Speaker 1: M. This is Mesters in Business with Very Renaults on
Speaker 1: Bluebird Radio. This week on the podcast, I have an
Speaker 1: extra special guest. What can I say about Sandy rat Trey.
Speaker 1: He is the chief investment officer of the Man Group,
Speaker 1: which manages over a hundred and twenty five billion dollars UH.
Speaker 1: He's the co inventor of the VIX index. He has
Speaker 1: an incredible career UH both in UH equity research and
Speaker 1: for derivatives as well as systematic investing UM. He's just
Speaker 1: a rock star. I don't know what else to say
Speaker 1: about it. The track record that the Man Group has amassed,
Speaker 1: as well as how they've pushed forward UM portfolio construction
Speaker 1: theory is really incredibly, incredibly influential. Not only was he
Speaker 1: the co inventor of the VIX index, but he's written
Speaker 1: extensively about risk management and how to design portfolios and
Speaker 1: you know what to expect when you're expecting a black swan,
Speaker 1: which you by definition can't know what to expect, and
Speaker 1: how to build strategies that give you some degree of
Speaker 1: protection against this. If you're remotely interested in hedge funds,
Speaker 1: asset management, quantitative strategies, the VIX and managing risk well
Speaker 1: strap yourself in because this one, uh is a good one.
Speaker 1: With no further ado, my conversation with Sandy Retrey of
Speaker 1: the Man Group. This is Masters in Business with very
Speaker 1: Renaults on Bluebird Radio. My extra special guest this week
Speaker 1: is Sandy Retrey. He is the chief Investment Officer of
Speaker 1: the Man Group. He is also on the Executive Committee
Speaker 1: and the Responsible Investment Committee. He is per Apps most
Speaker 1: famously the co inventor of the VIX index. He's also
Speaker 1: run a number of different systematic strategies for Man and
Speaker 1: other organizations. The Man Group's assets under management are over
Speaker 1: a hundred and twenty billion dollars and Sandy is the
Speaker 1: co author of the book Strategic Risk Management, Designing Portfolios
Speaker 1: and Managing Risk. Sandy Retree, Welcome to Bloomberg. Great, Thank
Speaker 1: you very much, Perry. It's good to be with you.
Speaker 1: So you have a really interesting background. You're deeply steeped
Speaker 1: in mathematics. How did you find your way into the
Speaker 1: investment business? So it's a it's a long story. I
Speaker 1: as a teenager, I thought I would become a theoretical
Speaker 1: physicistem that was my ambition. I went to Cambridge University
Speaker 1: to study physics, and I really discovered a number of
Speaker 1: things at that time. Number one, UM, I was I
Speaker 1: thought quite good at physics. Turned out that I surrounded
Speaker 1: myself for the whole bund of other people are also
Speaker 1: pretty good, and really standing out was hard Um. And Second,
Speaker 1: I think at the time the amount of innovation that
Speaker 1: was taking place in physics seemed to have sort of
Speaker 1: dropped off a little bit from the nineteen seventies and
Speaker 1: and it was a sort of slow period in the
Speaker 1: late nineteen eighties, and so that made me think, well,
Speaker 1: maybe I could use these math skills for something else.
Speaker 1: And that really got me into thinking about finance. So
Speaker 1: I some people duck out of physics having done PhD
Speaker 1: s or or taught at universities. I ducked out a
Speaker 1: little bit earlier on UM, and that got me into them,
Speaker 1: thinking well I should use these skills. I end up
Speaker 1: joining Golden Sacks, and and there again I learned something
Speaker 1: which was I thought the exciting bit would be the
Speaker 1: corporate finance areas, so advising on major corporate transactions, and
Speaker 1: what I realized was that that didn't really use the
Speaker 1: quantz skills that I had. So I did that for
Speaker 1: a couple of years, and then I moved over to
Speaker 1: first sixt income research, then equity derivators research, and then
Speaker 1: finally transitioned out of that into UM into more proprietary trading,
Speaker 1: and then into fund management. So let's build off of that.
Speaker 1: You did a lot of work on derivatives and fundamental
Speaker 1: strategy of government. You then go to MAN where you're
Speaker 1: running things like systematic strategies and a h L. What
Speaker 1: was man h L focused on when you were managing that.
Speaker 1: So when I arrived at h L, which was the
Speaker 1: end of two thousand and twelve, was really a futures
Speaker 1: trend following business. It was c t A and c
Speaker 1: t as have been having a pretty difficult time really
Speaker 1: since the end of the financial crisis. They had a
Speaker 1: tremendous two thousand eight and then that really done nothing
Speaker 1: in the following years. So nine eleven, twelve, we're all
Speaker 1: years which essentially added up to nothing. So the fantastic
Speaker 1: crisis here. Very few as the marriagers could say that
Speaker 1: two thousand eight was a great year, but HL could
Speaker 1: definitely say that. But then you had a long dry
Speaker 1: period and people were starting to say momentum doesn't work anymore.
Speaker 1: It's broken. And I think what I really did when
Speaker 1: I arrived in NHL it was to say, well, I've
Speaker 1: been involved in all sorts of different quant strategies in
Speaker 1: my golden years, and I can use a much broader
Speaker 1: perspective than maybe the futures trend followers had and look
Speaker 1: to develop a much wider range of systematic stranities. So
Speaker 1: when I arrived, we had a handful of models UM.
Speaker 1: Today we've got three or four models running in HL,
Speaker 1: so we really expanded number of models we're using. UM,
Speaker 1: we expanded the number of markets that were trading, so
Speaker 1: we used to trade futures, UM and effects markets and
Speaker 1: my group it started trading OTC markets, but it was
Speaker 1: still quite small UM, and we really picked up and
Speaker 1: start trading much wider variety of markets. So now we
Speaker 1: trade around seven markets around the world using system addic models.
Speaker 1: And then finally we came up with different many different
Speaker 1: types of funds, so very short term funds, funds which
Speaker 1: are more fundamentally driven, funds which are maybe trying to
Speaker 1: provide more protection characteristics, or funds trying to maximize the
Speaker 1: sharp ratio. So so we really tried to grow quant
Speaker 1: into many different areas. And I suppose my advantage coming
Speaker 1: into a place like h L is that most people
Speaker 1: in those HL and in the competitors had really grown
Speaker 1: up and had the whole careers in the in the
Speaker 1: c t A or the futures trend following business, and
Speaker 1: I had had none of my career and futures try
Speaker 1: and following, and but I had all these other influences
Speaker 1: that I could bring in, And so that really was
Speaker 1: how I worked with the team now to significantly expand
Speaker 1: UM the business which was having a very difficult time
Speaker 1: and arrived and UM declined to lesson ten billion dollars
Speaker 1: of assets in the h L unit and today were
Speaker 1: many times launcher than now. So I want to focus
Speaker 1: on something you mentioned in passing, but it's so relevant
Speaker 1: to what we've been seeing in the markets recently. You
Speaker 1: said that momentum as a factor seemed to have been fading.
Speaker 1: We've seen other factor based investing like value, go long
Speaker 1: long periods of underperforming. There's so many different questions I
Speaker 1: can ask you about this. Let's just start with is
Speaker 1: that the nature of any factor or any specific trading
Speaker 1: edge that they only last so long before eventually everybody
Speaker 1: wise is up to them and the alpha gets arbitraged away.
Speaker 1: And do you see these sort of edges disappearing more
Speaker 1: quickly these days then they used to in a perhaps kindler,
Speaker 1: gentler era twenty or thirty years ago. So, um, I
Speaker 1: agree actually with everything you said, Barry, except for the
Speaker 1: last bit about kind of legenter era. From my memory
Speaker 1: twenty or thirty years ago, it was probably less kind
Speaker 1: and less gentle than it is today. Um, But let's
Speaker 1: let's start with factors. So I think one of the
Speaker 1: things which has been interesting over my career is nobody
Speaker 1: really talked about factors apart from a very small sort
Speaker 1: of quant group twenty thirty years ago. Today they're in,
Speaker 1: you know that they're sort of part of all our
Speaker 1: portfolio marriagers at Man Group, whether they're quants or discretion marriages.
Speaker 1: Everybody talks factors and so that's been a big change,
Speaker 1: and they've become sort of part of, you know, just
Speaker 1: sort of general dialogue when people are talking about markets.
Speaker 1: The thing I'd say is that I don't think that, um,
Speaker 1: the core factors which have been around in a long
Speaker 1: time are going to disappear. And for me, you know,
Speaker 1: as a European, I was working in New York in
Speaker 1: the late nine nineties and into the two thousand's, and
Speaker 1: I remember looking at front page of the Wall Street
Speaker 1: Journal one day and on it it said value investing
Speaker 1: is for old people and um and I you know,
Speaker 1: as the European obviously, you know Europeans want to live
Speaker 1: in old houses. Clearly in the US people mostly want
Speaker 1: to live in new houses. So it's a big sort
Speaker 1: of didn't quite understand that the the the extent of
Speaker 1: the statement there, and it was a ridiculous thing to say.
