Sandy Rattray on Strategic Risk Management

about it. The track record that the Man Group has amassed, as well as how they've pushed forward UM portfolio construction theory is really incredibly, incredibly influential. Not only was he the co inventor of the VIX index, but he's written extensively about risk management and how to design portfolios and you know what to expect when you're expecting a black swan, which you by definition can't know what to expect, and how to build strategies that give you some degree of

protection against this. If you're remotely interested in hedge funds, asset management, quantitative strategies, the VIX and managing risk well strap yourself in because this one, uh is a good one. With no further ado, my conversation with Sandy Retrey of the Man Group. This is Masters in Business with very Renaults on Bluebird Radio. My extra special guest this week is Sandy Retrey. He is the chief Investment Officer of the Man Group. He is also on the Executive Committee

and the Responsible Investment Committee. He is per Apps most famously the co inventor of the VIX index. He's also run a number of different systematic strategies for Man and other organizations. The Man Group's assets under management are over a hundred and twenty billion dollars and Sandy is the co author of the book Strategic Risk Management, Designing Portfolios and Managing Risk. Sandy Retree, Welcome to Bloomberg. Great, Thank you very much, Perry. It's good to be with you.

So you have a really interesting background. You're deeply steeped in mathematics. How did you find your way into the investment business? So it's a it's a long story. I as a teenager, I thought I would become a theoretical physicistem that was my ambition. I went to Cambridge University to study physics, and I really discovered a number of things at that time. Number one, UM, I was I thought quite good at physics. Turned out that I surrounded myself for the whole bund of other people are also

pretty good, and really standing out was hard Um. And Second, I think at the time the amount of innovation that was taking place in physics seemed to have sort of dropped off a little bit from the nineteen seventies and and it was a sort of slow period in the late nineteen eighties, and so that made me think, well, maybe I could use these math skills for something else. And that really got me into thinking about finance. So I some people duck out of physics having done PhD

s or or taught at universities. I ducked out a little bit earlier on UM, and that got me into them, thinking well I should use these skills. I end up joining Golden Sacks, and and there again I learned something which was I thought the exciting bit would be the corporate finance areas, so advising on major corporate transactions, and what I realized was that that didn't really use the

quantz skills that I had. So I did that for a couple of years, and then I moved over to first sixt income research, then equity derivators research, and then finally transitioned out of that into UM into more proprietary trading, and then into fund management. So let's build off of that. You did a lot of work on derivatives and fundamental

strategy of government. You then go to MAN where you're running things like systematic strategies and a h L. What was man h L focused on when you were managing that. So when I arrived at h L, which was the end of two thousand and twelve, was really a futures trend following business. It was c t A and c t as have been having a pretty difficult time really since the end of the financial crisis. They had a tremendous two thousand eight and then that really done nothing

in the following years. So nine eleven, twelve, we're all years which essentially added up to nothing. So the fantastic crisis here. Very few as the marriagers could say that two thousand eight was a great year, but HL could definitely say that. But then you had a long dry period and people were starting to say momentum doesn't work anymore.

It's broken. And I think what I really did when I arrived in NHL it was to say, well, I've been involved in all sorts of different quant strategies in my golden years, and I can use a much broader perspective than maybe the futures trend followers had and look to develop a much wider range of systematic stranities. So when I arrived, we had a handful of models UM. Today we've got three or four models running in HL,

so we really expanded number of models we're using. UM, we expanded the number of markets that were trading, so we used to trade futures, UM and effects markets and my group it started trading OTC markets, but it was still quite small UM, and we really picked up and start trading much wider variety of markets. So now we trade around seven markets around the world using system addic models.

And then finally we came up with different many different types of funds, so very short term funds, funds which are more fundamentally driven, funds which are maybe trying to provide more protection characteristics, or funds trying to maximize the sharp ratio. So so we really tried to grow quant

into many different areas. And I suppose my advantage coming into a place like h L is that most people in those HL and in the competitors had really grown up and had the whole careers in the in the c t A or the futures trend following business, and I had had none of my career and futures try and following, and but I had all these other influences that I could bring in, And so that really was how I worked with the team now to significantly expand

UM the business which was having a very difficult time and arrived and UM declined to lesson ten billion dollars of assets in the h L unit and today were many times launcher than now. So I want to focus on something you mentioned in passing, but it's so relevant to what we've been seeing in the markets recently. You said that momentum as a factor seemed to have been fading. We've seen other factor based investing like value, go long

long periods of underperforming. There's so many different questions I can ask you about this. Let's just start with is that the nature of any factor or any specific trading edge that they only last so long before eventually everybody wise is up to them and the alpha gets arbitraged away. And do you see these sort of edges disappearing more quickly these days then they used to in a perhaps kindler,

gentler era twenty or thirty years ago. So, um, I agree actually with everything you said, Barry, except for the last bit about kind of legenter era. From my memory twenty or thirty years ago, it was probably less kind and less gentle than it is today. Um, But let's let's start with factors. So I think one of the things which has been interesting over my career is nobody really talked about factors apart from a very small sort

of quant group twenty thirty years ago. Today they're in, you know that they're sort of part of all our portfolio marriagers at Man Group, whether they're quants or discretion marriages. Everybody talks factors and so that's been a big change, and they've become sort of part of, you know, just sort of general dialogue when people are talking about markets. The thing I'd say is that I don't think that, um, the core factors which have been around in a long

time are going to disappear. And for me, you know, as a European, I was working in New York in the late nine nineties and into the two thousand's, and I remember looking at front page of the Wall Street Journal one day and on it it said value investing is for old people and um and I you know, as the European obviously, you know Europeans want to live in old houses. Clearly in the US people mostly want

to live in new houses. So it's a big sort of didn't quite understand that the the the extent of the statement there, and it was a ridiculous thing to say. I was a young person being included in the front page of the Wall Street Journal, right in the tail end of the tech bubble and just before a huge

outperformance of value stocks. So these factors and we should talk about, you know, which are the which are the actors that are likely to persist in which are not um But factors like value for example, I think generally don't have particularly high sharp ratios, so they're their returns

adjusted for risk are not particularly high. But the idea that buying cheap stocks will never work again, I've never thought that was a sensible thing to say or think, but every now and then, you know, that's what this fellow on the front page of the journal said um twenty odd years ago. So I think factors, at least a core set of factors are very likely to persist, but it won't give particularly amazing risk adjusted returns, But I think they are likely to give you positive risk

