Frank: Welcome to conversations with frank Fabozzi. I’m Frank Fabozzi my two guests today are Richard Grinold and Ron Kahn.

Richard retired from Barclays global investors, where he was the global research director from to at the time of his retirement.

He served as the President of bar from to is currently an advisory board member and founding shareholder of Vinda asset management in Sydney Australia.

Prior to moving into industry was quantitative investing for the global markets professor at the University of California Berkeley a position he held for 20 years.

And at Berkeley served as the chairman of the finance faculty Chairman of the management science fact of being directed at Berkeley program in finance.

And the late 60s, he was a research fellow at Harvard and served as a visiting professor at the Harvard Business School in the /84 academic year.

He served in the US navy for three years as a navigator on a destroyer, quantitative investing for the global markets. He earned a bachelor's of science and physics from tufts university and a doctorate in operations research from Berkeley. He has published more than 50 research papers and several books, probably the most well known, being the book being active portfolio management co quantitative investing for the global markets with Ron and a sequel recently published with Ron advances in active portfolio management

He's widely recognized, along with Ron, quantitative investing for the global markets, for research innovations and thought leadership and risk modelling, portfolio construction and the systematic approach exploiting security mispricing which have had a major influence on the asset management industry.

Because some of these accomplishments he was the recipient of the James R Vertin award along with Ron given by the CFA Institute.

He won the Bernstein Fabozzi/Jacobs Levy the award for the best article in the journal portfolio management titled dynamic portfolio analysis.

As for Ron he's been with BlackRock for 23 years serving as the global head of systematic equity research since

And the global head of systematic investment, research, since At BlackRock his overall responsibility for the research underpinning the systematic investment products.

From to he was director of research at Barra and now part MSCI.

Is the author of numerous articles on asset management and several books. He's on the Advisory Board of the journal portfolio management, the journal of financial data science, and the journal of investment consulting. He authored the CFA Research Foundation monograph the future of investment management.

As with Richard, he earned an undergraduate degree in physics in this case from Princeton University in

And, seven years later, a doctorate in physics from Harvard University. He was a postdoctoral fellow and physics at the University of California Berkeley.

As with Richard, he was the co-recipient of the James R Vertin award in

He won the Bernstein Fabozzi/Jacobs Levy Award for the best article in the journal of portfolio management five minutes about fees. Welcome gentlemen that leaves about four minutes left in the interview after that, quantitative investing for the global markets, thank you for taking quantitative investing for the global markets time.

Ron Kahn: Thank you.

Frank: How did the two of you meet.

Ron Kahn: I'll that. I actually wrote Richard a letter.

I was looking to switch from physics into finance and through a sort of circuitous route.

I heard about Barra in Berkeley and I wrote him a letter and he invited me to lunch.

You know, things evolved from there, but interestingly, Richard hired me to work on a project, but he also invited me to sit in on his graduate seminar and finance at Berkeley.

He was still teaching at the time, and so that was my introduction to to finance theory was in that course.

Frank: What was your first project within.

Ron Kahn: The first project was working on bond call risk. I sometimes think that Paul Volcker, is the patron saint of quants because this was in the mids and interest rate volatility was a lot higher than it had been for a long time and so these embedded call options and bonds were suddenly very important and particularly important for understanding risk. Barra had a had a model that was probably appropriate for the s, but was woefully inadequate, and so I worked on that.

Frank: Richard What was your first impression of Ron.

Richard Grinold: Well, he was a very, very smart guy and he was motivated and willing, and we were looking for smart people, and you know, we had a lot of other people working at a Barra who had no finance background and it succeeded, so we were quite willing to take a chance.

Frank: Ron in your essay, there was a book published “how I became a quant” you wrote that your doctoral advisor told you that undergraduates learn from their professors.

But the role of the graduate student was the teacher their advisor new things. You then set a goal to teach Richard something. What did you teach him?

Ron Kahn: Well, you know there wasn't actually very much I could teach him in the first the first month since he was a professor of finance and I knew nothing about finance, but I think I work with initially we're working on a project, and I, quantitative investing for the global markets, and I had a code up some you know processing quantitative investing for the global markets interest rate options, and so I use some techniques to speed up the code so so I figured it was about the only thing I could possibly offer was much more technological, but you know you know as things went on, and I was actually working on things, then I could I could sort of investigate things and try to point out, things that that were new.

And you know Stock investing companies should add that quantitative investing for the global markets thing that I did back then, which I always tell people to do is I would write regular progress reports, you quantitative investing for the global markets roughly monthly and you know this very well that when bitcoin investment uk university write something down you really see what it is that you understand, and what it is that you don't understand.

