviation of the returns on an asset. And one of the arguments in that paper is that, that that's an insufficient measure of risk. But for the purposes of today's discussion, we're gonna, we're gonna go with that<laugh>. So what is, tell us about what is the sharp ratio? What was that capturing?

Speaker 2:

Well, I mean, the sharp ratio is very, it is just capturing, think about it as a, you know, how much you are getting in, in excess of a, a risk-free rate, right? Um, versus the volatility, as you say, the standard deviation. So that, that, think about that as, as the ratio, right? Mm-hmm.<affirmative>, you know, ideally it's every investor's dream is to have, you know, an unbelievably high return over the risk-free rate with no volatility whatsoever, right? Mm-hmm.<affirmative>. Um, but we know that that's not realistic. So what the ratio tries to do is to kind of measure an efficiency, right? So, so anything that's divided by a standard deviation is really trying to say or figure out per unit of risk that I take, how much am I getting in reward for that?

Speaker 1:

Right? And so, uh, it doesn't necessarily, uh, I I just bring it up cause I think it's, it's an, it's a ratio that people are familiar with they've heard of, and I just wanted to talk about it from'em before we go on to other measures. But, uh, I think one of the drawbacks of the sharp ratio is that it kind of assumes that there's a linear relationship. If, if you use sharp ratios to compare different assets, you're assuming that ideally there's, uh, there's this linear relationship between, uh, return or excess return over the risk free rate and, and risk. So that if one asset earns a a 2% higher return, uh, over the risk free rate with say 4% volatility, that's a sharp ratio of 0.5. Yes. And that therefore, uh, see, oh gee, if I want to earn a 4% return then, uh, to get for the same sharp ratio, I would get 8%. Like I should accept an 8% standard division. But that's not really what the, the way the real world works is that the, as you move out the volatility spectrum, yeah, I say from treasury bills to short term bonds to, you know, more risky bonds to stocks, et cetera, there isn't really a linear relationship between the higher volatility that we observe and the return it tends to flatten

Speaker 2:

Out.

Speaker 1:

Exactly. So, you know, stocks have had a return of say something like, you know, 10, uh, say eight to 10% with a standard deviation of about 16. So you'd get a, a sharp ratio. Well, depending on what you use as the risk-free rate, I mean, historically it's been, you know, like 4%, three to four mm-hmm.<affirmative> depends on what inflation's been. Of course these days it's closer to zero, but, um, uh, you know, you, you're gonna get a sharp ratio of, you know, about 0.5 or, or, or something like that. And, uh, but you know, you can get a much higher sharp ratio with short term bonds. Just go a little bit out the, uh, the yield, the, uh, maturity spectrum from the risk rate. You can, you can get a much, much higher sharp ratio cuz there's very little volatility on those short term bonds. Right. So, sharp ratio, my point being, uh, in, in talking about this, that it's, uh, it's not necessarily useful in comparing different asset classes. It can be useful perhaps within an asset class looking at a couple of different investments. But, um, again, it's not, uh, the idea that the higher, the better would, uh, if, if you take that to an extreme, it would just constantly have you in the very, very short term fixed income. Right. Because that has the best sharp ratio.

Speaker 2:

Exactly. And so to your point, it's, you know, mathematically it is a ratio. So basically one of the things that does ignore is sort of the, the, the, the, the absolute return or the level of return, if you will, right? So if you think about something, um, like I like an asset class, you, you mentioned sort of short term bonds. Uh, you have, you think about an asset class that has sort of very little risk as we measure bystander deviation, right? Since it is a ratio, it just gets amplified, sort of the lower it is, right? So, so I think that's a very valid point. Um, you bring up a very interesting point, however, that when you look within an asset class, so let's just take the asset class of equities, right? Um, the sharp ratio is kind of an interesting thing to take a look at because, you know, we have this, this concept of market exposure. The market exposure by definition has, you know, certain, um, standard deviation and return associated with these market benchmarks that we have. Um, but, um, you, you in theory could come up with a different type of strategy that gives you similar exposure, right? So if, if we're looking at the sharp ratio, let's say, think about it like the, the, the numerator or the top number is gonna be similar to the market, but it comes up with, um, you're able to get lower volatility in that, right? So if you're able to do that, you end up with a higher sharp ratio. It really is reflecting that the, the, the strategy is more efficient, right? Mm-hmm.<affirmative>, the funny thing is though, however, if you, if you take that strategy, and we will talk later about the information ratio, but if you, you take that strategy and you measure it versus a sort of a benchmark relative, uh, metric, um, it may be more challenging to see the, you know, the benefit or the, um, the, the greater efficiency of the, of the non-market, um, strategy.