adjusted returns over you know, relatively long cycles. I would say, though, that one of the things I've seen in the last ten years or so is as people thought they understood factors, and people started to find hundreds of these things. And I don't think there are hundreds of real factors. Think there's a small handful um of factors, and this explosion really is an overfitting exercise. It's people finding patterns in the data that don't really exist, and I think that's

something that people should be very wary of. You know, I've seen data providers and firms sell their libraries of factors with with you know, literally hundreds of these things, and I don't think that's going to be a source of returns. The final thing I should say is that you mentioned momentum, and momentum is quite an interesting factor

because there are two very different definitions of momentum. One is really used by equities people and they will go along the positive momentum price momentum stocks and short the negative price momentum stocks. They sometimes do it with earnings as well, but basically go along the stocks which have been out performing and short the stocks which have been

under performing. But then is a very different definition of momentum, and that's what the c t as use, And they don't they don't look at the price move against anything. They just do it in absolute terms. And they tend to do that in macro markets. So they'll create the SMP or the or the the dacks or the euro or gold or something like that. And and that's a much different definition because it's not it's not going long one set of markets and or one set of stocks

and short another set of stocks. It could be long everything, or it could be short everything, and it gives you a very different type of return profile. That second type has a very nice feature, which is that it barely reliably will do well in bad periods in markets. So I talked about how, for example, HL two thousand eight was an excellent year. Well, not many strategies that could

say two thousand eight was an excellent year. And that's because that second definition of momentum what I would call time series momentum the or univariate momentum. That that definition momentum has very good protection like characteristics. It will pick up on a trend, especially in negative trend in markets, and jump on that trend. So it sounds like the price momentum seems to be relative, while the time series has a persistence that gives it a very different characteristic.

Or am I oversimplifying that? No? No, I think that's exactly right. So I think you know, the most investing strategies, including price momentum and equities, but most investing strategies have what people like me would call a left tail, so it make you money most of the time, and then every now and then they serve your up an unpleasant surprise.

Time series momentum does the opposite. Time series amentum most of the time gives you pretty boring returns, but every now and then I'll give you a very positive surprise. And and that's really rare in investing strategies. And from my perspective, that's you know, that's a very attractive characteristic when you're building portfolios, to have a bit of something which does the opposite of most other investing strategies. Let's talk a little bit about the VIX index, which you

were the co inventor of tell us. How does one go about inventing the VIX index? Well, you know very In short, you get lucky. So the the story behind it is, as we talked about in our earlier segment.

I was working in New York basically and see clients and every time you went to somebody's office to be a TV screen in the in the fire in the entrance area, and they would have prices of all sorts of things coming across that screen, you know, the sort of the price of crude ale, the treasury bond yield, the sple hundred level, that sort of thing, and it would have the VIX on it. And so there was a VIX, but that was the VIX was the only thing that seemed to come across the greens that you

couldn't trade. And so the story really came because a colleague of mine at Golden Sacks came to me one evening and said, you know, I've had this call um from a client who wants to do a trade on the VIX. Could we do that? And my colleague ran options trading and I ran the drug to his research a bit of Goldman and we got our heads together and I went and found the formula for the VIX as stood as as pre existed, and we worked at it just wasn't possible for us to do a trade

on that. It wasn't designed in a way that you could hedge a trade on the VIX. So we came up with a kind of crazy idea which was, Okay, well there's this VIX thing which was owned by the cbo E, but you couldn't trade it. And there was a good reason why you couldn't trade it, because you couldn't hedge it. So why don't we change it? And

so we came up with a completely different formula. It turned out to give fairly similar levels to the old VIX, but but it was a completely different formula, didn't use black sholes at all and um and we thought, well, hey, this this formula actually you could hedge UM futures contract on or something like that. And so what we then did is I was quite friendly with a fellow called Bill Speth, but the CBOE, who has had of research there.

And I called Bill and said, you know, you have this VIX but you make no money out of it, because you just published this thing and doesn't give you any income. And we've got an idea how you could change it to make it something that could be traded, and maybe you could launch futures contracts on that, and so that might be interesting to you. And then I launched into a long description of the math behind that formula, and UM Bill very wisely said, you know, maybe you

could send me a letter with that formula. So so I sent him a letter and which was actually, with the benefit of findset quite helpful because now I have a quite clear record of when we communicated this formula in two thousand three to the cbo E UM and UM and within six months we had a new VIX being calculated using this new formula, and another six months later the cbo E, which until that point was only

an options exchange, had launched futures contracts on it. And so there was a series of sort of you know, lucky moments in there. A client came and asked a question. I happened to know Bill at the cbo E, so I knew somebody to call. They happened to be interested in in launching futures contracts and our timing was was spot on. So there was a whole series of bits

of luck along the way. And what really happened after that is another good learning experience for me, which is at a boss at the time who said, look, you should talk to all the salespeople and find out if they're gonna bring in business on this new Vix thing that you've been working on. So I spoke to the salespeople and we did a little survey, and you know, I was going to retire on the proceeds of this survey. It was just amazing how much business we were going

to do. Day one comes along, I wait for the phone to ring, and it doesn't ring. They two comes along, the phone still doesn't ring, and by day three you sort of get it. You know, it's nothing's happening. And amazingly, the first significant trades we got done were actually with investors outside of the us UM and they were the people that still have got the early stages of this

new market in in Vix going. The other thing that happened was my boss said, you should call, you know, some of the other banks and and see if they're going to support this thing. So I called nine other banks, and nine out of nine said they had no interest in supporting it. So it wasn't a particularly auspicious beginning, you know that, And it took a little bit of persistence. Now today it's this huge market and it trades enormous volumes, but it's a good sort of lesson in terms of

how difficult it is to get something new going. So so let's talk a little bit about what the VIX index does and some of the misunderstandings around it. When when you talk to people, especially traders on the equity side, they look at it as the fear index. It measures volatility, but but to be more precise, it's really measuring volatility expectations. Right, Yes, I think the the word fear gauge or fear index

is actually pretty accurate. But you're also absolutely correct that what the VIX actually is is the market's expectation of alterality over the next thirty calendar days. And so it is a it's a market price, and inevitably because the volatality cannot mathematically go below zero, there's no such thing as vaultality below zero. But it's unlimited on the upside, you know, there's no limit to how high vaultality can be.

Then if you go to the market and say, hey, give me a price for the next thirty days of alterality, it's generally going to overestimate because it's going to have to protect itself a bit against the possibility of huge swings upwards and the fact that there's a floor. It can't go below zero. So your description is absolutely correct. It's the market's expectation of realized volatility over the next thirty calendar days, but it has some features built into it.