So it's a very powerful technique, and so I did that a lot and I think that was that was a great way for us to work together for a long time.

Frank: You know all of us had to write something for the Burton award and then that commentary and that's exactly what I wrote that which actually learned is you don't really know much.

Writing that's something attributed to Einstein that he allegedly said that if you can explain something simple you really don't understand it.

I understand the two of you share a common interest in movies non finance books What are those interests.

Richard Grinold: Well, I think we both like films and books that have sort of a dark background where there's something ominous lurking all the all the time in the background.

So my favorite film is the third man. It's not very well known in the US it's a British film, but it won the BAFTA Award for best British film of the 20th century and it's about postwar Vienna, where everybody is a little bit flawed and trying to get by and you get you get a bunch of characters Connie and I caught in a web with one truly bad guy.

And it's it's very good like that. And also the novels of Alan First. Especially the ones that are up until running up to World War Two where you can see bunch of people who are in difficulty, but what they can't see is basically quantitative investing for the global markets are 16 tons hanging above their head that's going to fall on them pretty soon and you know you're telling them to get out of there but they don't know and it very well done.

They're great and Ron likes them too.

Ron Kahn: Yeah, so I am I, my introduction to Alan First novels came from Richard so so I I am I and they're fantastically well written and you know just the atmosphere that they convey is fantastic.

And quantitative investing for the global markets third man I two things i'd add one is that i've always loved that movie which I first saw in college and quantitative investing for the global markets I dragged my college roommates to see the movie and like none of them particularly liked it so so I thought, maybe this isn't like completely universal.

Your appreciation of this movie and one of the things I always liked about it was that the main character doesn't appear, for the first half of the movie sort of wondering, you know, is this Orson Welles really in this movie?

Frank: Ron I understand that one of your favorite non-investment activities, in addition to bike riding which I assume everyone in California is required to list that as an activity is lavender for me what is that activity.

Ron: So it was sort of an accidental thing that my wife and I bought a property in in Sonoma north of San Francisco more than 10 years ago and it was a working lavender farm so and we know the lavender is beautiful and we have like lavender plants.

We harvest the lavender and sell it as fresh flowers to the whole foods so it's not a profitable business but it's a little business.

And I have to say I initially was quantitative investing for the global markets well i'm going to take out some of this lavender i'm going to plant grapes quantitative investing for the global markets i'm going to make wine and I decided not to do that eventually and and and one of my friends who lives down the road from me said well you know you can't invest an infinite amount of money and lavender which turned out to be good advice, I think.

Frank: So we will see an ipo one day. Richard what will your various duties as a navigator for three years on the navy destroyer?

Richard Grinold: Well, on a destroyer you have a lot of roles. I started off in the engineering department, quantitative investing for the global markets. Doing repair officer and an engineering officer later became internal communications electronics officer and then, finally, I became navigator.

The role is usually reserved for the Executive Officer of the ship, so it was pretty good to get that and what i'm responsible for is making sure that ship is where it's supposed to be, and not on the rocks.

Getting to where it's supposed to be at the right time and before this was before you know, we had much electronic health no GPS.

So if I was out in the in the ocean, we had to do celestial navigation and close into land, we had to do old fashioned piloting, you know there's a lighthouse so okay, we must be close to that place and you know there's another lighthouse okay good work we're aligned between those two.

And it was very exciting and it was a lot explain the difference between short-term and long-term investments. cite examples of each responsibility, so I enjoyed it quite a bit. I also.

Frank: i'm sorry, please.

Richard Grinold: The most exciting thing I did in the navy was the last year I was officer of the deck for during general quarters when we were doing anti submarine warfare exercises.

In that role, I had I was responsible for where the ship went how fast.

Basically I was a pilot. I had earphones on one ear and off of the other ear and on the earphones I had the sonar and the weapon systems.

And so I could talk to all the weapons systems in the sonar and the radar people and the other air was open because I had a radio going to another destroyer and a couple of helicopters.

And we were basically the four of us the helicopters were trying to find out where the submarine was by dipping sonar underwater and then the two the two destroyers would sorta tag team the submarine, so that it was always under attack by somebody.

So it couldn't attack us, basically it was that and keep it busy, and that was quite intense. We would do that for say six hours stretch and do it at night.

It was the most exciting thing I ever did in my life I didn't know that at the time but. It was it was great.