Speaker 1:

Okay. So let's, we've talked about the sharp pressure. Let's move on now. Um, let's talk about how do you measure active managers? Active managers by definition are managers who are not holding just the benchmark. They are trying to do better in some way. And we'll talk about what do we mean by better? How do you measure that? It's not as simple as it sounds. Uh, how do you even measure how active a manager is? So again, by definition, and an active manager is overweighting some securities relative to the benchmark underweighting. Others maybe not owning some of the securities in the benchmark, but how do you, how do you measure that that level of activeness, say one manager is, is are all active managers alike, or how do we differentiate between them? Well,

Speaker 2:

I mean, one of the, the measures that you can use is sort of tracking error. And tracking error is simply the, the, the technical definition is that it's the standard deviation of the excess returns. So at times, a manager will outperform underperforming benchmark, and if you then take, you know, the, the, the standard deviation of those outperformance and underperformance, that's what's called tracking error.

Speaker 1:

Mm-hmm.<affirmative>,

Speaker 2:

If you have a manager that takes a lot of risk and either, you know, greatly outperforms or, or greatly underperforms over different periods of time, uh, that tr that manager will have a higher tracking error than, you know, than a manager that is sticking very much close to the, the benchmark and making sort of little bets on the edge. So one of the things that investors look for since they're paying a fee is they're saying they want to make sure that the, the manager, the, the active manager takes enough risk such that they're able to, to, to outperform at a level that exceeds the fees that they charge.

Speaker 1:

But in recent years, there's been another measure that's come along, uh, which is called active share. Yes. And tell us what that measure is.

Speaker 2:

Well, active share, really, it's, it's, um, instead of looking, so the tracking area looks at the actual performance and the excess and under excess, uh, returns, what active share does is it looks at the, the weights in the portfolio. So effectively what it's trying to do is saying, look, if I have a benchmark, and within the benchmark there are a number of securities and there's a waiting, then what it does is it says, okay, the, the active manager is gonna have a different portfolio than the benchmark, or else I, there's no reason for me to pay for the active manager services. Those deviations then are measured. So it could be, you know, the active manager could be underweight one stock, and then under, you know, overweight another stock. Those overweights and underweights, they're, they are basically measured. Um, and then the absolute value of those are added together, and then they're sort of divided by two. That's all the math, right? But effectively, what you're trying to get to in active share is give me a measure of how different the portfolio that I'm, I'm paying for, I'm paying the active investor to create for me. How different is that than the benchmark?

Speaker 1:

Okay, so let me give you a hypothetical example. Suppose there's a benchmark that has two stocks and they each have a 50% weight in the benchmark. And in my portfolio, I have 40% in one stock and 60% in the other. So what's my active share gonna be in that case? So I, I'm 10% overweight one, and I'm 10% underweight. The other, do those cancel out and I have an active share of zero? Or do we add them up and I, we have an active share 20, basically take gaps with values.

Speaker 2:

You, you, you take the absolute value, so you add those together. So it would be, you know, you have 20 and then you divide by two. So you have an active share. 10%.

Speaker 1:

Okay, so that's pretty low.

Speaker 2:

Yes.

Speaker 1:

Yeah. So yeah, give, give us a sense of what's the scale,

Speaker 2:

The, the scale. Typically you start thinking about active managers just to be active with an active share. At the low end of the range is probably about 60%. Mm-hmm.<affirmative> for, you know, high conviction managers, the expectation really is that the active share should be somewhere in the, the high eighties or the low 90%, um, in terms of a measure of how different they are from the portfolio. Now, you know, the one caveat there is a lot of it depends upon benchmarks as well. So if there's a benchmark that has, you know, a lot of small stocks and whatnot, it may be easier to get, you know, you know, higher active share, um, than, than other types of benchmarks. So that I, I think the, the main point is you need to think about, you know, the number of stocks in the benchmark, um, and how easy it is to kind of replicate or deviate from that benchmark when you're thinking about active share as well. But in general, for high conviction managers, um, and for active managers in general, the expectations is somewhere in the, you know, the, the, the high eighties and low nineties.

Speaker 1:

So, so basically, uh, I mean active risk tracking error, which is also called active risk, right? That's all about returns and active share is all about awaits.

Speaker 2:

That is correct.

Speaker 1:

Is one better than the other?