It's always going to overestimate because you're effectively selling insurance if you're selling the vics and and people don't sell insurance cheap for the most part. That makes a whole lot of sense. I know that a lot of traders seem to conflate volatility with risk. How do you define the different is between the two. It's a very good question, and I think you know something I've thought about a lot over the years, um and there's many different versions

of this. I think so most models, most risk models do estimate volatility. They give you a var number, which is basically a manipulated volatility number, or a risk in stand deviations, or an expectation of loss or something like that, and they're useful numbers, but in the end, I'm pretty sure that both you and certainly I would never clue what the volatility of our personal portfolios was last year

in a very volatile year. But we have a pretty good idea of what the worst point is, you know, when when we have the most losses of most pain in our portfolios, and that's got nothing to do with voltility. That's a draw down. So in the end, actually the risk that a lot of us really experience and worry of out his drawdowns. It's not volatility, which is mathematical formula which describes the shape of a of a distribution, and that I think is something which is difficult because

estimating drawdowns is extremely hard. Estimating volatility is actually fairly straightforward. But the two don't really connect. And so why do people estimate volatility? Because it's useful. It gives you how wide the distribution will be. You can estimate it quite accurately, you can forecast it quite well as well. You can forecast volatality much better than you can forecast returns. So

all of that is useful. But what's not useful is that we don't really worry in the end about what I've altilt he was last year or what it will be next year. What we really worry about is how much we lost or how much we might lose. That sort of that that pain threshold, and volatility doesn't really connect with that. So unfortunately, really useful statistic drawdowns very

hard to estimate, and people don't really estimate it. The other statistic volatility is useful, but it's in my view, not the most useful estimate of of of risk because we actually experience it. That's that's really kind of intriguing. So so not only can you go long the vix um, but some people can go short the VIX not not exactly um a hedge if you have a long portfolio, but I guess if you're short, maybe that that useful.

What do you think of of how people have been using the VIX, either as a risk management tool or as a way to UM get some non correlated exposure to their to their equity holdings. Well, I think there's a few things in there. Firstly, the the VIX and the price of futures on the VIX will nearly always disagree in generally, the futures will be higher than the

current level. So you know, as we're speaking now, the VIX is a round sixteen better futures contract three months out is trading at almost twenty one, so five points higher, and that is a normal state of affairs. And as we talked about earlier on voltialty, because voltality can't go below zero than people, and it can go to an unlimited upside level than people, generally the market will overestimate volatilty to give it a little bit of an insurance

premium in there. Now, in terms of trading and investing with the VIX, I think this thing is quite important to know. So if you buy an ETS on the VIX, for example, then it is going to have to hedge itself with these futures contracts, and these futures contracts will trade a lot higher. I said twenty one for September, verus is sixteen now, so that's almost a thirty percent higher. And by September, one of two things has to happen. Either the VIX has to go up to twenty one,

or the futures contract will go down to sixteen. More lightly, the futures contract will go down to sixty. And so I think people often don't understand this when they're buying a vix et f that they're not buying the level of the vix they see on the on the screen on their television screens or on their Bloomberg terminals or wherever they see it. They're they're buying effectively a future on the vix which generally is trading a much higher level,

and so they will have expected losses built in. That is something which then some people on the other side have said, well, this is very exciting. You know I can I can sell the vix at twenty one in September, and I expect it will go to sixteen. I can make thirty percent in three months. And they're absolutely right, you can and most likely will make thirty percent in three months. But and the butt is a big thing.

If if something bad happens between now in September, then you know, you can make very very significant losses because the vix can just, you know, very very quickly go up to very high levels. So what people often do is they say that buying the vix that gives me some protection against crises. If there's a crisis, then it will lightly go up, and they're right, but they do have to understand that it's not the VIX going from

the current sixteen to say eighteen. It's got to go above that twenty one that's priced in in September before you make any money. Um. But it can be a protection strategy and insurance strategy. And then you've got people on the other side who, let's say, look, I don't expect something bad to happen, and I can earn this very large insurance premium UM if I'm prepared to go short the VIX and you of course can do that through futures contracts, but you can also do it through ets.

I think my real observation on this those you know, I've tried to give as clear an explanation as I can of how this is working, but it's quite subtle. This is not a simple thing. And I think a lot of people that trade the VIX cts don't really understand what's going on underneath the surface of the TS contract. And there's a lot going on underneath the surface. So so let's talk a little bit about um, people trading

products that they didn't really understand. And who better to ask you the co inventor of the VIX back in ten we saw the notes UH that were based on the VIX and Credit Swiss was one of the larger underwriters of these. UM just blow up and sent the VIC spiking. I kind of remember we kissed fifty I could I could be wrong about that, UM, but that whole series of products, those short term E t N s UM x I V was one and s v x Y was another. UM. They just blew up spectacularly.

As you're watching this from from your seat, what are you thinking about? Gee? Look what look? I lent the keys to the car to the kids and they seem to have wrecked it. Oh. I think I think your description that Barry is pretty fair. When we did the works, I'm Aaron. I did the work back in two thousand three two four. The boss that asked me to do the various other things that we talked about said, you know, you should look at creating an ETF on this thing,

and so we did. Then we got together with one of the very big E t F providers and they said, well, look, maybe you could do some modeling of how this thing will behave and we did that and we concluded this is just not a good product. You know, people it's got some nasty characteristics, and so we decided, along with that large firm, that we should not sell E T s on the vix. Now, other people took a different view, and so your analogy of kind of the kids getting

the keys to the car more or less accurate. Actually, I think the the the so I don't like the E t F products mostly because they're very complicated. They look simple on the outside, but underneath them they're very complicated, and I don't think people always understand all that complexity. So the events that happened in fabruy E two thousand eighteen were these short vix et s and the short vix ets trying to earn this insurance premium. So lots of people, lots of all streets were happy owners of

these short vix ets. The problem with them is that what the t F does then, is it it issues units to people like you or I UM and then has to sell futures contracts against it. If the price of the vix starts going up, it needs to start buying those contracts back, and if it goes up a lot, it needs to buy a heck of a lot of them.