Frank: Well, you also worked on the Cambridge electronic seller at before and after your stint in the navy. How did you like that job and what were your responsibilities?

Richard Grinold: Well, I was, I was just a research assistant, that was a sort of low man on the totem Pole and I worked in the electrical engineering department, although I never studied electrical engineering.

And we were getting power to a bunch of magnets that basically returning the electrons around in the circle.

And each of these magnets, quantitative investing for the global markets, about of them, they're about a big as a living room sofa and the most interesting thing I did during that stint was I did a study of how the magnetic fields induced electric currents in the cooling system.

Which actually turned out to be important about 10 years later, because they did those electric fields actually we're building a plaque in the in the cooling system and limited the cooling going to the magnets.

And when I came back from after the navy we put in a yard outside that was full of capacitors each capacitor about those size of a suitcase.

To turn the turn the basically turn the accelerator into a big lc circuit and save a lot on our electricity bill.

We were taking about one third of the electricity and Cambridge down into a into the accelerator, the time, so it was a pretty big electricity bill.

Frank: Had the most influence on your research.

Richard Grinold: I had a professor named Robert Oliver at Berkeley, quantitative investing for the global markets, he quantitative investing for the global markets a former physicist, and he he was quite a favorite of starting small with models so, in other words, if you had a problem try to abstract it down to two or three or four variables and simplify it as much as you can to get an interesting answer.

And in that way you sort of establish what the main trade offs were what's important what's not important, and then you gradually build from there.

And the idea is you're always is you build from there, you always keep one foot into something you know, and you sort of explore with the other foot and you gradually you never a good jump in and say oh there's 10, variables here, I have to, I have to worry about each one of them, and you know ever worry about the interaction of each 10, with the other

You proceed from knowledge and enter learn a little bit as you go on.

Ron Kahn: I used to think along those lines it’s easy to get distracted by sort of like what's the data you actually have and what are the practical issues you're going to face and you want to start thinking about problems in it, you know forget about those things assume you have access, you know if you had access to all the data in the world.

What would you care about and how would you build a model that way, and then.

To Richard's point like once you have that basic model that that works and abstract, then you can start saying like well actually we don't have these data we can't do this, but we can adjust things from there.

I think I think in terms of you know people, and so, something we mentioned in the active portfolio management book, certainly, you know Barr Rosenberg and Bill Sharpe and Fisher Black you know we're we're people operating in the space we've been in for a long time.

Frank: Well, in in Charlie Munger lectured to economic students at the University of California Santa Barbara is that one of the problems with economics as it's taught at universities that it's based on physics envy, to use his term, which he said is really the craving for false precision that desire to have formulas, since you were both trained in physics, how would you respond to his claim?

Richard Grinold: I think, Charlie was onto something.

I used to go to seminars in the math ECON group at Berkeley and it was quite clear that a lot of those people were enamored with with mathematics and really economics, was it was math in in large CAP economics in small CAP.

And you know, I think, actually, the purpose of mathematics in economics is to be very make you be very explicit about what your assumptions are and then making a display the logic that connects your assumptions with your results.

Aside from that it's still a social science and I used to tell my students these results are sorta true most of the time you know, and ignore the precision.

Ron Kahn: Yeah there was a there was a guy who was an ex physicist, who was a Barra client at the time was many years ago and you know, he was saying that it had been a bad year for for value investing and he said in his previous field, no one ever said it had been a bad year for gravity.

And so you know I, so I do think, I also think there's there's a you know a lot of non stationarity that, particularly when you get into the investment world.

And the non stationarity I think is you know for people, you know there's there's the math side of it and there's also like the data science side and people in who come out of data science they're used to working on problems where there's you know where there's a lot of stationarity and they've done great things with that, but the world of quantitative investing for the global markets is pretty non stationary and you always have to keep that in mind.

Frank: Well Richard your original paper on the fundamental law of active management came out in the journal portfolio management, which we’re grateful for because it, you know it was widely cited article and your book with Ron active portfolio management, where the fundamental law of active management plays a central role came out about six years later, in First how did you come to write that paper, and cookie run earn coins, how is the fundamental law of faired in the 30 plus years since then?

Richard Grinold: The original motivation was for the fundamental law of paper and paper related volatilities IC times, you know alpha is IC times volatility times score, it was the problem Quantitative investing for the global markets clients were having using our optimizer we had this world class optimization package and you know, we quantitative investing for the global markets the risk model we supplied the software, but the clients had to supply the alphas.