Speaker 1: I was a young person being included in the front
Speaker 1: page of the Wall Street Journal, right in the tail
Speaker 1: end of the tech bubble and just before a huge
Speaker 1: outperformance of value stocks. So these factors and we should
Speaker 1: talk about, you know, which are the which are the
Speaker 1: actors that are likely to persist in which are not
Speaker 1: um But factors like value for example, I think generally
Speaker 1: don't have particularly high sharp ratios, so they're their returns
Speaker 1: adjusted for risk are not particularly high. But the idea
Speaker 1: that buying cheap stocks will never work again, I've never
Speaker 1: thought that was a sensible thing to say or think,
Speaker 1: but every now and then, you know, that's what this
Speaker 1: fellow on the front page of the journal said um
Speaker 1: twenty odd years ago. So I think factors, at least
Speaker 1: a core set of factors are very likely to persist,
Speaker 1: but it won't give particularly amazing risk adjusted returns, But
Speaker 1: I think they are likely to give you positive risk
Speaker 1: adjusted returns over you know, relatively long cycles. I would say, though,
Speaker 1: that one of the things I've seen in the last
Speaker 1: ten years or so is as people thought they understood factors,
Speaker 1: and people started to find hundreds of these things. And
Speaker 1: I don't think there are hundreds of real factors. Think
Speaker 1: there's a small handful um of factors, and this explosion
Speaker 1: really is an overfitting exercise. It's people finding patterns in
Speaker 1: the data that don't really exist, and I think that's
Speaker 1: something that people should be very wary of. You know,
Speaker 1: I've seen data providers and firms sell their libraries of
Speaker 1: factors with with you know, literally hundreds of these things,
Speaker 1: and I don't think that's going to be a source
Speaker 1: of returns. The final thing I should say is that
Speaker 1: you mentioned momentum, and momentum is quite an interesting factor
Speaker 1: because there are two very different definitions of momentum. One
Speaker 1: is really used by equities people and they will go
Speaker 1: along the positive momentum price momentum stocks and short the
Speaker 1: negative price momentum stocks. They sometimes do it with earnings
Speaker 1: as well, but basically go along the stocks which have
Speaker 1: been out performing and short the stocks which have been
Speaker 1: under performing. But then is a very different definition of momentum,
Speaker 1: and that's what the c t as use, And they
Speaker 1: don't they don't look at the price move against anything.
Speaker 1: They just do it in absolute terms. And they tend
Speaker 1: to do that in macro markets. So they'll create the
Speaker 1: SMP or the or the the dacks or the euro
Speaker 1: or gold or something like that. And and that's a
Speaker 1: much different definition because it's not it's not going long
Speaker 1: one set of markets and or one set of stocks
Speaker 1: and short another set of stocks. It could be long everything,
Speaker 1: or it could be short everything, and it gives you
Speaker 1: a very different type of return profile. That second type
Speaker 1: has a very nice feature, which is that it barely
Speaker 1: reliably will do well in bad periods in markets. So
Speaker 1: I talked about how, for example, HL two thousand eight
Speaker 1: was an excellent year. Well, not many strategies that could
Speaker 1: say two thousand eight was an excellent year. And that's
Speaker 1: because that second definition of momentum what I would call
Speaker 1: time series momentum the or univariate momentum. That that definition
Speaker 1: momentum has very good protection like characteristics. It will pick
Speaker 1: up on a trend, especially in negative trend in markets,
Speaker 1: and jump on that trend. So it sounds like the
Speaker 1: price momentum seems to be relative, while the time series
Speaker 1: has a persistence that gives it a very different characteristic.
Speaker 1: Or am I oversimplifying that? No? No, I think that's
Speaker 1: exactly right. So I think you know, the most investing strategies,
Speaker 1: including price momentum and equities, but most investing strategies have
Speaker 1: what people like me would call a left tail, so
Speaker 1: it make you money most of the time, and then
Speaker 1: every now and then they serve your up an unpleasant surprise.
Speaker 1: Time series momentum does the opposite. Time series amentum most
Speaker 1: of the time gives you pretty boring returns, but every
Speaker 1: now and then I'll give you a very positive surprise.
Speaker 1: And and that's really rare in investing strategies. And from
Speaker 1: my perspective, that's you know, that's a very attractive characteristic
Speaker 1: when you're building portfolios, to have a bit of something
Speaker 1: which does the opposite of most other investing strategies. Let's
Speaker 1: talk a little bit about the VIX index, which you
Speaker 1: were the co inventor of tell us. How does one
Speaker 1: go about inventing the VIX index? Well, you know very
Speaker 1: In short, you get lucky. So the the story behind
Speaker 1: it is, as we talked about in our earlier segment.
Speaker 1: I was working in New York basically and see clients
Speaker 1: and every time you went to somebody's office to be
Speaker 1: a TV screen in the in the fire in the
Speaker 1: entrance area, and they would have prices of all sorts
Speaker 1: of things coming across that screen, you know, the sort
Speaker 1: of the price of crude ale, the treasury bond yield,
Speaker 1: the sple hundred level, that sort of thing, and it
Speaker 1: would have the VIX on it. And so there was
Speaker 1: a VIX, but that was the VIX was the only
Speaker 1: thing that seemed to come across the greens that you
Speaker 1: couldn't trade. And so the story really came because a
Speaker 1: colleague of mine at Golden Sacks came to me one
Speaker 1: evening and said, you know, I've had this call um
Speaker 1: from a client who wants to do a trade on
Speaker 1: the VIX. Could we do that? And my colleague ran
Speaker 1: options trading and I ran the drug to his research
Speaker 1: a bit of Goldman and we got our heads together
Speaker 1: and I went and found the formula for the VIX
Speaker 1: as stood as as pre existed, and we worked at
Speaker 1: it just wasn't possible for us to do a trade
Speaker 1: on that. It wasn't designed in a way that you
Speaker 1: could hedge a trade on the VIX. So we came
Speaker 1: up with a kind of crazy idea which was, Okay,
Speaker 1: well there's this VIX thing which was owned by the
Speaker 1: cbo E, but you couldn't trade it. And there was
Speaker 1: a good reason why you couldn't trade it, because you
Speaker 1: couldn't hedge it. So why don't we change it? And
Speaker 1: so we came up with a completely different formula. It
Speaker 1: turned out to give fairly similar levels to the old VIX,
Speaker 1: but but it was a completely different formula, didn't use
Speaker 1: black sholes at all and um and we thought, well, hey,
Speaker 1: this this formula actually you could hedge UM futures contract
Speaker 1: on or something like that. And so what we then
Speaker 1: did is I was quite friendly with a fellow called
Speaker 1: Bill Speth, but the CBOE, who has had of research there.
Speaker 1: And I called Bill and said, you know, you have
Speaker 1: this VIX but you make no money out of it,
Speaker 1: because you just published this thing and doesn't give you
Speaker 1: any income. And we've got an idea how you could
Speaker 1: change it to make it something that could be traded,
Speaker 1: and maybe you could launch futures contracts on that, and
Speaker 1: so that might be interesting to you. And then I
Speaker 1: launched into a long description of the math behind that formula,
Speaker 1: and UM Bill very wisely said, you know, maybe you
Speaker 1: could send me a letter with that formula. So so
Speaker 1: I sent him a letter and which was actually, with
Speaker 1: the benefit of findset quite helpful because now I have
Speaker 1: a quite clear record of when we communicated this formula
Speaker 1: in two thousand three to the cbo E UM and
Speaker 1: UM and within six months we had a new VIX
Speaker 1: being calculated using this new formula, and another six months
Speaker 1: later the cbo E, which until that point was only
Speaker 1: an options exchange, had launched futures contracts on it. And
Speaker 1: so there was a series of sort of you know,
Speaker 1: lucky moments in there. A client came and asked a question.
Speaker 1: I happened to know Bill at the cbo E, so
Speaker 1: I knew somebody to call. They happened to be interested
Speaker 1: in in launching futures contracts and our timing was was
Speaker 1: spot on. So there was a whole series of bits
Speaker 1: of luck along the way. And what really happened after
Speaker 1: that is another good learning experience for me, which is
Speaker 1: at a boss at the time who said, look, you
Speaker 1: should talk to all the salespeople and find out if
Speaker 1: they're gonna bring in business on this new Vix thing
Speaker 1: that you've been working on. So I spoke to the
Speaker 1: salespeople and we did a little survey, and you know,
Speaker 1: I was going to retire on the proceeds of this survey.
Speaker 1: It was just amazing how much business we were going
Speaker 1: to do. Day one comes along, I wait for the
Speaker 1: phone to ring, and it doesn't ring. They two comes along,
Speaker 1: the phone still doesn't ring, and by day three you
Speaker 1: sort of get it. You know, it's nothing's happening. And amazingly,
Speaker 1: the first significant trades we got done were actually with
Speaker 1: investors outside of the us UM and they were the
Speaker 1: people that still have got the early stages of this
Speaker 1: new market in in Vix going. The other thing that
Speaker 1: happened was my boss said, you should call, you know,
Speaker 1: some of the other banks and and see if they're
Speaker 1: going to support this thing. So I called nine other banks,
Speaker 1: and nine out of nine said they had no interest
Speaker 1: in supporting it. So it wasn't a particularly auspicious beginning,
Speaker 1: you know that, And it took a little bit of persistence.
Speaker 1: Now today it's this huge market and it trades enormous volumes,
Speaker 1: but it's a good sort of lesson in terms of
Speaker 1: how difficult it is to get something new going. So
Speaker 1: so let's talk a little bit about what the VIX
Speaker 1: index does and some of the misunderstandings around it. When
Speaker 1: when you talk to people, especially traders on the equity side,
Speaker 1: they look at it as the fear index. It measures volatility,
Speaker 1: but but to be more precise, it's really measuring volatility expectations. Right, Yes,
Speaker 1: I think the the word fear gauge or fear index
Speaker 1: is actually pretty accurate. But you're also absolutely correct that
Speaker 1: what the VIX actually is is the market's expectation of
Speaker 1: alterality over the next thirty calendar days. And so it
Speaker 1: is a it's a market price, and inevitably because the
Speaker 1: volatality cannot mathematically go below zero, there's no such thing
Speaker 1: as vaultality below zero. But it's unlimited on the upside,
Speaker 1: you know, there's no limit to how high vaultality can be.