And so you have some volatility very late in the closing, towards the end of the day UM, and I think it was February five, if I remember rightly, two thousand eighteen and UM, and they needed to buy a heck of a lot of futures contracts in a very short period of time. What did that do It push the thing up even more, and so it kind of created its own volatility and its own noise UM in that period. So I think that it's a pretty good example of

an unintended consequence from a financial product. And again, you know, it's quite a complicated concept. Along vix CTF is complicated as short vic CTF is very complicated, and I think from that perspective and people probably had some surprising results that you know, a lot of people lost a lot of money, some of the issues of these short vix cts made a lot of money UM at the same time, and it felt like a pretty bad state of affairs

to me. Let's talk a little bit about your book, UM, which was written with Harvey Campbell, who was a prior guest and and just a delightful individual. What compelled you guys to write this book? This is a pretty UM in the Weeds Inside Baseball sort of sort of book. Yeah you're you're right, it's absolutely in the Weeds type

of book. What got us going on this was really a sense that for many portfolio managers, risk management is something which comes afterwards they build their portfolio, and then the risk team do something later on and tell them whether it's okay or not. And we thought that that's actually a very bad way to run portfolios. In a much better way is to have the alpha side of building portfolios in the risk side to be equal partners.

And that's something that we've really tried to build as a culture of a man group that risk is part of the investment team. It's, as they sometimes put it, if risk is the police for other people that come kind of knocking on your door telling you've done something wrong. That's that's not really a good way of running portfolios. What do you really want is as you're building the portfolios, the risk and the alphabets to to come as as

equal partners. And the reason for that is, firstly, huge amounts of damage tend to be done when there is bad risk management in stress periods that people aren't prepared for those stress periods, they make bad decisions, and those stress periods lose a lot of money, often crystallize losses, that sort of thing. So so firstly, if you don't have a proper risk approach to building portfolios when you enter chop your periods in markets, then you're likely to

make bad decisions. That The second, which I mentioned earlier on is kind of surprisingly. It's actually much easier to forecast risk than it is to forecast returns, and you

can use that to give yourself more stable portfolios. And so we were really trying to say that you can blend the alpha side of portfolio management with the risk side as equal partners, and you'll build better and more stable portfolios which will actually do better over the long run because you won't end up making bad decisions during the during the stress periods. So let's talk about that

last quote, it's easier to forecast risk then return. I'm going to play devil's advocate and take the other side of that argument. Hey, we know over long periods of time what historical asset class returns are, and so we can reasonably forecast eight percent over twenty years for equities UM, but we can forecast the sort of UM black swans that show up every now and then that are just

completely unexpected and are amongst the quote unknown unknowns. Tell me what's wrong with that perspective that looks at extrapolating long term returns versus hey, we have no idea what the next random events is going to be. Yeah, I think what's wrong with that is that you've tried to compare forecasting very long run returns and then said that I need to forecast short term risk. And so let

me sort of decompose that a little bit more. Many times, people when they when they forecast returns, and we're all guilty of this, we end up being pretty influence by what happened in the last month or two, and so we say, you know, people, many people, for example, are pretty positive about equity market returns globally and maybe especially

in the US. And one of the reasons that really is that we've had good returns, you know, really since March shows since April last year, and people are extrapolating, they're just stretching forwards. But if you look at the data, and if you look at the data over long periods of time, you find the correlation between past returns and next month's returns is about zero in almost all markets around the world, equity markets, bond markets, commodity markets just

about zero. There has no last month's returns have close to zero predictive ability of telling you what next month's returns are going to be. If you now do that on risk and you calculate the fertility of markets last month or the month before or the month before that, lets hee has very high predictive ability. So people like

me would call this the serial correlation. So the serial correlation the returns in other words, from last month or two months ago or three months ago to this month's returns is close to zero, you might as well call it zero. So close to zero. If you do the same thing in volatility, and you can do this across equity markets in the US, but also across other parts of the world, bond markets, commodity markets, currency markets, that

zerial correlation is around forty percent. So that's telling you that last month's vulatility is actually telling you quite a lot about this month's faultiality. And so whilst you might be able to make a statement about you know, twenty year expected returns um I suspect both you and I will be, you know, at least twenty years older, and that your point and whether anybody will really hold us to it or whether it's the useful observation will be

tricky to to to really evaluate. But for most people they need to have nearer term forecasts in order to be able to make their decisions. And I think they make a mistake that they think they can forecast returns often by extrapolating from past months or turns, And the evidence is that you shouldn't extractly. There's no extrapolation to be done. Whilst a risk you can that that is very parallel to what we were talking about earlier with

price versus time series. So equities follow a random walk, but volatility and risk tends to be persistent and enhance more likely to have some time series correlation. I think

that's what I'm hearing, Am I am I saying that right? Yeah, absolutely, absolutely, the the there's a very small and what the c t a s try and do, but the futures trend followers is that there's there's a very small effect to being able to pick up some persistence in returns, but you have to do it across hundreds of markets and you have to do it consistently over time, and you'll just managed to eke out a little bit of alpha

about doing that, but altility is much easier. So what should managers do proactively to prepare for are not the known risk but the unknown risks? And and really just over the past twenty years, we had nine eleven, we had the Great Financial Crisis, we had the VICS meltdown, and more recently we had the COVID pandemic. How how can a fund manager build protection against these black swans into their process? Well, so I think the first thing that I portfolio managed to do is realize that you

cannot forecast these events. And you know, I speak, at least I used to speak at conferences a lot when we've still had conferences, and you know, people would ask me, well, what's the black swan event that's going to happen this year? And I also that was the most ridiculous question because you know, clearly all of these events are unfoecastable, and generally the ones that you forecast will happen don't end

up being the thing that takes place. So I think the first thing is to show a lot of human tear about our ability to forecast what what the bad events will be. The only thing I can really feel confident about is that there will be more bad events in the future. You know, it seems that they just

keep coming, but they have different shapes and forms. And maybe as a side anecdote on that, we have an excellent risk manager at Man Group and at the end of two thousand nineteen and a sort of planning exercise who was giving all the risk that could affect markets. And you had about twenty of these things, and one of them was epidemic. And I looked at this at the end of two thousand nineteen and I said, epidemic. Well, I mean, like, you know, I don't know much about epidemics,

but I can't say it's impossible. But it doesn't seem very lightly. So we did exactly nothing about the risk of epidemic. And if he got the word almost right, pandemic instead of epidemic. But you know, even if you have it on your list of things, which my risk manager did, um, it's very hard to act on it. So I think humility in terms of ability to forecast these things really important. If you think you can forecast, you'll probably make them. State by protecting yourselves against the

wrong thing. Once you've got over that and said likely it probably can't forecast the next bad thing, then I think what becomes much more important is, Okay, now you need a strategy that is going to be relatively insensitive to the nature of the bad thing. In other words, whether it's a tech bubble collapse or um credit crisis, or you know, something entirely different to war, or a pandemic, or it could be any of these things. Obviously you need a strategy which is going to be robust to