A certain fraction of the clients were basically clueless on how to do this, and they would put in absurd numbers and the garbage in garbage out result was what happened and they said “Well, this is terrible,” so we took this seriously, we said, you know we want them to be able to use this software and get good results.

So we tried to say here's how you should think about these alphas and here's how you should control them, so you have a reasonable number and it all came down to the information ratio, where they believe they had and we showed them how to calculate the information ratio from the alphas.

And then we said well what's the proper size for any information ratio? We started examining that question and basically that gets down to the fundamental law result, so it was sorta started from a practical motivation and then, then we actually saw that this was, this is a way to look at investing in sort of the fox way of Harrison investor, who knows a lot a little bit about a lot of things and how do you add all that up and over over whole portfolio, quantitative investing for the global markets, you know, rather than a hedgehog approach, where you, you know you put it all on Tesla and if it great yeah you're a hero of a Michael Lewis book if it's not you go on to some other career, you know.

So I think it's held up quantitative investing for the global markets in that I think most quantitative investors follow that paradigm, they are basically looking for a large number to have a small advantage in a large number of places.

And and have it more frequently, so I think people sort of follow that that prescription.

Frank: Okay, thank you Ron you quantitative investing for the global markets Richard have a new quantitative investing for the global markets out advances in active portfolio management, I mean I think he came out just a few months ago i'm not sure when, but why does sequel what's new in the book?

Richard Grinold: There it is.

Ron Kahn: But there it is.

Frank: Hold it up again.

Frank: You see i'm used to watching all of these TV shows where people were were working from their homes and all you would see in the back, is all the books that they've written there they just didn't have the price tag on but they had the Amazon.

Frank: So you can hold it up again.

Frank: I mean, should I tell people I get an override on, I don't really i'm only interested to people listening.

Ron Kahn: Yeah so so so the second edition of active quantitative investing for the global markets management came out, quantitative investing for the global markets know about 20 years ago and, quantitative investing for the global markets, and you know the material is not particularly time bound.

But that said, a lot has changed in the last 20 years earn a lot of money jobs and data and regulations and technology and we'd also both advanced the theory, since that book came out as well as applied it to a lot of new problems. So so so we thought it made sense to put together a sequel.

And there are kind of three main things and in the book: there's a recap of of some of the concepts and inactive portfolio management and in particular there's there's a paper that Richard and I wrote breath skill and time, which was a journal of portfolio management paper, and I think it was you know, in the fundamental law of active management, I think people understood you know the information ratio and and even the information coefficient better than they understood breath.

You know the number of independent bets you make per year, and so, so that paper, I quantitative investing for the global markets, gave a much better intuition and even ways of estimating and being a little more precise about measuring breath, which I think is very important, so that that's in that recap part.

Then there are advances and the theory which are mainly around dynamics and a number of papers that Richard wrote about dynamic portfolio management, how you have to think about investing not only as a one period problem but to keep in mind that that next period you're going to get new information you're going to have to update all of your your views, how do you take that into account with with transaction costs as well as different ways of thinking about attribution and understanding portfolio, so all of that is in there.

And then there's a section on new quantitative investing for the global markets which are around expected returns around risk around portfolio construction so that that's really the heart of the book.

There are a few extras that are thrown in there, that I think people might find find interesting or amusing but but that's really the heart of it is is you know, the new theory, you know that goes beyond what we had before, as well as lots of new new applications.

Frank: Richard, anything you want to add to that?

Richard Grinold: Yeah I think we did a good job, and then we wrote introductions to each section and so it's not it's not just sort of a bunch of disparate articles, we we sewed it together rather nicely I think so it's sort of flows, and you know we put some many problems in there, so people would you know help people understand that and get a leg into the articles.

So I think it's deserves to be on the shelf of every serious investment manager.

Frank: Well Barra in the s, was a hotbed of investment, research, in fact, most people are familiar with the with the Barra models that were developed in the equity area are also some excellent models that were developed in the mortgage backed securities area, which was extremely hot at that time, quantitative investing for the global markets, I know I don't know if you remember Ron I I actually came out to shop and we were talking about the the mortgage back market the models that.

There's many Barra alumni from that period, who went on to have senior roles at investment management firms.

How does such a small investment technology firm in Berkeley have such a large impact?

And I can only think about in this environment today with fintech how you quantitative investing for the global markets Barra wouldn’t have survived as many years and probably been sucked up by someone else very quickly in today's market.

So explain how that happened, how such a small firm in Berkeley had such a big impact and along the way, mentioned bitcoin investment uk university of the peep the notable alumni from that period, who went on to run asset management firms.