Speaker 1: Then if you go to the market and say, hey,
Speaker 1: give me a price for the next thirty days of alterality,
Speaker 1: it's generally going to overestimate because it's going to have
Speaker 1: to protect itself a bit against the possibility of huge
Speaker 1: swings upwards and the fact that there's a floor. It
Speaker 1: can't go below zero. So your description is absolutely correct.
Speaker 1: It's the market's expectation of realized volatility over the next
Speaker 1: thirty calendar days, but it has some features built into it.
Speaker 1: It's always going to overestimate because you're effectively selling insurance
Speaker 1: if you're selling the vics and and people don't sell
Speaker 1: insurance cheap for the most part. That makes a whole
Speaker 1: lot of sense. I know that a lot of traders
Speaker 1: seem to conflate volatility with risk. How do you define
Speaker 1: the different is between the two. It's a very good question,
Speaker 1: and I think you know something I've thought about a
Speaker 1: lot over the years, um and there's many different versions
Speaker 1: of this. I think so most models, most risk models
Speaker 1: do estimate volatility. They give you a var number, which
Speaker 1: is basically a manipulated volatility number, or a risk in
Speaker 1: stand deviations, or an expectation of loss or something like that,
Speaker 1: and they're useful numbers, but in the end, I'm pretty
Speaker 1: sure that both you and certainly I would never clue
Speaker 1: what the volatility of our personal portfolios was last year
Speaker 1: in a very volatile year. But we have a pretty
Speaker 1: good idea of what the worst point is, you know,
Speaker 1: when when we have the most losses of most pain
Speaker 1: in our portfolios, and that's got nothing to do with voltility.
Speaker 1: That's a draw down. So in the end, actually the
Speaker 1: risk that a lot of us really experience and worry
Speaker 1: of out his drawdowns. It's not volatility, which is mathematical
Speaker 1: formula which describes the shape of a of a distribution,
Speaker 1: and that I think is something which is difficult because
Speaker 1: estimating drawdowns is extremely hard. Estimating volatility is actually fairly straightforward.
Speaker 1: But the two don't really connect. And so why do
Speaker 1: people estimate volatility? Because it's useful. It gives you how
Speaker 1: wide the distribution will be. You can estimate it quite accurately,
Speaker 1: you can forecast it quite well as well. You can
Speaker 1: forecast volatality much better than you can forecast returns. So
Speaker 1: all of that is useful. But what's not useful is
Speaker 1: that we don't really worry in the end about what
Speaker 1: I've altilt he was last year or what it will
Speaker 1: be next year. What we really worry about is how
Speaker 1: much we lost or how much we might lose. That
Speaker 1: sort of that that pain threshold, and volatility doesn't really
Speaker 1: connect with that. So unfortunately, really useful statistic drawdowns very
Speaker 1: hard to estimate, and people don't really estimate it. The
Speaker 1: other statistic volatility is useful, but it's in my view,
Speaker 1: not the most useful estimate of of of risk because
Speaker 1: we actually experience it. That's that's really kind of intriguing.
Speaker 1: So so not only can you go long the vix um,
Speaker 1: but some people can go short the VIX not not
Speaker 1: exactly um a hedge if you have a long portfolio,
Speaker 1: but I guess if you're short, maybe that that useful.
Speaker 1: What do you think of of how people have been
Speaker 1: using the VIX, either as a risk management tool or
Speaker 1: as a way to UM get some non correlated exposure
Speaker 1: to their to their equity holdings. Well, I think there's
Speaker 1: a few things in there. Firstly, the the VIX and
Speaker 1: the price of futures on the VIX will nearly always
Speaker 1: disagree in generally, the futures will be higher than the
Speaker 1: current level. So you know, as we're speaking now, the
Speaker 1: VIX is a round sixteen better futures contract three months
Speaker 1: out is trading at almost twenty one, so five points higher,
Speaker 1: and that is a normal state of affairs. And as
Speaker 1: we talked about earlier on voltialty, because voltality can't go
Speaker 1: below zero than people, and it can go to an
Speaker 1: unlimited upside level than people, generally the market will overestimate
Speaker 1: volatilty to give it a little bit of an insurance
Speaker 1: premium in there. Now, in terms of trading and investing
Speaker 1: with the VIX, I think this thing is quite important
Speaker 1: to know. So if you buy an ETS on the VIX,
Speaker 1: for example, then it is going to have to hedge
Speaker 1: itself with these futures contracts, and these futures contracts will
Speaker 1: trade a lot higher. I said twenty one for September,
Speaker 1: verus is sixteen now, so that's almost a thirty percent higher.
Speaker 1: And by September, one of two things has to happen.
Speaker 1: Either the VIX has to go up to twenty one,
Speaker 1: or the futures contract will go down to sixteen. More lightly,
Speaker 1: the futures contract will go down to sixty. And so
Speaker 1: I think people often don't understand this when they're buying
Speaker 1: a vix et f that they're not buying the level
Speaker 1: of the vix they see on the on the screen
Speaker 1: on their television screens or on their Bloomberg terminals or
Speaker 1: wherever they see it. They're they're buying effectively a future
Speaker 1: on the vix which generally is trading a much higher level,
Speaker 1: and so they will have expected losses built in. That
Speaker 1: is something which then some people on the other side
Speaker 1: have said, well, this is very exciting. You know I
Speaker 1: can I can sell the vix at twenty one in September,
Speaker 1: and I expect it will go to sixteen. I can
Speaker 1: make thirty percent in three months. And they're absolutely right,
Speaker 1: you can and most likely will make thirty percent in
Speaker 1: three months. But and the butt is a big thing.
Speaker 1: If if something bad happens between now in September, then
Speaker 1: you know, you can make very very significant losses because
Speaker 1: the vix can just, you know, very very quickly go
Speaker 1: up to very high levels. So what people often do
Speaker 1: is they say that buying the vix that gives me
Speaker 1: some protection against crises. If there's a crisis, then it
Speaker 1: will lightly go up, and they're right, but they do
Speaker 1: have to understand that it's not the VIX going from
Speaker 1: the current sixteen to say eighteen. It's got to go
Speaker 1: above that twenty one that's priced in in September before
Speaker 1: you make any money. Um. But it can be a
Speaker 1: protection strategy and insurance strategy. And then you've got people
Speaker 1: on the other side who, let's say, look, I don't
Speaker 1: expect something bad to happen, and I can earn this
Speaker 1: very large insurance premium UM if I'm prepared to go
Speaker 1: short the VIX and you of course can do that
Speaker 1: through futures contracts, but you can also do it through ets.
Speaker 1: I think my real observation on this those you know,
Speaker 1: I've tried to give as clear an explanation as I
Speaker 1: can of how this is working, but it's quite subtle.
Speaker 1: This is not a simple thing. And I think a
Speaker 1: lot of people that trade the VIX cts don't really
Speaker 1: understand what's going on underneath the surface of the TS contract.
Speaker 1: And there's a lot going on underneath the surface. So
Speaker 1: so let's talk a little bit about um, people trading
Speaker 1: products that they didn't really understand. And who better to
Speaker 1: ask you the co inventor of the VIX back in
Speaker 1: ten we saw the notes UH that were based on
Speaker 1: the VIX and Credit Swiss was one of the larger
Speaker 1: underwriters of these. UM just blow up and sent the
Speaker 1: VIC spiking. I kind of remember we kissed fifty I
Speaker 1: could I could be wrong about that, UM, but that
Speaker 1: whole series of products, those short term E t N
Speaker 1: s UM x I V was one and s v
Speaker 1: x Y was another. UM. They just blew up spectacularly.
Speaker 1: As you're watching this from from your seat, what are
Speaker 1: you thinking about? Gee? Look what look? I lent the
Speaker 1: keys to the car to the kids and they seem
Speaker 1: to have wrecked it. Oh. I think I think your
Speaker 1: description that Barry is pretty fair. When we did the works,
Speaker 1: I'm Aaron. I did the work back in two thousand
Speaker 1: three two four. The boss that asked me to do
Speaker 1: the various other things that we talked about said, you know,
Speaker 1: you should look at creating an ETF on this thing,
Speaker 1: and so we did. Then we got together with one
Speaker 1: of the very big E t F providers and they said, well, look,
Speaker 1: maybe you could do some modeling of how this thing
Speaker 1: will behave and we did that and we concluded this
Speaker 1: is just not a good product. You know, people it's
Speaker 1: got some nasty characteristics, and so we decided, along with
Speaker 1: that large firm, that we should not sell E T
Speaker 1: s on the vix. Now, other people took a different view,
Speaker 1: and so your analogy of kind of the kids getting
Speaker 1: the keys to the car more or less accurate. Actually,
Speaker 1: I think the the the so I don't like the
Speaker 1: E t F products mostly because they're very complicated. They
Speaker 1: look simple on the outside, but underneath them they're very complicated,
Speaker 1: and I don't think people always understand all that complexity.
Speaker 1: So the events that happened in fabruy E two thousand
Speaker 1: eighteen were these short vix et s and the short
Speaker 1: vix ets trying to earn this insurance premium. So lots
Speaker 1: of people, lots of all streets were happy owners of
Speaker 1: these short vix ets. The problem with them is that
Speaker 1: what the t F does then, is it it issues
Speaker 1: units to people like you or I UM and then
Speaker 1: has to sell futures contracts against it. If the price
Speaker 1: of the vix starts going up, it needs to start
Speaker 1: buying those contracts back, and if it goes up a lot,
Speaker 1: it needs to buy a heck of a lot of them.
Speaker 1: And so you have some volatility very late in the closing,
Speaker 1: towards the end of the day UM, and I think
Speaker 1: it was February five, if I remember rightly, two thousand
Speaker 1: eighteen and UM, and they needed to buy a heck
Speaker 1: of a lot of futures contracts in a very short
Speaker 1: period of time. What did that do It push the
Speaker 1: thing up even more, and so it kind of created
Speaker 1: its own volatility and its own noise UM in that period.