any of those things coming along. And my own suggestion on this would be that it's it's too expensive to buy put options. Buying put options on the sp you can do every now and then, but you can't do it all the time. It's just it just becomes too costly. And so you're gonna have to have a strategy which relies a little bit more on either assets in your portfolio which you think are likely to do well in a stress period. That could be gold in my view,

it's not terribly reliable. It could be gold. It could be U strategury bonds or other government bonds around the world. Of course, if the problem emerges from the bond market, it's not really going to help you. Um. And you know, people, I think forget that they have definitely been problems from the bond markets. You just need to look back a little bit to the early es or before then to see that actually there were plenty of problems that came

from the bond market. And or you could have some sort of trading strategy, and certainly for me this time series momentum, which we touched on in our first segment, this idea that you can build a trading strategy like the c t as have done, which relies on just a little bit of persistence in returns and looks for them everywhere. That can be a very good way of

building a defensive strategy. So, in other words, if we describe a crisis, and I can't tell you whether it comes from a war, credit crisis, an epidemic, something else, but typically in a crisis like that, you will tend to have equities going down, you will tend to have bonds going up, you'll tend to have gold going up. Um. It's a bit harder to tell what might happen to energy prices, but those moves tend to persist for a bit.

They tend to know equities fall and then they keep falling, and bonds might go up and they keep rising, and gold the same thing. And you can build a strategy which encapsulates and tries to capture that effect. Then you can build something which is robust and is not depending on your ability to put your finger on what the next bad event might be. So in the book, you you get into the nitty gritty, you you go over details of a new risk management approach to portfolio design.

I want to ask, how did those strategies do in back tests looking at OH eight or nine, and how do they do in the real war world in March. Well, so for us, actually, I don't think O eight O nine really was back test because we were actually trading most of these strategies at that time. But so I

think we actually have pretty good life experience. And the first thing I should say, March of was an extraordinary crisis, and all all crises are extraordinary, but one of the things which was most extraordinary about March was that it

markets fell very quickly. What we've seen that before, but then they reverted remarkably quickly, and really the most similar crisis in terms of market action that you can put your finger on since the Second World War was the October seven crisis, so that that very rapid fall you had followed by an almost equally rapid recovery. So so that might say, well, you know, if you fitted, if you've sort of tested your crisis protection on all these slower crises, then maybe you wouldn't do too well in

this faster crisis. And it's actually not what we found. So we found that, um, we had you know, quite good strength of our strategies during um the March April period. So for example, futures trend firing, something we talked about quite a lot, um did you know, really rather well

in March and April of last year. But we also talk about for example, rebouncing and and and trying to stop rebouncing, which can can have the nasty effect of buying the losers, and then if the losers carry on falling, then you will, damn it, you just bought a whole bunch of losers in time for another month's falls. And we found that if you if you have UH strategies which try and control your rebouncing, but they have to have relatively rapid they have to be quite fast strategies,

and that would be my real point. So most of our protection strategies are quite fast. The signals are quite quick. They use data that goes back typically a few weeks um, and they can move positions around quite rapidly. And that worked pretty well in March and April last year. If you do much slower strategies, you would not have had the protection characteristics right and and and to put some numbers on on the speed of March, we smp um.

That was the fastest drop in history, and I believe it was just a day under a month, maybe a few days under a month. And then the recovery from the end of March beginning of April was back to break even by August. That that's a pretty astounding turnaround, arguably faster than the recovery from seven, which was itself pretty quick, wasn't it Absolutely so, it was just totally extraordinary.

And from that perspective, you know, the past didn't give you a particularly good guide as to how how that crisis would would unfold. And then maybe that sort of retrates my point a little bit that you can't you can't build protection strategies which are really trying to put your finger on exactly what's going to happen. You have to you have to be aware that your forecasting ability is poor, UM, and you've really got to have a strategic response. And that's why we call the book strategic

risk Management. It's really a set of strategies that the plan. And you can't make the plan up on the fly. You know what. You really the worst thing you could have been doing last year is making up your protection strategy during March. It was too late by that point. You have to make up your your protection strategy in the months and years before then, and then you had to be implementing it during March. So there was a quote of yours really liked, and I want to ask

you about this quote. We are in a riskier environment than we have been in the past twenty years for the foreseeable future. Unquote the past twenty years. Really there were a lot of risky events that took place, from nine eleven to the Great Financial Crisis to the pandemic. What makes this a riskier environment and why do you see this as um being a persistent risk for the

for the next foreseeable future. So the reason that I think we're in this highly or much riskier environment that we've been in is because markets are much less diversified than at any point in my career. And so you know, I started trading markets when I was at high school in the late nineties, and at that time people got very worried that the Japanese market was round of the MSCI world. Well, today the US market is two thirds

of the MSCI world. So it's and that's the highest way to to ever be in such the highest way that any one country has ever been in the global

Equity Index. And then if you now dig in within the US market, and this is a little tougher to do, but if you dig into the proportion of the U s ecty market made up by tech, and the reason it's difficult is they changed the classification system a couple of years ago, then you'll see the tech is a bigger portion of the US equity market than it has ever been, including in the late in the tech bubble.

So you have an incredibly concentrated equity market both globally into the US and also by sector within the US. And for me, that means that you know, this is not sort of looking at the vix today, tomorrow, yesterday, whatever. More strategically, the market feels much more likely to be able to produce unpleasant outcomes because the only freelance you haven't financed the diversification you have the least diversification I've ever seen. Huh, that's kind of interesting. So we have concentrated,

non diversified portfolios. And one of the things we've seen is domestically, the US seems to be a higher poor proportion of global equity markets. And then within the US, the text actor continues to increase its waiting when the sp What does that mean for the future of of risk and managing it? Well, I look, I think it means firstly that you know, you need to be just aware of this, that that the market is so heavily concentrated. Um the I think the what can you do about it?