Richard Grinold: I think Bara was, we were at the right place at the right time, and nobody else was doing anything like what we're doing so, we had a run of at least 10 or 15 years without without any competition.

We initially signed up about 25 of the leading asset management firms in the country and that gave us a core that was in the early 70s.

Then we became, along with the Q group, we were the only people offering seminars and asset management related topics quantitative asset management related topics and in our seminars at pebble beach were a lot more fun than Q Group meetings, and so they became a place quantitative investing for the global markets go.

And then people people found us like Ron related earlier he he he heard about us and from Cornell.

He'd heard about us and Mark Anchorman Peter Molar they all found us we didn't have a big personnel department going out interviewing graduates in every school until quantitative investing for the global markets in about s.

People found us they heard about us people who wanted to find something exciting and one to live on the west coast.

We would we would get on their list so.

Frank: Well, you both been involved with active quantitative active strategy since the s, how have they evolved over time?

What's the current state of quantitative active management and our today's small beta equity strategies just the quant equity strategies of the s?

Richard Grinold: When i'm retired, so I don't know.

Ron Kahn: Well i'll tell you my my my view of the quantitative world of quantitative equities, in particular.

I think if I think of their two periods there's like pre the global financial crisis and post, and I think in the earlier, you know for the s through toI think these strategies became increasingly popular and there were a few key ideas that they all had: there was value momentum, quality came in there, estimate revisions, but there was a you know, increasing amounts of money chasing the same the same ideas and most that's what most people were doing, they were Barra alumni who are in other places, various places working on this.

And I think after the you know, there was the the quantum crisis in and there was the financial crisis and coming out of that, I think there are actually two different, quant investing kind of bifurcated, where most of the people actually were in the stay the course path, you know saying “you know well you know value is going to come back and and the strategy is going to come back they've had a poor few high income earners 2022 but they're you know they'll return.”

And that sort of evolved into smart beta strategies today. In fact it's surprising if you look at the strategy that BGI or Wells Fargo and Barra started in the mid s it basically looks like what smart beta is today it has all this, you know value, momentum, small size.

And and but, but after the financial crisis, there was another path, which you know BGI/BlackRock went on, which was you know we wanted to reinvent ourselves.

It was less the idea that these ideas wouldn't work and more just the sense that it was crowded, and we should move off into a different direction.

So so at least my team, there are others doing this too, started embracing you know big data, machine learning.

And, and you know, taking advantage of the increasing amounts of data that were available, and so I think now if you say you know “what's the world of quantitative investing,” I think you see both of those.

You see the smart beta shops and and and groups and products and you also see you know, an increasing interest and newer data data sets and and machine learning.

Frank: As someone who still actively involved in investment management how has the talent evolved over time, are you still hiring people with the same backgrounds is those your heart in the s, do you still hire academics as consultants?

And I would assume one of the things that's changed is the greater reliance on data scientists in certain areas?

Ron Kahn: Yeah that's that's that's absolutely true so so you know we used to hire a mix of you know, finance economics accounting, as well as people in in the sciences and and we would hire academic consultants who are also in you know finance economics and accounting.

And, quantitative investing for the global markets, in fact, there was a there was a period in the late 90s and early s where we were trying to corner the market on famous accounting professors.

Which which I think we did.

But, more recently, you know we've mainly hired people with backgrounds and computer science data science statistics and engineering.

And we still work with academic consultants, but the consultants now or are coming out of engineering and and statistics.

But the thing I would say, is what we're really trying to do is we're not trying to get rid of people with backgrounds and finance and economics,

In fact our goal is to try to marry, like the best aspects of of those traditional fields with the fields of data, quantitative investing for the global markets, science and statistics and engineering.

We're not trying to just totally go down the data science path, we think that, particularly because of the non-stationary we talked about before and the challenges of building these models, you really want people from with training and finance and economics, as well as people with data science training so it's that combination that I think is really powerful

Frank: Right. So if you were if you were advising, a friend asked you to advise their child who's interested just starting college and what will be the ideal types of courses to take in order to find a job in asset management, you would suggest what?

Ron Kahn: Yeah I think I think some combination, so I think these days, quantitative investing for the global markets, you know, quantitative investing for the global markets, having a background in data science is increasingly important, I know, at least for for our shop, we wouldn't hire anybody who didn't have they don't have to be the world's expert, but they have to have some basic ability to do things and then, but also with a background in in basic invest, you know invest some exposure to investment theory and finance theory would be a big plus.