Speaker 1: So I think that it's a pretty good example of
Speaker 1: an unintended consequence from a financial product. And again, you know,
Speaker 1: it's quite a complicated concept. Along vix CTF is complicated
Speaker 1: as short vic CTF is very complicated, and I think
Speaker 1: from that perspective and people probably had some surprising results
Speaker 1: that you know, a lot of people lost a lot
Speaker 1: of money, some of the issues of these short vix
Speaker 1: cts made a lot of money UM at the same time,
Speaker 1: and it felt like a pretty bad state of affairs
Speaker 1: to me. Let's talk a little bit about your book, UM,
Speaker 1: which was written with Harvey Campbell, who was a prior
Speaker 1: guest and and just a delightful individual. What compelled you
Speaker 1: guys to write this book? This is a pretty UM
Speaker 1: in the Weeds Inside Baseball sort of sort of book.
Speaker 1: Yeah you're you're right, it's absolutely in the Weeds type
Speaker 1: of book. What got us going on this was really
Speaker 1: a sense that for many portfolio managers, risk management is
Speaker 1: something which comes afterwards they build their portfolio, and then
Speaker 1: the risk team do something later on and tell them
Speaker 1: whether it's okay or not. And we thought that that's
Speaker 1: actually a very bad way to run portfolios. In a
Speaker 1: much better way is to have the alpha side of
Speaker 1: building portfolios in the risk side to be equal partners.
Speaker 1: And that's something that we've really tried to build as
Speaker 1: a culture of a man group that risk is part
Speaker 1: of the investment team. It's, as they sometimes put it,
Speaker 1: if risk is the police for other people that come
Speaker 1: kind of knocking on your door telling you've done something wrong.
Speaker 1: That's that's not really a good way of running portfolios.
Speaker 1: What do you really want is as you're building the portfolios,
Speaker 1: the risk and the alphabets to to come as as
Speaker 1: equal partners. And the reason for that is, firstly, huge
Speaker 1: amounts of damage tend to be done when there is
Speaker 1: bad risk management in stress periods that people aren't prepared
Speaker 1: for those stress periods, they make bad decisions, and those
Speaker 1: stress periods lose a lot of money, often crystallize losses,
Speaker 1: that sort of thing. So so firstly, if you don't
Speaker 1: have a proper risk approach to building portfolios when you
Speaker 1: enter chop your periods in markets, then you're likely to
Speaker 1: make bad decisions. That The second, which I mentioned earlier
Speaker 1: on is kind of surprisingly. It's actually much easier to
Speaker 1: forecast risk than it is to forecast returns, and you
Speaker 1: can use that to give yourself more stable portfolios. And
Speaker 1: so we were really trying to say that you can
Speaker 1: blend the alpha side of portfolio management with the risk
Speaker 1: side as equal partners, and you'll build better and more
Speaker 1: stable portfolios which will actually do better over the long
Speaker 1: run because you won't end up making bad decisions during
Speaker 1: the during the stress periods. So let's talk about that
Speaker 1: last quote, it's easier to forecast risk then return. I'm
Speaker 1: going to play devil's advocate and take the other side
Speaker 1: of that argument. Hey, we know over long periods of
Speaker 1: time what historical asset class returns are, and so we
Speaker 1: can reasonably forecast eight percent over twenty years for equities UM,
Speaker 1: but we can forecast the sort of UM black swans
Speaker 1: that show up every now and then that are just
Speaker 1: completely unexpected and are amongst the quote unknown unknowns. Tell
Speaker 1: me what's wrong with that perspective that looks at extrapolating
Speaker 1: long term returns versus hey, we have no idea what
Speaker 1: the next random events is going to be. Yeah, I
Speaker 1: think what's wrong with that is that you've tried to
Speaker 1: compare forecasting very long run returns and then said that
Speaker 1: I need to forecast short term risk. And so let
Speaker 1: me sort of decompose that a little bit more. Many times,
Speaker 1: people when they when they forecast returns, and we're all
Speaker 1: guilty of this, we end up being pretty influence by
Speaker 1: what happened in the last month or two, and so
Speaker 1: we say, you know, people, many people, for example, are
Speaker 1: pretty positive about equity market returns globally and maybe especially
Speaker 1: in the US. And one of the reasons that really
Speaker 1: is that we've had good returns, you know, really since
Speaker 1: March shows since April last year, and people are extrapolating,
Speaker 1: they're just stretching forwards. But if you look at the data,
Speaker 1: and if you look at the data over long periods
Speaker 1: of time, you find the correlation between past returns and
Speaker 1: next month's returns is about zero in almost all markets
Speaker 1: around the world, equity markets, bond markets, commodity markets just
Speaker 1: about zero. There has no last month's returns have close
Speaker 1: to zero predictive ability of telling you what next month's
Speaker 1: returns are going to be. If you now do that
Speaker 1: on risk and you calculate the fertility of markets last
Speaker 1: month or the month before or the month before that,
Speaker 1: lets hee has very high predictive ability. So people like
Speaker 1: me would call this the serial correlation. So the serial
Speaker 1: correlation the returns in other words, from last month or
Speaker 1: two months ago or three months ago to this month's
Speaker 1: returns is close to zero, you might as well call
Speaker 1: it zero. So close to zero. If you do the
Speaker 1: same thing in volatility, and you can do this across
Speaker 1: equity markets in the US, but also across other parts
Speaker 1: of the world, bond markets, commodity markets, currency markets, that
Speaker 1: zerial correlation is around forty percent. So that's telling you
Speaker 1: that last month's vulatility is actually telling you quite a
Speaker 1: lot about this month's faultiality. And so whilst you might
Speaker 1: be able to make a statement about you know, twenty
Speaker 1: year expected returns um I suspect both you and I
Speaker 1: will be, you know, at least twenty years older, and
Speaker 1: that your point and whether anybody will really hold us
Speaker 1: to it or whether it's the useful observation will be
Speaker 1: tricky to to to really evaluate. But for most people
Speaker 1: they need to have nearer term forecasts in order to
Speaker 1: be able to make their decisions. And I think they
Speaker 1: make a mistake that they think they can forecast returns
Speaker 1: often by extrapolating from past months or turns, And the
Speaker 1: evidence is that you shouldn't extractly. There's no extrapolation to
Speaker 1: be done. Whilst a risk you can that that is
Speaker 1: very parallel to what we were talking about earlier with
Speaker 1: price versus time series. So equities follow a random walk,
Speaker 1: but volatility and risk tends to be persistent and enhance
Speaker 1: more likely to have some time series correlation. I think
Speaker 1: that's what I'm hearing, Am I am I saying that right? Yeah, absolutely, absolutely,
Speaker 1: the the there's a very small and what the c
Speaker 1: t a s try and do, but the futures trend
Speaker 1: followers is that there's there's a very small effect to
Speaker 1: being able to pick up some persistence in returns, but
Speaker 1: you have to do it across hundreds of markets and
Speaker 1: you have to do it consistently over time, and you'll
Speaker 1: just managed to eke out a little bit of alpha
Speaker 1: about doing that, but altility is much easier. So what
Speaker 1: should managers do proactively to prepare for are not the
Speaker 1: known risk but the unknown risks? And and really just
Speaker 1: over the past twenty years, we had nine eleven, we
Speaker 1: had the Great Financial Crisis, we had the VICS meltdown,
Speaker 1: and more recently we had the COVID pandemic. How how
Speaker 1: can a fund manager build protection against these black swans
Speaker 1: into their process? Well, so I think the first thing
Speaker 1: that I portfolio managed to do is realize that you
Speaker 1: cannot forecast these events. And you know, I speak, at
Speaker 1: least I used to speak at conferences a lot when
Speaker 1: we've still had conferences, and you know, people would ask me, well,
Speaker 1: what's the black swan event that's going to happen this year?
Speaker 1: And I also that was the most ridiculous question because
Speaker 1: you know, clearly all of these events are unfoecastable, and
Speaker 1: generally the ones that you forecast will happen don't end
Speaker 1: up being the thing that takes place. So I think
Speaker 1: the first thing is to show a lot of human
Speaker 1: tear about our ability to forecast what what the bad
Speaker 1: events will be. The only thing I can really feel
Speaker 1: confident about is that there will be more bad events
Speaker 1: in the future. You know, it seems that they just
Speaker 1: keep coming, but they have different shapes and forms. And
Speaker 1: maybe as a side anecdote on that, we have an
Speaker 1: excellent risk manager at Man Group and at the end
Speaker 1: of two thousand nineteen and a sort of planning exercise
Speaker 1: who was giving all the risk that could affect markets.
Speaker 1: And you had about twenty of these things, and one
Speaker 1: of them was epidemic. And I looked at this at
Speaker 1: the end of two thousand nineteen and I said, epidemic. Well,
Speaker 1: I mean, like, you know, I don't know much about epidemics,
Speaker 1: but I can't say it's impossible. But it doesn't seem
Speaker 1: very lightly. So we did exactly nothing about the risk
Speaker 1: of epidemic. And if he got the word almost right,
Speaker 1: pandemic instead of epidemic. But you know, even if you
Speaker 1: have it on your list of things, which my risk
Speaker 1: manager did, um, it's very hard to act on it.
Speaker 1: So I think humility in terms of ability to forecast
Speaker 1: these things really important. If you think you can forecast,
Speaker 1: you'll probably make them. State by protecting yourselves against the
Speaker 1: wrong thing. Once you've got over that and said likely
Speaker 1: it probably can't forecast the next bad thing, then I
Speaker 1: think what becomes much more important is, Okay, now you
Speaker 1: need a strategy that is going to be relatively insensitive
Speaker 1: to the nature of the bad thing. In other words,
Speaker 1: whether it's a tech bubble collapse or um credit crisis,
Speaker 1: or you know, something entirely different to war, or a pandemic,
Speaker 1: or it could be any of these things. Obviously you
Speaker 1: need a strategy which is going to be robust to
Speaker 1: any of those things coming along. And my own suggestion
Speaker 1: on this would be that it's it's too expensive to
Speaker 1: buy put options. Buying put options on the sp you
Speaker 1: can do every now and then, but you can't do
Speaker 1: it all the time. It's just it just becomes too costly.