It is probably the real question. And I think that this is a pretty big challenge for people because historically the answer was, well, if if I want to build a balanced portfolio, then I'll hold some equities, and then I'll hold some government bonds, often US treasury bonds um as the sort of as the ballast as the thing which gives a bit of stability to my equity portfolio. But where we are today, I think people are are much less convinced that treasury bonds will be the ballast

that they have been historically. In particular, you know, if we continue to get high inflation numbers, then I don't think anybody is good to argue that high inflation is good for government bonds. It's is clearly bad for government bonds. And so your challenge is that the way you built stable portfolios in the past, there's balancing of equities and bonds, is really much less suited to the current environment than it was to the to the past environment. So what

can you do about it? But I think what most people that I speak with at least to think of doing about it is saying, well, I need to own something other than treasury bonds to balance out my equity risk. And for some people that's private equity, for some people that's hedge funds or alternatives. For some people it's infrastructure

or housing or other forms of real estate. But I think it's reasonably clear in my mind that you need to you need to think about balancing your portfolio, and then you need to think pretty carefully about whether bonds give you the same level of protection or balancing them may then they would have done in the last twenty thirty years. From my own perspective, if I look back at the last twenty years in particular, it's a very dangerous period to look at when you look at equities

and bonds. Over the last twenty years, when equities went down, bonds nearly always went up in price, and and so we've got used to this idea of bonds being the protecting asset. But if you look before then, and you can look back for in hundreds of years worth of data, both in the US and then also the UK, whether bond market started earlier than the US market, you see that for almost all of history, except for the last twenty years. When equities went down, bonds went down at

the same time. And so for me, I think it's a very important question for investors, which is you need to balance the risk in your portfolio. Are bonds the answer to it? In my view, they're probably much less the answer than they were historically, So then you need to look at other asset classes and think more creatively about how you do that. And the fact that I think that equities public equities are as risky as they've ever been from a strategic perspective, means that question is

actually as important as it's ever been to think about. Huh, that's that's really kind of intriguing. Um. So, So, sticking with the theme of risk, what does this concentration mean and this lack of diversific asian mean in terms of, you know, calibrating what we should be expecting. Should we be looking for larger moves in the future, or you know, how do you approach this um philosophically when you're thinking

about what you want to do with your portfolios? Yeah, I think that you know, right now or in a relatively sort of quiet summer period, and so markets have been relatively stable. But but I looking beyond just a short term than this lack of diversification, markets definitely means that we should expect to see bigger moves in both directions. Just to be clear, both upwards and downwards. Um, it does not mean that markets couldn't go up a whole

bunch more from here. It just means that they're lately to be more roletile than we've been used to in the last few years. And so I think it's really having a plan of action and being prepared for how you respond to that, because in the end, for most of us, the big up moves, I mean, maybe we were underinvested in we have a bit of regret about it, but you don't have much pain from the big up moves.

It's the big down moves that that caused all the pain and caused cause the bad decisions to be made. So I think it's having a plan of action. And I would argue that you know, here we are sitting in a relatively comfortable moment in markets currently, and you know, if we're fortunate, then the summer will be enjoyable for all of us and not too not too noisy. That's a great time to be thinking about your plans for how you'd respond if there was a if there was

a negative altality event. Really really quite quite interesting. So let's talk a little bit about some strategies you mentioned, alternatives like private equity and hedge funds. What can one do to hedge against the risk of increased volatility in the future. Well, I think you know, you can do

direct hedges, but they tend to be very expensive. So you could go and buy futures on tracts on the VIX or something like that, but they will turn out to be very costly few over time, as we've talked about now earlier sections, So more likely what you need to do is to think about assets that will just behave differently um to equity markets. And as equity markets have become more concentrated, especially into tech um it's uh to be precise about that. It's tech and communication services

of the two classifications that people use today. So as it's become more concentrated into that into those sectors, then you need, I think to think about things which will be not so affected by a negative price move in those and I think that private equity in the end, it has a lot of equity market exposure into it, but you tend to see the price action more slowly.

But infrastructure that's you know, could behave very differently. Hedge funds, if they're good, hedge funds should have lots of protection strategies built in and lots of short holding as well as long holdings, and so should be less sensitive. We talked about c T A S a little bit, one of the few strategies which has a right tail to it run the left tail that could be part of

your list of strategies as well. I think the core things from my perspective would be recognize that you're not gonna build a forecast the next difficult events number one. Number two, you can't forecast it, then don't tin all your diversification on a single thing. Have a range of protection strategies out there. And number three would we make sure those protection strategies are not too expensive to run.

And that, of course is the disadvantage of buying put options on the SMP or buying VIC futures that they're very expensive to run. So you need something that you can actually put up with for a period of time. I often see people that go and buy those more expensive strategies and they do it for six months or twelve months or eighteen months, and then they give up. And oftentimes they managed to give up just before the next bad event happens. That's been a terrible outcome. Yeah.

With with earthquake insurance, it turns out that there's always a spike right after an earthquake, which is the least likely time for there to be another earthquake, and by the time enough time elapses where risk has gone up dramatically. People have forgotten about it and they let that um, that risk lapse. Um, I wanna. I want to emphasize something you said, And it comes back to that question someone ask you at the conference. You're not really thinking

about the specifics of the potential risk. It almost doesn't matter if it's a bond risk or an equity miss risk or some geopolitical risk. It's hey, we can expect these asset classes to go down, these st classes to go up with a whole lot of increase in volatility. The black swan matters less than your preparation for some unforeseen event? Am I stating that correctly? Absolutely? Yes. And we've talked a little bit about how, and people ask me at conferences forecast the next black swan. I think

it's actually the question I get asked the most. Because I'm a strong believer in this phrase that there's no such thing as a bad question. But I think that one actually might be the bad question, because by definition, you can't forecast the black swan. That's kind of what a black swant is forecast of all. So So let's talk a little bit about the technology you guys use to create these models too, and to model out risks

and and other strategies. You build all that stuff in house. There, you're not really a big buyer of off the shelf UM risk management technology. Tell us a little bit about your approach, which seems to be pretty comprehensive to thinking about and planning for unforeseen risk. Yes, so, I mean that's the first thing i'd say is that we essentially build all of our own technology. We don't really buy technology. We we buy the hardware, of course, but but we

write all the software, all the code ourselves. And that's because we think that the things you can buy off the shelf, or everyone can buy it off the shelf, and therefore it's not really going to be a competitive advantage. It's going to be it's going to be maybe a base standard. And not trying to criticize the external products.

I just think that if you really want to have an edge UM in building risk models or building short term forecasters of risk or return or whatever, you need to write your own code, your own software, and and and you need to put a lot of effort into that, and you need to create an environment where you can

hire the best software developers. And I often see people saying well, you know, I hired a bunch of quants and I had a bunch of developers as if you know, that's the sort of a generic thing like buying a loaf of bread or something. It's just not it's you know, the best developers hundreds maybe even thousands of times as productive as the average developers. So getting those best de plenty your organization really important, and thinking about why they would want to work for you and not want to

work for somebody else, that's pretty important. UM. So I think for us, we felt that we can have an edge by building better technology than you can buy off the shelf and UM and then in order to get that, we've invested a huge amount in providing UM a good environment for UH quantitative researchers and technologists to operate in. And just to give you a sort of a side example of that, we open source quite a lot of

our code. So that means that, you know, we pay our developers to write code for us, and then we go and stick it on a website for other people to download it if they want. So why on earth, you know, what would possess you to do something like that? And the reason that we do it is that that then provides advertising to people that we're actually really serious software develop opers and that we take our code really seriously.