Speaker 2:

No, I think when a, as an investor will look at the managers that they hire, I always like to think about it as a, as a mosaic to have all these different types of information or different data points that you look at, and it kind of paints a picture as to what the manager is doing. And so I think that those, these are very two, two data points. Um, one is sort of ex-post and the other one is a kind of ex-ante or, or, you know, after the factor of the return or before the fact or, or, or, you know, concurrent, which is looking at the portfolio and taking those two pieces of, of data together will give you, I think, a broader picture of, um, of, of, uh, what the active manager is doing or the type of risk that the active manager is taking. Now, I, I think this, it's a good time to have a quick segue into some of the, the controversy around sort of active share. And I think as in measures, it's perfectly fine. It, it gives you a sense, but there had, there had been some controversy before that sort of high active share led to our performance. And then that, you know, we don't really believe that, right? So the way we think about it at Epic is that it is just another measure to, and, and it, and it is a way for us to gauge the level of active risk that we're taking. Mm-hmm.

Speaker 1:

<affirmative>. Okay. So we've talked about these two measures of, of active risk in a sense, or, or how active a manager is. One, again, one is based on the returns that the portfolio generates, and one is based on just looking at the weights that the portfolio has. Now let's talk about how do you measure whether a manager, an active manager has, you know, done a good job? How, how do we define that? So I know that there's, there's a, a measure that we like here at Epic called the information ratio.

Speaker 2:

That's correct.

Speaker 1:

So tell me what that is.

Speaker 2:

So the information ratio, you, you remember how we had talked about the sharp ratio and the sharp ratio being sort of the absolute return over in general, like, you know, return over the risk-free rate divided by the standard deviation, right? Mm-hmm.<affirmative>, it's a similar concept, but what we do is we bring in the benchmark for information ratio. And so what you're trying to do is you say, look, here's my excess return or my return relative to the benchmark, and I'm gonna divide that then by the, um, the tracking or, or effectively the standard deviation of that excess return. Now, another thing that we do here is, uh, we do what's, we calculate what's called using jenssen's alpha, or we actually risk adjust the excess return. So if a, a strategy has a lower beta, we'll basically use, you know, we'll adjust the benchmark return down to reflect the beta, and then we'll divide that by the standard deviation of, of the excess returns. And essentially, uh, whether or not you do that adjustment or you just take the straight excess returns, it's done both ways. It is a measure to try to illustrate how much the active manager is adding per unit of active risk that they're taking. So, whereas sharp ratio is more absolute, right? You think about information ratio as more relative, and it's, it's a more, more relevant measure if you're trying to look at whether or not your manager is outperforming this, the benchmark that you've stated for the manager,

Speaker 1:

What it's all about is you're, you're, as an active manager, you're, you're taking active risk, you're looking different than the benchmark, right? And the question is, how much are you getting paid in act in excess return in active return for taking that active risk? Yeah,

Speaker 2:

That's a very good

Speaker 1:

Way of putting it. How, how efficient are you at converting active risk into active

Speaker 2:

Returns? Exactly. Exactly

Speaker 1:

Right. And so there's really no equivalent for, uh, of that yet anyway, for active share. Is there, I mean, nobody's found a way to sort of relate X's return to active share in that same kind of way,

Speaker 2:

But I, I, there was one interesting thing though about, um, the, the information ratio, which is, you know, we had talked earlier about the sharp ratio and, and how potentially if you had a strategy, let's say that, that you had an equity strategy that had a better sharp ratio simply because it gave you the similar, similar returns to the benchmark, but had a, a lower volatility, right? So it had a high sharp ratio. If you actually take that into active space and look at the information ratio, what you get is actually is something very interesting, which, and, and a little bit counterintuitive, which is that, okay, you have a strategy that gives you similar performance so that, that that top part of your information ratio, the, the, the numerator of your information ratio is gonna be pretty close to zero or very small, right? And then it gets to that, that return in a different way than the benchmark does. Cuz you're, you're re you're defining it the strategies by definition, you know, trying to create a different type of ex, um, different type of exposure than the benchmark that's lower volatility, you end up with higher track error. So in this case where you have a strategy that is essentially, you would argue more efficient on a sharp ratio basis, you get a very ugly looking, um, information ratio because, you know, the, the, the standard deviation of, of excess returns could be fairly high because it's, it's a different type of exposure. And the, the numerator, the excess return at the end of the day could be, you know, fairly, you know, similar to the, the, the benchmark returns. So it's either gonna be very small or potentially negative, right? Mm-hmm.<affirmative>, so this is, this is why I think that, you know, the investor really needs to take a step back and say, what is it that I'm really trying to achieve? So if, if you were in, let's say you were in charge of a pension plan, Steve, and you looked at, well, I really would like something that's similar in that'll generate similar returns. Um, but is, you know, is is diversifying to the, the, the regular market exposure that I have, if you looked at information ratio, you wouldn't put pick any kind of different strategy, right? So that's, you know, this is when you would look at sort of sharp ratio and say, oh, well, are, are there more strategies that are sort of similar but more efficient? If you were, as the pension manager, were more concerned about, well, I have put together this asset allocation and this is the, this is the benchmark that I think these, these are the types of exposures that I want, and I want somebody to add alpha on top of that, I add excess return on top of that, then you would look at the information ratio to kind of evaluate, so different tools for different situations.