Speaker 1: And so you're gonna have to have a strategy which
Speaker 1: relies a little bit more on either assets in your
Speaker 1: portfolio which you think are likely to do well in
Speaker 1: a stress period. That could be gold in my view,
Speaker 1: it's not terribly reliable. It could be gold. It could
Speaker 1: be U strategury bonds or other government bonds around the world.
Speaker 1: Of course, if the problem emerges from the bond market,
Speaker 1: it's not really going to help you. Um. And you know, people,
Speaker 1: I think forget that they have definitely been problems from
Speaker 1: the bond markets. You just need to look back a
Speaker 1: little bit to the early es or before then to
Speaker 1: see that actually there were plenty of problems that came
Speaker 1: from the bond market. And or you could have some
Speaker 1: sort of trading strategy, and certainly for me this time
Speaker 1: series momentum, which we touched on in our first segment,
Speaker 1: this idea that you can build a trading strategy like
Speaker 1: the c t as have done, which relies on just
Speaker 1: a little bit of persistence in returns and looks for
Speaker 1: them everywhere. That can be a very good way of
Speaker 1: building a defensive strategy. So, in other words, if we
Speaker 1: describe a crisis, and I can't tell you whether it
Speaker 1: comes from a war, credit crisis, an epidemic, something else,
Speaker 1: but typically in a crisis like that, you will tend
Speaker 1: to have equities going down, you will tend to have
Speaker 1: bonds going up, you'll tend to have gold going up. Um.
Speaker 1: It's a bit harder to tell what might happen to
Speaker 1: energy prices, but those moves tend to persist for a bit.
Speaker 1: They tend to know equities fall and then they keep falling,
Speaker 1: and bonds might go up and they keep rising, and
Speaker 1: gold the same thing. And you can build a strategy
Speaker 1: which encapsulates and tries to capture that effect. Then you
Speaker 1: can build something which is robust and is not depending
Speaker 1: on your ability to put your finger on what the
Speaker 1: next bad event might be. So in the book, you
Speaker 1: you get into the nitty gritty, you you go over
Speaker 1: details of a new risk management approach to portfolio design.
Speaker 1: I want to ask, how did those strategies do in
Speaker 1: back tests looking at OH eight or nine, and how
Speaker 1: do they do in the real war world in March. Well,
Speaker 1: so for us, actually, I don't think O eight O
Speaker 1: nine really was back test because we were actually trading
Speaker 1: most of these strategies at that time. But so I
Speaker 1: think we actually have pretty good life experience. And the
Speaker 1: first thing I should say, March of was an extraordinary crisis,
Speaker 1: and all all crises are extraordinary, but one of the
Speaker 1: things which was most extraordinary about March was that it
Speaker 1: markets fell very quickly. What we've seen that before, but
Speaker 1: then they reverted remarkably quickly, and really the most similar
Speaker 1: crisis in terms of market action that you can put
Speaker 1: your finger on since the Second World War was the
Speaker 1: October seven crisis, so that that very rapid fall you
Speaker 1: had followed by an almost equally rapid recovery. So so
Speaker 1: that might say, well, you know, if you fitted, if
Speaker 1: you've sort of tested your crisis protection on all these
Speaker 1: slower crises, then maybe you wouldn't do too well in
Speaker 1: this faster crisis. And it's actually not what we found.
Speaker 1: So we found that, um, we had you know, quite
Speaker 1: good strength of our strategies during um the March April period.
Speaker 1: So for example, futures trend firing, something we talked about
Speaker 1: quite a lot, um did you know, really rather well
Speaker 1: in March and April of last year. But we also
Speaker 1: talk about for example, rebouncing and and and trying to
Speaker 1: stop rebouncing, which can can have the nasty effect of
Speaker 1: buying the losers, and then if the losers carry on falling,
Speaker 1: then you will, damn it, you just bought a whole
Speaker 1: bunch of losers in time for another month's falls. And
Speaker 1: we found that if you if you have UH strategies
Speaker 1: which try and control your rebouncing, but they have to
Speaker 1: have relatively rapid they have to be quite fast strategies,
Speaker 1: and that would be my real point. So most of
Speaker 1: our protection strategies are quite fast. The signals are quite quick.
Speaker 1: They use data that goes back typically a few weeks um,
Speaker 1: and they can move positions around quite rapidly. And that
Speaker 1: worked pretty well in March and April last year. If
Speaker 1: you do much slower strategies, you would not have had
Speaker 1: the protection characteristics right and and and to put some
Speaker 1: numbers on on the speed of March, we smp um.
Speaker 1: That was the fastest drop in history, and I believe
Speaker 1: it was just a day under a month, maybe a
Speaker 1: few days under a month. And then the recovery from
Speaker 1: the end of March beginning of April was back to
Speaker 1: break even by August. That that's a pretty astounding turnaround,
Speaker 1: arguably faster than the recovery from seven, which was itself
Speaker 1: pretty quick, wasn't it Absolutely so, it was just totally extraordinary.
Speaker 1: And from that perspective, you know, the past didn't give
Speaker 1: you a particularly good guide as to how how that
Speaker 1: crisis would would unfold. And then maybe that sort of
Speaker 1: retrates my point a little bit that you can't you
Speaker 1: can't build protection strategies which are really trying to put
Speaker 1: your finger on exactly what's going to happen. You have
Speaker 1: to you have to be aware that your forecasting ability
Speaker 1: is poor, UM, and you've really got to have a
Speaker 1: strategic response. And that's why we call the book strategic
Speaker 1: risk Management. It's really a set of strategies that the plan.
Speaker 1: And you can't make the plan up on the fly.
Speaker 1: You know what. You really the worst thing you could
Speaker 1: have been doing last year is making up your protection
Speaker 1: strategy during March. It was too late by that point.
Speaker 1: You have to make up your your protection strategy in
Speaker 1: the months and years before then, and then you had
Speaker 1: to be implementing it during March. So there was a
Speaker 1: quote of yours really liked, and I want to ask
Speaker 1: you about this quote. We are in a riskier environment
Speaker 1: than we have been in the past twenty years for
Speaker 1: the foreseeable future. Unquote the past twenty years. Really there
Speaker 1: were a lot of risky events that took place, from
Speaker 1: nine eleven to the Great Financial Crisis to the pandemic.
Speaker 1: What makes this a riskier environment and why do you
Speaker 1: see this as um being a persistent risk for the
Speaker 1: for the next foreseeable future. So the reason that I
Speaker 1: think we're in this highly or much riskier environment that
Speaker 1: we've been in is because markets are much less diversified
Speaker 1: than at any point in my career. And so you know,
Speaker 1: I started trading markets when I was at high school
Speaker 1: in the late nineties, and at that time people got
Speaker 1: very worried that the Japanese market was round of the
Speaker 1: MSCI world. Well, today the US market is two thirds
Speaker 1: of the MSCI world. So it's and that's the highest
Speaker 1: way to to ever be in such the highest way
Speaker 1: that any one country has ever been in the global
Speaker 1: Equity Index. And then if you now dig in within
Speaker 1: the US market, and this is a little tougher to do,
Speaker 1: but if you dig into the proportion of the U
Speaker 1: s ecty market made up by tech, and the reason
Speaker 1: it's difficult is they changed the classification system a couple
Speaker 1: of years ago, then you'll see the tech is a
Speaker 1: bigger portion of the US equity market than it has
Speaker 1: ever been, including in the late in the tech bubble.
Speaker 1: So you have an incredibly concentrated equity market both globally
Speaker 1: into the US and also by sector within the US.
Speaker 1: And for me, that means that you know, this is
Speaker 1: not sort of looking at the vix today, tomorrow, yesterday, whatever.
Speaker 1: More strategically, the market feels much more likely to be
Speaker 1: able to produce unpleasant outcomes because the only freelance you
Speaker 1: haven't financed the diversification you have the least diversification I've
Speaker 1: ever seen. Huh, that's kind of interesting. So we have concentrated,
Speaker 1: non diversified portfolios. And one of the things we've seen
Speaker 1: is domestically, the US seems to be a higher poor
Speaker 1: proportion of global equity markets. And then within the US,
Speaker 1: the text actor continues to increase its waiting when the
Speaker 1: sp What does that mean for the future of of
Speaker 1: risk and managing it? Well, I look, I think it
Speaker 1: means firstly that you know, you need to be just
Speaker 1: aware of this, that that the market is so heavily concentrated.
Speaker 1: Um the I think the what can you do about it?
Speaker 1: It is probably the real question. And I think that
Speaker 1: this is a pretty big challenge for people because historically
Speaker 1: the answer was, well, if if I want to build
Speaker 1: a balanced portfolio, then I'll hold some equities, and then
Speaker 1: I'll hold some government bonds, often US treasury bonds um
Speaker 1: as the sort of as the ballast as the thing
Speaker 1: which gives a bit of stability to my equity portfolio.
Speaker 1: But where we are today, I think people are are
Speaker 1: much less convinced that treasury bonds will be the ballast
Speaker 1: that they have been historically. In particular, you know, if
Speaker 1: we continue to get high inflation numbers, then I don't
Speaker 1: think anybody is good to argue that high inflation is
Speaker 1: good for government bonds. It's is clearly bad for government bonds.