And if you're a young software developer, you'll probably see the stuff that we've published and say, well, you know, actually I wouldn't mind working in a place like that because code and and and technology and standards and all those sorts of things are really high at at this firm. So that's how we think about UM investing in technology, investing in developers, creating a culture where developers and quant researchers really want to work. And the reason for that, UM,

why would we carry on doing all this investment? It is really that you need you It's a very competitive business and you need to stay ahead all the time and you need to carry on innovating all the time. And if you don't, then somebody will eat your lunch. So from our perspective, we're always building new models. We're always coming up with new approaches to estimate risk. We're always worrying about, you know, how can we find a

new alpha source, what my go wrong? UM? And how a market is changing in their structure this uh, you know, big effect of more retail investment in retail investors in equity markets today, how should we respond to that. That's really something which is just a very ongoing and continuous form of a place for us to invest, and when we try and get the benefits out of out of that over long term, m really really kind of interesting.

Let's talk a little bit about machine learning. You guys have been on the cutting edge of that, including a collaboration with the University of Oxford at the Oxford Man Institute. Tell us what you guys are doing with machine learning and does any of this relate back to volatility? So what we're doing with machine learning is we're really saying that financial markets have patterns in them which you can dig out often profit from if you um if you

look hard enough. The problem in financial markets is that the patterns are are pretty weak. You know, they're not They're not simple patterns. There are people like me would say there's a low signal to noise ratio. There's a lot of noise and not very much signal out there. So what are we using machine learning for. We're using it for a number of different things, but the underlying theme is that most models that people use in markets, and you could even think of it just as a

value model. You know, you pee, for example, price over earnings. That's a linear model. It seems to sort of assume that price goes up in line with earnings. But we all know that when you look at markets, if there's one thing they don't ever do, it's move in a linear or straight line fashion. They move into every shape you could imagine except for the straight line. And UM, And what machine learning is really trying to do is

to say, can I find much more subtle patterns? Um the straight line, which is what most of finance actually ends up using for modeling, And here are some examples of that. One that I'm particularly excited about is UM what we would call natural language processing, which is having machines read text. Now we all know that there's far too much for us all to read. You know, nobody can read every analyst report, every company earning statement, every

annual report, attend every investor or day. Those are too much. So wouldn't it be wonderful if you could have machines to all that reading for you and tell you what to think at the end of it. And that might sound like science fiction, and at a certain level, I think it probably is. Science fiction today. But five years ago, if you said, well, machines will process images, will process pictures better than humans, I think people would look at you a bit funny and say, you know, well, no, no,

not really. Now you know you have that on your phone. You just type a word into your phone, into your photos library and just watch it happen in action. It's just extraordinary how it will find all the pictures that reflect the words that you've that you've typed in. So machines definitely do process images better than humans. It's well known. For example, processing X ray images looking at for cancers is much better done today by humans, by machines and

by humans. You might want the human at the end, but you want the machine to do all the sifting type work. And so one example of machine learning is getting machines to understand text and tell you, you know, go ahead, machine read every analyst report on you know, these hundred stocks, and tell me you know, not only the analyst reports, but all the newspapers in every language around the world, all the ending schools, read all of it,

and then tell me what to think. Um, that is something which today is science fiction, but I don't think it will be in five years time. M that's that's really kind of intriguing. My last regg your question I have to ask you is, so you spent most of your career developing quantitative strategies. What are some of the biggest changes you've noticed. I think the firstly is actually a bit striking what hasn't changed? So a lot of

things haven't changed. You know, the many of the standard models today are basically the same as the standard models twenty thirty years ago. We still use black sholes for pricing options. We'll still use the Barre risk model for calculating equity risk in portfolios. We still send data in very similar ways to the way that we sent it twenty thirty years ago, for the most part, in really terrible file formats. But nobody seems to come up with

a better convention that everyone will accept. Um. So there's a lot of stuff that hasn't changed, But I think there are some things which have changed. The first is that people often like to sort of characterize the world. There's there's the quantz versus the discretionary people. So the quants, you know, the model driven people in some sort of battle with the discretionary people. And I say, I don't

view it that way at all. I think that everyone is becoming much more quantitative in the way that they build and run their portfolios. The tools that we all have today on our Bloomberg terminals or on websites or products that we can buy from third parties or build ourselves, were essentially unimaginable um five or ten years ago, and everybody's got them. So give you an example. Fifteen twenty years ago, I was building quite sophisticated screening tools that

would search equity markets for opportunities. Today you can basically do what I built on a Bloomberg terminal. So everybody's got it, or everybody that's got a Bloomberg terminal has got it. So there's been you know, all investors, not just the quants, but the discretion marriage as well. They've all become more quantitative. We've seen it was trading as well. Trading used to be people shouting each other on a

trading floor. Today it's it's all machines and in almost every market around the world, and very sophisticated machines and very sophisticated algorithms trading with each other. So what I think I've really seen is everything in markets has become more quantitative. But then there are some things which have been kind of, you know, unattainable. So far, credit markets have remained stubbornly sort of immune to being taken over by more quantitative strategies. So far, private equity is the same.

It's it's really done in the same way as it was twenty or thirty years ago. Um, And I think that will probably change over time. Was certainly starting to see that in credit, for example, where the markets are starting to trade more like equity markets or futures markets, and it is starting to be possible to build the same sorts of risk models and the same sorts of of alpha models, of return forecasting models in credit that

you've had in equities for a long time. So I think my real thing is that everything has become more quantitative, and I think it is going to become a whole bunch more quantitative over the next twenty years. Alright, so let's jump to our favorite questions that we ask all our guests. Tell us what you've been streaming this past year, or anything interesting that you're watching on Netflix or Amazon Prime or or whatever. Um. I'm not a huge watcher

of things on streaming services. But you know, I have been um watching a rewatching a series of films by a famous British actor called Bill Nike, written by a playwright called David Hair. The first of them is called Page eight and there are quite sophisticated plays about um An m I six agent and his life struggles. So that's what I've been streaming, and I you can find it on on on Netflix. Page eight is the is the first of them. Well, we'll definitely check that out.