Speaker 1:

Uh, and so the numerator in that information ratio, as you were saying, is excess return. And you mentioned that it can be adjusted or, you know, proper, it should be adjusted for some measure of risk. That's correct. It's, it's really quote alpha in the numerator, not just Exactly, yes. Raw excess return. Yes. This is getting kind of geeky here, but you know, you say you can adjust it for beta, but, uh, you could also, I mean, as, as risk models get more and more elaborate with more and more factors, uh, theoretically one could be correcting not just for beta, but for all sorts of exposures.

Speaker 2:

That's correct. Exactly. Yeah. You could in theory, you know, if you really wanted to geek out, sort of adjust for all the different factor exposures, uh, and that, that would make it even more difficult for the active manager to outperform or generate alpha. It used to be back in the day that, you know, managers would just tilt, uh, towards mar uh, a lower market cap and they would be able to outperform or do, you know, all these other types of factor tilts. But more and more I think that the investing community has sort of defined those as more structural. The only, the only o other interesting observation I would, I would share with you, Steve, is that I think about these factors, and you've written about them, I think about these factors they've been compared to ingredients in, in a recipe, right? Just the fact that the, the, the factors are there. I'm, I'm Lee of taking away from the value that the manager adds of combining those factors together in a, in a unique way. So we are in a world now where, you know, a lot of people think about factors as things that you kind of stack up and, and then you get, but I, but I really do think that the, that active managers add value by creating these strategies that do give you these exposures to different factors, but also combine them in such a way. And, and my point being that I don't think it's as easy as people think to just sort of stack these factory exposures together.

Speaker 1:

Mm-hmm.<affirmative>. Yeah. Well, it leads to, uh, a, a broader question that we're not gonna, we don't really have time to talk about here, of really the, the line has shifted over time between what is beta and what is alpha, right? Right. You, it used to be quite simple. There was, there's this one factor called beta, which is just based on really how does the stocks, how are the stocks returns, you know, cova with the market and so forth. And now we've, it's become much more nebulous what's considered, uh, beta and what's considered alpha. There's all these quote smart beta factors and so forth. It, it's an interesting question maybe, uh, to discuss another time of, uh, where, where do you draw that line between beta and alpha? And I think you make a good point that it's, it's not as, uh, as straightforward as it seems that these factors are not just, you know, simple, easily stackable. Exactly. Uh, things. So, yeah. Okay. Well, thanks. Uh, this has been a, a really useful, uh, primer on, um, on what it means to be an active manager. How do you measure how, how active a manager is, and how do you measure whether an active manager is adding value or not? So Wayne, uh, thanks for joining me.

Speaker 2:

Thank you for having me as your guest.

Speaker 1:

Remember to subscribe to actively speaking on Spotify Apple Podcast or Google Play. You can find all of our previous episodes and additional content on our website, www.eipy.com. We'll talk to you again soon.

Speaker 3:

The information contained in this podcast is distributed for informational purposes only, and should not be considered investment advice or recommendation of any particular security strategy or investment. Product. Information contained herein has been obtained from sources believed to be reliable but not guaranteed. The information contained in this podcast is accurate as of the date submitted, but as subject to change any performance information reference in this podcast represents past performance and is not indicative of future returns. Any projections, targets, or estimates in this podcast are forward-looking statements and are based on epic's research, analysis, and assumptions made by Epic. There can be no assurances that such projections, targets or estimates will occur, and the actual results may materially be different. Other events which were not taken into account in formulating such projections, targets or estimates may occur and may significantly affect the returned or performance of any accounts and or funds managed by Epic. To the extent this podcast contains information about specific companies or securities, including whether they are profitable or not, they are being provided as a means of illustrating our investment thesis. Past references to specific companies or securities are not a complete list of securities selected for clients and not all securities selected for clients in the past year were profitable.

Speaker 4:

Stop audio.