Speaker 1: And so your challenge is that the way you built
Speaker 1: stable portfolios in the past, there's balancing of equities and bonds,
Speaker 1: is really much less suited to the current environment than
Speaker 1: it was to the to the past environment. So what
Speaker 1: can you do about it? But I think what most
Speaker 1: people that I speak with at least to think of
Speaker 1: doing about it is saying, well, I need to own
Speaker 1: something other than treasury bonds to balance out my equity risk.
Speaker 1: And for some people that's private equity, for some people
Speaker 1: that's hedge funds or alternatives. For some people it's infrastructure
Speaker 1: or housing or other forms of real estate. But I
Speaker 1: think it's reasonably clear in my mind that you need
Speaker 1: to you need to think about balancing your portfolio, and
Speaker 1: then you need to think pretty carefully about whether bonds
Speaker 1: give you the same level of protection or balancing them
Speaker 1: may then they would have done in the last twenty
Speaker 1: thirty years. From my own perspective, if I look back
Speaker 1: at the last twenty years in particular, it's a very
Speaker 1: dangerous period to look at when you look at equities
Speaker 1: and bonds. Over the last twenty years, when equities went down,
Speaker 1: bonds nearly always went up in price, and and so
Speaker 1: we've got used to this idea of bonds being the
Speaker 1: protecting asset. But if you look before then, and you
Speaker 1: can look back for in hundreds of years worth of data,
Speaker 1: both in the US and then also the UK, whether
Speaker 1: bond market started earlier than the US market, you see
Speaker 1: that for almost all of history, except for the last
Speaker 1: twenty years. When equities went down, bonds went down at
Speaker 1: the same time. And so for me, I think it's
Speaker 1: a very important question for investors, which is you need
Speaker 1: to balance the risk in your portfolio. Are bonds the
Speaker 1: answer to it? In my view, they're probably much less
Speaker 1: the answer than they were historically, So then you need
Speaker 1: to look at other asset classes and think more creatively
Speaker 1: about how you do that. And the fact that I
Speaker 1: think that equities public equities are as risky as they've
Speaker 1: ever been from a strategic perspective, means that question is
Speaker 1: actually as important as it's ever been to think about. Huh,
Speaker 1: that's that's really kind of intriguing. Um. So, So, sticking
Speaker 1: with the theme of risk, what does this concentration mean
Speaker 1: and this lack of diversific asian mean in terms of,
Speaker 1: you know, calibrating what we should be expecting. Should we
Speaker 1: be looking for larger moves in the future, or you know,
Speaker 1: how do you approach this um philosophically when you're thinking
Speaker 1: about what you want to do with your portfolios? Yeah,
Speaker 1: I think that you know, right now or in a
Speaker 1: relatively sort of quiet summer period, and so markets have
Speaker 1: been relatively stable. But but I looking beyond just a
Speaker 1: short term than this lack of diversification, markets definitely means
Speaker 1: that we should expect to see bigger moves in both directions.
Speaker 1: Just to be clear, both upwards and downwards. Um, it
Speaker 1: does not mean that markets couldn't go up a whole
Speaker 1: bunch more from here. It just means that they're lately
Speaker 1: to be more roletile than we've been used to in
Speaker 1: the last few years. And so I think it's really
Speaker 1: having a plan of action and being prepared for how
Speaker 1: you respond to that, because in the end, for most
Speaker 1: of us, the big up moves, I mean, maybe we
Speaker 1: were underinvested in we have a bit of regret about it,
Speaker 1: but you don't have much pain from the big up moves.
Speaker 1: It's the big down moves that that caused all the
Speaker 1: pain and caused cause the bad decisions to be made.
Speaker 1: So I think it's having a plan of action. And
Speaker 1: I would argue that you know, here we are sitting
Speaker 1: in a relatively comfortable moment in markets currently, and you know,
Speaker 1: if we're fortunate, then the summer will be enjoyable for
Speaker 1: all of us and not too not too noisy. That's
Speaker 1: a great time to be thinking about your plans for
Speaker 1: how you'd respond if there was a if there was
Speaker 1: a negative altality event. Really really quite quite interesting. So
Speaker 1: let's talk a little bit about some strategies you mentioned,
Speaker 1: alternatives like private equity and hedge funds. What can one
Speaker 1: do to hedge against the risk of increased volatility in
Speaker 1: the future. Well, I think you know, you can do
Speaker 1: direct hedges, but they tend to be very expensive. So
Speaker 1: you could go and buy futures on tracts on the
Speaker 1: VIX or something like that, but they will turn out
Speaker 1: to be very costly few over time, as we've talked
Speaker 1: about now earlier sections, So more likely what you need
Speaker 1: to do is to think about assets that will just
Speaker 1: behave differently um to equity markets. And as equity markets
Speaker 1: have become more concentrated, especially into tech um it's uh
Speaker 1: to be precise about that. It's tech and communication services
Speaker 1: of the two classifications that people use today. So as
Speaker 1: it's become more concentrated into that into those sectors, then
Speaker 1: you need, I think to think about things which will
Speaker 1: be not so affected by a negative price move in
Speaker 1: those and I think that private equity in the end,
Speaker 1: it has a lot of equity market exposure into it,
Speaker 1: but you tend to see the price action more slowly.
Speaker 1: But infrastructure that's you know, could behave very differently. Hedge funds,
Speaker 1: if they're good, hedge funds should have lots of protection
Speaker 1: strategies built in and lots of short holding as well
Speaker 1: as long holdings, and so should be less sensitive. We
Speaker 1: talked about c T A S a little bit, one
Speaker 1: of the few strategies which has a right tail to
Speaker 1: it run the left tail that could be part of
Speaker 1: your list of strategies as well. I think the core
Speaker 1: things from my perspective would be recognize that you're not
Speaker 1: gonna build a forecast the next difficult events number one.
Speaker 1: Number two, you can't forecast it, then don't tin all
Speaker 1: your diversification on a single thing. Have a range of
Speaker 1: protection strategies out there. And number three would we make
Speaker 1: sure those protection strategies are not too expensive to run.
Speaker 1: And that, of course is the disadvantage of buying put
Speaker 1: options on the SMP or buying VIC futures that they're
Speaker 1: very expensive to run. So you need something that you
Speaker 1: can actually put up with for a period of time.
Speaker 1: I often see people that go and buy those more
Speaker 1: expensive strategies and they do it for six months or
Speaker 1: twelve months or eighteen months, and then they give up.
Speaker 1: And oftentimes they managed to give up just before the
Speaker 1: next bad event happens. That's been a terrible outcome. Yeah.
Speaker 1: With with earthquake insurance, it turns out that there's always
Speaker 1: a spike right after an earthquake, which is the least
Speaker 1: likely time for there to be another earthquake, and by
Speaker 1: the time enough time elapses where risk has gone up dramatically.
Speaker 1: People have forgotten about it and they let that um,
Speaker 1: that risk lapse. Um, I wanna. I want to emphasize
Speaker 1: something you said, And it comes back to that question
Speaker 1: someone ask you at the conference. You're not really thinking
Speaker 1: about the specifics of the potential risk. It almost doesn't
Speaker 1: matter if it's a bond risk or an equity miss
Speaker 1: risk or some geopolitical risk. It's hey, we can expect
Speaker 1: these asset classes to go down, these st classes to
Speaker 1: go up with a whole lot of increase in volatility.
Speaker 1: The black swan matters less than your preparation for some
Speaker 1: unforeseen event? Am I stating that correctly? Absolutely? Yes. And
Speaker 1: we've talked a little bit about how, and people ask
Speaker 1: me at conferences forecast the next black swan. I think
Speaker 1: it's actually the question I get asked the most. Because
Speaker 1: I'm a strong believer in this phrase that there's no
Speaker 1: such thing as a bad question. But I think that
Speaker 1: one actually might be the bad question, because by definition,
Speaker 1: you can't forecast the black swan. That's kind of what
Speaker 1: a black swant is forecast of all. So So let's
Speaker 1: talk a little bit about the technology you guys use
Speaker 1: to create these models too, and to model out risks
Speaker 1: and and other strategies. You build all that stuff in house. There,
Speaker 1: you're not really a big buyer of off the shelf
Speaker 1: UM risk management technology. Tell us a little bit about
Speaker 1: your approach, which seems to be pretty comprehensive to thinking
Speaker 1: about and planning for unforeseen risk. Yes, so, I mean
Speaker 1: that's the first thing i'd say is that we essentially
Speaker 1: build all of our own technology. We don't really buy technology.
Speaker 1: We we buy the hardware, of course, but but we
Speaker 1: write all the software, all the code ourselves. And that's
Speaker 1: because we think that the things you can buy off
Speaker 1: the shelf, or everyone can buy it off the shelf,
Speaker 1: and therefore it's not really going to be a competitive advantage.
Speaker 1: It's going to be it's going to be maybe a
Speaker 1: base standard. And not trying to criticize the external products.
Speaker 1: I just think that if you really want to have
Speaker 1: an edge UM in building risk models or building short
Speaker 1: term forecasters of risk or return or whatever, you need
Speaker 1: to write your own code, your own software, and and
Speaker 1: and you need to put a lot of effort into that,
Speaker 1: and you need to create an environment where you can
Speaker 1: hire the best software developers. And I often see people
Speaker 1: saying well, you know, I hired a bunch of quants
Speaker 1: and I had a bunch of developers as if you know,
Speaker 1: that's the sort of a generic thing like buying a
Speaker 1: loaf of bread or something. It's just not it's you know,
Speaker 1: the best developers hundreds maybe even thousands of times as
Speaker 1: productive as the average developers. So getting those best de
Speaker 1: plenty your organization really important, and thinking about why they
Speaker 1: would want to work for you and not want to
Speaker 1: work for somebody else, that's pretty important. UM. So I
Speaker 1: think for us, we felt that we can have an
Speaker 1: edge by building better technology than you can buy off
Speaker 1: the shelf and UM and then in order to get that,
Speaker 1: we've invested a huge amount in providing UM a good
Speaker 1: environment for UH quantitative researchers and technologists to operate in.