Tell me us, tell us a little bit about your mentors who helped to shape your career. So I was very fortunate in the first fifteen years of my career I was at Golden Sacks. It was an outstanding place to work. The people that I particularly worked with over that period were really two or three folks. Um Manny Roman, who today is the CEO of Pimco, I worked with pretty much twenty five years, both at Golden Sacks as well as at Man Group. Before he went off to PIMCO.

Worked with a fellow called Garish Ready who went on to run a fund of funds business called Prisma Um and a fellow called Mark Zorak, who went on to be a professor at Cornell, and I think it was important to me to have a variety of different people to learn from and to sort of build different experiences. All three of them are extremely different people, but I really learned, I think, both how to manage people and

how to get to the core of a problem. I think, how to work out what was important and what was not important, and not to give different practice of equal weight when you're making decisions. And I learned that differently from from each of those um three people who are really sort of my core mentors. Very interesting, tell us about some of your favorite books. What are you reading right now and what are some of your all time faves. Well, so, I'm I am very interested in architecture, and so I

tend to read relatively quirky and eclectic books. I'm currently reading something about Nordic Modernism, which I suspect will not be that popular with your audience. It's a niche area. UM but um So, I I think architectural theory is something which I'm very interested in. In another life, I might have been an architect. Um So that's sort of one area of interest for me. Um. I'm actually a

keen piano player as well. So I know you asked about books, but I play a lot of music, and at the moment I'm um playing um, some early twentieth century music by Debussy, which drives my family nuts because it's very hard to play, but if you play it well, it sounds good. I'm not sure I've mastered how to play it well yet, So that's another sort of core part of my life is try and play the piano for at least an hour every day and that sort

of straightens out my mind at the end of the day. Um. And in fiction, I amid I'm really a sort of enthusiast for mid twentieth century writers, So Treuman, Capote or Gray and Green or sort of that. That group of

writers are all my favorites. Very interesting. You know, there's some really fascinating I know you're not a big video guy, but on YouTube there are some really fascinating shows about music, like Song Exploder or Polyphonic or there's just a run of things that take apart various genres of music from a musicologists or historians perspective. There if you're if you're a classical music fan and play the piano, you may find some of that stuff interesting because the parallels back

and forth between classical music and pop music. The pop audience is not familiar with it, but the classical fans clearly are. You you might find some of that stuff in interesting. Um. And actually there are very strong parallels between music and architecture as well. Architecture is all about rhythms. People often don't see it, but they're there. Well you, I'm gonna assume that someone like you read godal escher

Bach twenty years ago. Um, am, I uh so they're the same parallels between pattern and repetition and and how they things morph over time. It's it's it's really and and I never thought about architecture, um the way music and math show up and and art show up. But I guess in a lot of ways, especially with UM, with with larger buildings, clearly some of those fractal progressions are are there. I'm we're really off on a on

a digression. Let me let me go to my next question. UM, what sort of advice would you give to a recent college grad who is interested in a career in quantitative strategies or risk management and find it so My advice would be not to get too narrow too quickly, and to try and build as broad a range of experience as you can. In my case, I did quite a few things in the early years of my career and they really turned out to be differentiated for me later on.

So I worked a bill in corporate finance. I worked out it wasn't for me, but I learned to heck of a lot in my a couple of years of doing corporate finance, I worked and fixed income and equities and credit. I work on the cell side as well as the buy side, and that was incredibly valuable to me. It gave me just different approaches to problems when I came across them. So I think that's the first piece

of advice. The second piece of advice, I think is that like most quants, I thought I was good at math and um and you know it probably wasn't bad

at it. But it turns out that actually there are a lot of people that are good at math, and I, from my perspective, learned that I had some skills which maybe differentiated me a little bit from the core skill of all these other people that were good at math and those in my case where I was good at making decisions I think I remained good at making decisions. I can see things, I don't have to spend a lot of time thinking about it. I can decide and

move on and have too much regret. And I learned that I was, I think, better than many quants a communicating a quantitative things in straightforward English, which most quants are not pretty good at. And so I really tried to work out what are my other strengths that my differentiating strengths, and and and and tried to use those.

And I would recommend any quant workout. If you're just going to go straight for a math competition, you know there's some people who are pretty damn good at math out there, so you're going to have to think about your extra strengths to things which separate you from the crowd. That makes a lot of sense in our final question, what do you know about the world of investing today? You wish you knew thirty or so years ago when you were first getting started. So I think thirty or

so years ago. Um. I think the thing that I most didn't realize was how much more tech and quant focused the world was going to become. And I slightly under emphasized my quantum tech skills at that time. So that's the first thing I think, and I shouldn't have done. You know, today it all feels very obvious that tech has dominated our lives so much, and behind most tech through a lot of algorithms, but thirty years ago it wasn't so obvious. So that's the first thing. I think.

The second thing that I wish i'd known thirty years ago was that it's very easy to gravitate towards the glamorous businesses. So when I was sitting on trading floors that called them sacks, then the glamorous bit was was trading what we call the exotic derivatives or the more complicated derivatives contracts. But actually that business almost doesn't exist anymore. It does exist, but it's really much smaller than it was twenty years ago. And that's what everyone sort have

wanted to do. And then there were other things which were apparently less interesting, like understanding equity in disease um or um or building quant equity models or something like that, and those turned out to be much bigger things. So I think the second thing which I've realized, which I wish i'd realized, and understood twenty years ago is the glamorous stuff is not always the stuff to go for. Often it's the stuff that actually people sort of think

maybe it's a bit boring or something like that. Um, but there are very often the big opportunities lie in the stuff that people think is a bit boring. M really quite quite interesting. Thank you, Sandy for being so generous with your time. We have been speaking with Sandy rat Trey. He is the chief investment officer of the hundred billion dollar Man Group, as well as the co inventor of the VIX index and the author of the

book Strategic Risk Management. If you enjoyed this conversation, well, be sure and check out any of our previous three d and fifty or so UH interviews that we've done over the past seven years. You can find those at iTunes, Spotify, a cast, wherever final podcasts are sold. We love your comments, feedback and suggestions right to us at m IB podcast at Bloomberg dot net. You can sign up for my daily reading list at Rit Halts dot com. Check out

my weekly column on Bloomberg dot com slash Opinion. Follow me on Twitter at rit Halts. I would be remiss if I did not thank the crack staff that helps put these conversations together each week. Charlie Vollmer is my audio engineer. Paris Walt is my producer. A tick to Val Brunn is our project manager. Michael Batnick is our head of research. I'm Barry Ridholtz. You've been listening to Masters in Business on Bloomberg Radio.