Speaker 1: And just to give you a sort of a side
Speaker 1: example of that, we open source quite a lot of
Speaker 1: our code. So that means that, you know, we pay
Speaker 1: our developers to write code for us, and then we
Speaker 1: go and stick it on a website for other people
Speaker 1: to download it if they want. So why on earth,
Speaker 1: you know, what would possess you to do something like that?
Speaker 1: And the reason that we do it is that that
Speaker 1: then provides advertising to people that we're actually really serious
Speaker 1: software develop opers and that we take our code really seriously.
Speaker 1: And if you're a young software developer, you'll probably see
Speaker 1: the stuff that we've published and say, well, you know,
Speaker 1: actually I wouldn't mind working in a place like that
Speaker 1: because code and and and technology and standards and all
Speaker 1: those sorts of things are really high at at this firm.
Speaker 1: So that's how we think about UM investing in technology,
Speaker 1: investing in developers, creating a culture where developers and quant
Speaker 1: researchers really want to work. And the reason for that, UM,
Speaker 1: why would we carry on doing all this investment? It
Speaker 1: is really that you need you It's a very competitive
Speaker 1: business and you need to stay ahead all the time
Speaker 1: and you need to carry on innovating all the time.
Speaker 1: And if you don't, then somebody will eat your lunch.
Speaker 1: So from our perspective, we're always building new models. We're
Speaker 1: always coming up with new approaches to estimate risk. We're
Speaker 1: always worrying about, you know, how can we find a
Speaker 1: new alpha source, what my go wrong? UM? And how
Speaker 1: a market is changing in their structure this uh, you know,
Speaker 1: big effect of more retail investment in retail investors in
Speaker 1: equity markets today, how should we respond to that. That's
Speaker 1: really something which is just a very ongoing and continuous
Speaker 1: form of a place for us to invest, and when
Speaker 1: we try and get the benefits out of out of
Speaker 1: that over long term, m really really kind of interesting.
Speaker 1: Let's talk a little bit about machine learning. You guys
Speaker 1: have been on the cutting edge of that, including a
Speaker 1: collaboration with the University of Oxford at the Oxford Man Institute.
Speaker 1: Tell us what you guys are doing with machine learning
Speaker 1: and does any of this relate back to volatility? So
Speaker 1: what we're doing with machine learning is we're really saying
Speaker 1: that financial markets have patterns in them which you can
Speaker 1: dig out often profit from if you um if you
Speaker 1: look hard enough. The problem in financial markets is that
Speaker 1: the patterns are are pretty weak. You know, they're not
Speaker 1: They're not simple patterns. There are people like me would
Speaker 1: say there's a low signal to noise ratio. There's a
Speaker 1: lot of noise and not very much signal out there.
Speaker 1: So what are we using machine learning for. We're using
Speaker 1: it for a number of different things, but the underlying
Speaker 1: theme is that most models that people use in markets,
Speaker 1: and you could even think of it just as a
Speaker 1: value model. You know, you pee, for example, price over earnings.
Speaker 1: That's a linear model. It seems to sort of assume
Speaker 1: that price goes up in line with earnings. But we
Speaker 1: all know that when you look at markets, if there's
Speaker 1: one thing they don't ever do, it's move in a
Speaker 1: linear or straight line fashion. They move into every shape
Speaker 1: you could imagine except for the straight line. And UM,
Speaker 1: And what machine learning is really trying to do is
Speaker 1: to say, can I find much more subtle patterns? Um
Speaker 1: the straight line, which is what most of finance actually
Speaker 1: ends up using for modeling, And here are some examples
Speaker 1: of that. One that I'm particularly excited about is UM
Speaker 1: what we would call natural language processing, which is having
Speaker 1: machines read text. Now we all know that there's far
Speaker 1: too much for us all to read. You know, nobody
Speaker 1: can read every analyst report, every company earning statement, every
Speaker 1: annual report, attend every investor or day. Those are too much.
Speaker 1: So wouldn't it be wonderful if you could have machines
Speaker 1: to all that reading for you and tell you what
Speaker 1: to think at the end of it. And that might
Speaker 1: sound like science fiction, and at a certain level, I
Speaker 1: think it probably is. Science fiction today. But five years ago,
Speaker 1: if you said, well, machines will process images, will process
Speaker 1: pictures better than humans, I think people would look at
Speaker 1: you a bit funny and say, you know, well, no, no,
Speaker 1: not really. Now you know you have that on your phone.
Speaker 1: You just type a word into your phone, into your
Speaker 1: photos library and just watch it happen in action. It's
Speaker 1: just extraordinary how it will find all the pictures that
Speaker 1: reflect the words that you've that you've typed in. So
Speaker 1: machines definitely do process images better than humans. It's well known.
Speaker 1: For example, processing X ray images looking at for cancers
Speaker 1: is much better done today by humans, by machines and
Speaker 1: by humans. You might want the human at the end,
Speaker 1: but you want the machine to do all the sifting
Speaker 1: type work. And so one example of machine learning is
Speaker 1: getting machines to understand text and tell you, you know,
Speaker 1: go ahead, machine read every analyst report on you know,
Speaker 1: these hundred stocks, and tell me you know, not only
Speaker 1: the analyst reports, but all the newspapers in every language
Speaker 1: around the world, all the ending schools, read all of it,
Speaker 1: and then tell me what to think. Um, that is
Speaker 1: something which today is science fiction, but I don't think
Speaker 1: it will be in five years time. M that's that's
Speaker 1: really kind of intriguing. My last regg your question I
Speaker 1: have to ask you is, so you spent most of
Speaker 1: your career developing quantitative strategies. What are some of the
Speaker 1: biggest changes you've noticed. I think the firstly is actually
Speaker 1: a bit striking what hasn't changed? So a lot of
Speaker 1: things haven't changed. You know, the many of the standard
Speaker 1: models today are basically the same as the standard models
Speaker 1: twenty thirty years ago. We still use black sholes for
Speaker 1: pricing options. We'll still use the Barre risk model for
Speaker 1: calculating equity risk in portfolios. We still send data in
Speaker 1: very similar ways to the way that we sent it
Speaker 1: twenty thirty years ago, for the most part, in really
Speaker 1: terrible file formats. But nobody seems to come up with
Speaker 1: a better convention that everyone will accept. Um. So there's
Speaker 1: a lot of stuff that hasn't changed, But I think
Speaker 1: there are some things which have changed. The first is
Speaker 1: that people often like to sort of characterize the world.
Speaker 1: There's there's the quantz versus the discretionary people. So the quants,
Speaker 1: you know, the model driven people in some sort of
Speaker 1: battle with the discretionary people. And I say, I don't
Speaker 1: view it that way at all. I think that everyone
Speaker 1: is becoming much more quantitative in the way that they
Speaker 1: build and run their portfolios. The tools that we all
Speaker 1: have today on our Bloomberg terminals or on websites or
Speaker 1: products that we can buy from third parties or build ourselves,
Speaker 1: were essentially unimaginable um five or ten years ago, and
Speaker 1: everybody's got them. So give you an example. Fifteen twenty
Speaker 1: years ago, I was building quite sophisticated screening tools that
Speaker 1: would search equity markets for opportunities. Today you can basically
Speaker 1: do what I built on a Bloomberg terminal. So everybody's
Speaker 1: got it, or everybody that's got a Bloomberg terminal has
Speaker 1: got it. So there's been you know, all investors, not
Speaker 1: just the quants, but the discretion marriage as well. They've
Speaker 1: all become more quantitative. We've seen it was trading as well.
Speaker 1: Trading used to be people shouting each other on a
Speaker 1: trading floor. Today it's it's all machines and in almost
Speaker 1: every market around the world, and very sophisticated machines and
Speaker 1: very sophisticated algorithms trading with each other. So what I
Speaker 1: think I've really seen is everything in markets has become
Speaker 1: more quantitative. But then there are some things which have
Speaker 1: been kind of, you know, unattainable. So far, credit markets
Speaker 1: have remained stubbornly sort of immune to being taken over
Speaker 1: by more quantitative strategies. So far, private equity is the same.
Speaker 1: It's it's really done in the same way as it
Speaker 1: was twenty or thirty years ago. Um, And I think
Speaker 1: that will probably change over time. Was certainly starting to
Speaker 1: see that in credit, for example, where the markets are
Speaker 1: starting to trade more like equity markets or futures markets,
Speaker 1: and it is starting to be possible to build the
Speaker 1: same sorts of risk models and the same sorts of
Speaker 1: of alpha models, of return forecasting models in credit that
Speaker 1: you've had in equities for a long time. So I
Speaker 1: think my real thing is that everything has become more quantitative,
Speaker 1: and I think it is going to become a whole
Speaker 1: bunch more quantitative over the next twenty years. Alright, so
Speaker 1: let's jump to our favorite questions that we ask all
Speaker 1: our guests. Tell us what you've been streaming this past year,
Speaker 1: or anything interesting that you're watching on Netflix or Amazon
Speaker 1: Prime or or whatever. Um. I'm not a huge watcher
Speaker 1: of things on streaming services. But you know, I have
Speaker 1: been um watching a rewatching a series of films by
Speaker 1: a famous British actor called Bill Nike, written by a
Speaker 1: playwright called David Hair. The first of them is called
Speaker 1: Page eight and there are quite sophisticated plays about um
Speaker 1: An m I six agent and his life struggles. So
Speaker 1: that's what I've been streaming, and I you can find
Speaker 1: it on on on Netflix. Page eight is the is
Speaker 1: the first of them. Well, we'll definitely check that out.
Speaker 1: Tell me us, tell us a little bit about your
Speaker 1: mentors who helped to shape your career. So I was
Speaker 1: very fortunate in the first fifteen years of my career
Speaker 1: I was at Golden Sacks. It was an outstanding place
Speaker 1: to work. The people that I particularly worked with over
Speaker 1: that period were really two or three folks. Um Manny Roman,
Speaker 1: who today is the CEO of Pimco, I worked with
Speaker 1: pretty much twenty five years, both at Golden Sacks as
Speaker 1: well as at Man Group. Before he went off to PIMCO.
Speaker 1: Worked with a fellow called Garish Ready who went on
Speaker 1: to run a fund of funds business called Prisma Um
Speaker 1: and a fellow called Mark Zorak, who went on to
Speaker 1: be a professor at Cornell, and I think it was
Speaker 1: important to me to have a variety of different people
Speaker 1: to learn from and to sort of build different experiences.
Speaker 1: All three of them are extremely different people, but I
Speaker 1: really learned, I think, both how to manage people and
Speaker 1: how to get to the core of a problem. I think,
Speaker 1: how to work out what was important and what was
Speaker 1: not important, and not to give different practice of equal
Speaker 1: weight when you're making decisions. And I learned that differently
Speaker 1: from from each of those um three people who are
Speaker 1: really sort of my core mentors. Very interesting, tell us
Speaker 1: about some of your favorite books. What are you reading
Speaker 1: right now and what are some of your all time faves. Well, so,
Speaker 1: I'm I am very interested in architecture, and so I
Speaker 1: tend to read relatively quirky and eclectic books. I'm currently
Speaker 1: reading something about Nordic Modernism, which I suspect will not
Speaker 1: be that popular with your audience. It's a niche area.
Speaker 1: UM but um So, I I think architectural theory is
Speaker 1: something which I'm very interested in. In another life, I
Speaker 1: might have been an architect. Um So that's sort of
Speaker 1: one area of interest for me. Um. I'm actually a
Speaker 1: keen piano player as well. So I know you asked
Speaker 1: about books, but I play a lot of music, and
Speaker 1: at the moment I'm um playing um, some early twentieth
Speaker 1: century music by Debussy, which drives my family nuts because
Speaker 1: it's very hard to play, but if you play it well,
Speaker 1: it sounds good. I'm not sure I've mastered how to
Speaker 1: play it well yet, So that's another sort of core
Speaker 1: part of my life is try and play the piano
Speaker 1: for at least an hour every day and that sort
Speaker 1: of straightens out my mind at the end of the day. Um.
Speaker 1: And in fiction, I amid I'm really a sort of
Speaker 1: enthusiast for mid twentieth century writers, So Treuman, Capote or
Speaker 1: Gray and Green or sort of that. That group of
Speaker 1: writers are all my favorites. Very interesting. You know, there's
Speaker 1: some really fascinating I know you're not a big video guy,
Speaker 1: but on YouTube there are some really fascinating shows about music,
Speaker 1: like Song Exploder or Polyphonic or there's just a run
Speaker 1: of things that take apart various genres of music from
Speaker 1: a musicologists or historians perspective. There if you're if you're
Speaker 1: a classical music fan and play the piano, you may
Speaker 1: find some of that stuff interesting because the parallels back
Speaker 1: and forth between classical music and pop music. The pop
Speaker 1: audience is not familiar with it, but the classical fans
Speaker 1: clearly are. You you might find some of that stuff
Speaker 1: in interesting. Um. And actually there are very strong parallels
Speaker 1: between music and architecture as well. Architecture is all about rhythms.
Speaker 1: People often don't see it, but they're there. Well you,
Speaker 1: I'm gonna assume that someone like you read godal escher
Speaker 1: Bach twenty years ago. Um, am, I uh so they're
Speaker 1: the same parallels between pattern and repetition and and how
Speaker 1: they things morph over time. It's it's it's really and
Speaker 1: and I never thought about architecture, um the way music
Speaker 1: and math show up and and art show up. But
Speaker 1: I guess in a lot of ways, especially with UM,
Speaker 1: with with larger buildings, clearly some of those fractal progressions
Speaker 1: are are there. I'm we're really off on a on
Speaker 1: a digression. Let me let me go to my next question. UM,
Speaker 1: what sort of advice would you give to a recent
Speaker 1: college grad who is interested in a career in quantitative
Speaker 1: strategies or risk management and find it so My advice
Speaker 1: would be not to get too narrow too quickly, and
Speaker 1: to try and build as broad a range of experience
Speaker 1: as you can. In my case, I did quite a
Speaker 1: few things in the early years of my career and
Speaker 1: they really turned out to be differentiated for me later on.
Speaker 1: So I worked a bill in corporate finance. I worked
Speaker 1: out it wasn't for me, but I learned to heck
Speaker 1: of a lot in my a couple of years of
Speaker 1: doing corporate finance, I worked and fixed income and equities
Speaker 1: and credit. I work on the cell side as well
Speaker 1: as the buy side, and that was incredibly valuable to me.
Speaker 1: It gave me just different approaches to problems when I
Speaker 1: came across them. So I think that's the first piece
Speaker 1: of advice. The second piece of advice, I think is
Speaker 1: that like most quants, I thought I was good at
Speaker 1: math and um and you know it probably wasn't bad
Speaker 1: at it. But it turns out that actually there are
Speaker 1: a lot of people that are good at math, and I,
Speaker 1: from my perspective, learned that I had some skills which
Speaker 1: maybe differentiated me a little bit from the core skill
Speaker 1: of all these other people that were good at math
Speaker 1: and those in my case where I was good at
Speaker 1: making decisions I think I remained good at making decisions.
Speaker 1: I can see things, I don't have to spend a
Speaker 1: lot of time thinking about it. I can decide and
Speaker 1: move on and have too much regret. And I learned
Speaker 1: that I was, I think, better than many quants a
Speaker 1: communicating a quantitative things in straightforward English, which most quants
Speaker 1: are not pretty good at. And so I really tried
Speaker 1: to work out what are my other strengths that my
Speaker 1: differentiating strengths, and and and and tried to use those.
Speaker 1: And I would recommend any quant workout. If you're just
Speaker 1: going to go straight for a math competition, you know
Speaker 1: there's some people who are pretty damn good at math
Speaker 1: out there, so you're going to have to think about
Speaker 1: your extra strengths to things which separate you from the crowd.
Speaker 1: That makes a lot of sense in our final question,
Speaker 1: what do you know about the world of investing today?
Speaker 1: You wish you knew thirty or so years ago when
Speaker 1: you were first getting started. So I think thirty or
Speaker 1: so years ago. Um. I think the thing that I
Speaker 1: most didn't realize was how much more tech and quant
Speaker 1: focused the world was going to become. And I slightly
Speaker 1: under emphasized my quantum tech skills at that time. So
Speaker 1: that's the first thing I think, and I shouldn't have done.
Speaker 1: You know, today it all feels very obvious that tech
Speaker 1: has dominated our lives so much, and behind most tech
Speaker 1: through a lot of algorithms, but thirty years ago it
Speaker 1: wasn't so obvious. So that's the first thing. I think.
Speaker 1: The second thing that I wish i'd known thirty years
Speaker 1: ago was that it's very easy to gravitate towards the
Speaker 1: glamorous businesses. So when I was sitting on trading floors
Speaker 1: that called them sacks, then the glamorous bit was was
Speaker 1: trading what we call the exotic derivatives or the more
Speaker 1: complicated derivatives contracts. But actually that business almost doesn't exist anymore.
Speaker 1: It does exist, but it's really much smaller than it
Speaker 1: was twenty years ago. And that's what everyone sort have
Speaker 1: wanted to do. And then there were other things which
Speaker 1: were apparently less interesting, like understanding equity in disease um
Speaker 1: or um or building quant equity models or something like that,
Speaker 1: and those turned out to be much bigger things. So
Speaker 1: I think the second thing which I've realized, which I
Speaker 1: wish i'd realized, and understood twenty years ago is the
Speaker 1: glamorous stuff is not always the stuff to go for.
Speaker 1: Often it's the stuff that actually people sort of think
Speaker 1: maybe it's a bit boring or something like that. Um,
Speaker 1: but there are very often the big opportunities lie in
Speaker 1: the stuff that people think is a bit boring. M
Speaker 1: really quite quite interesting. Thank you, Sandy for being so
Speaker 1: generous with your time. We have been speaking with Sandy
Speaker 1: rat Trey. He is the chief investment officer of the
Speaker 1: hundred billion dollar Man Group, as well as the co
Speaker 1: inventor of the VIX index and the author of the
Speaker 1: book Strategic Risk Management. If you enjoyed this conversation, well,
Speaker 1: be sure and check out any of our previous three
Speaker 1: d and fifty or so UH interviews that we've done
Speaker 1: over the past seven years. You can find those at iTunes, Spotify,
Speaker 1: a cast, wherever final podcasts are sold. We love your comments,
Speaker 1: feedback and suggestions right to us at m IB podcast
Speaker 1: at Bloomberg dot net. You can sign up for my
Speaker 1: daily reading list at Rit Halts dot com. Check out
Speaker 1: my weekly column on Bloomberg dot com slash Opinion. Follow
Speaker 1: me on Twitter at rit Halts. I would be remiss
Speaker 1: if I did not thank the crack staff that helps
Speaker 1: put these conversations together each week. Charlie Vollmer is my
Speaker 1: audio engineer. Paris Walt is my producer. A tick to
Speaker 1: Val Brunn is our project manager. Michael Batnick is our
Speaker 1: head of research. I'm Barry Ridholtz. You've been listening to
Speaker 1: Masters in Business on Bloomberg Radio.