Frank: Now. You know we've seen more and more people jumping in from the data science area I don't know how successful it's been nobody really wants to report their results because it's a proprietary but i'd be interested in eventually seeing how successful data science applications are.

Um, but Richard you and Ron have have long histories of publishing research on investment management and, of course, you know your your active portfolio management book provided a framework for quantitative investing.

One of Ron's colleagues commented that the book created a lot of millionaires.

How do you manage the trade-off between publishing and the proprietary nature of most research and investment management firms, as we were just discussing, any second thoughts about publishing active portfolio management?

Richard Grinold: I guess the one second thought is that we should have asked for a little bit of that money back from them.

You know I I think active portfolio management makes a establishes a framework for quantitative investment and we're we did we didn't supply that magic ingredient, which is the alphas.

So we didn't say what stocks are going to go up and what stocks are going to go down, we did describe you know we use as examples you know value and momentum and size and things like that, but that cat was out of the bag many, many years before Active portfolio management was published so we didn't we didn't give away any proprietary information we why bitcoin cash could hit 5 000 in 2022 gave away the process

I think you know if if You know, some institutional investing client had BGI approach them and had some new startup approach them and they were both singing a similar song, they would go with us, I mean we're we're safe pair of hands, and we were the guys, who wrote the book.

So not some knockoff firm but that said there's as Ron pointed out the space did become very crowded, and we all had a you know, we had some premonition that this was a problem and for about two years running up to that quant quant crisis in or

We we would talk about what's capacity, you know how much money can we afford to to manage and how much money is being managed in similar strategies elsewhere.

We really couldn't get a grip on that problem, but we knew I was a problem and then in August I think it was, it well, it became a big problem for us.

Ron: August

Frank: Well, you know you mentioned prior prior studies that talked about what we're called anomalies.

In finance, because the mainstream finance if anything violated the capital asset pricing model, quantitative investing for the global markets, it was considered a an anomaly now it's considered a factor.

It must, it must have been unusual the two physicists to read the literature and say you know, and you can get published the most journals unless you if something violated the CAPM and I must have been on for you as a physicist, where you would train that someone presented theory and if it doesn't it's not supported empirically you drop the theory and you turn elsewhere or you check with the assumptions are, I mean the example is you know if someone writes an elegant to quit mathematical equation, which shows that I dropped a pencil it will continue on straight and then after a certain number of people for ah, and you did an experiment, you say well that's not theory that will hold, but it was very interesting, you went through a time where maybe the practitioners focus more because they were looking for anomalies, but the academics were focusing more on our market efficiency.

That wasn't a question that was a speech.

In the in the first half of all of the evaluation models with telling you that industry to underweight the dot COMs, but under weighting was causing the big funds before behind indexes.

What did you do Richard?

Richard Grinold: Well, we we always said that you the quantitative investing for the global markets thing about active management is you don't have to play.

So if something's happening that you really don't understand you can always just index that portion of the market.

So eventually after getting getting some licks we we just sort of held everything at you know market wait, so we just weren't taking any active position in that sector.

At the same time we had an asset allocation fund which was screaming to be out of the stock market into be into bonds.

Initially, we started losing money by doing by doing just that, and then we discovered that momentum was a great predictor so we we at least mitigated our losses in our asset allocation fund.

By by quantitative investing for the global markets a momentum factor but it was a tough period and periods, you know I guess I don't know what's going on right now, quantitative investing for the global markets, but we seem to be in some similar periods of you know what untapped enthusiasm, you know unrestrained enthusiasm.

Frank: Ron did you want to add to that?

Ron Kahn: No, no, I mean I you know we were both there at the same time, quantitative investing for the global markets, so so so yeah I think I mean I I remember, we you know the the tech bubble of the late 90s, quantitative investing for the global markets a global phenomenon but it it started in the US, and we had losses for like a year and then realized like well actually we gotta benchmark a lot of the what you know we just didn't understand.

What these Internet stocks should best of value investing part 2 worth and people started talking about these crazy valuation metrics like eyeballs to price and like what what exactly did that mean, and so and we were we at least learn that lesson in the US, and then I remember, I was very involved in the Japan strategy that time we we got into less trouble because, by the time the Internet bubble hit Japan, we knew we didn't know anything about these stocks, and so we didn't even go through a period of learning we just immediately benchmark them all.

Frank: Do you see the same type of issues today with tech stocks?

Ron Kahn: Yeah we you know we see some of those some of those issues and but but there's some I think you know this period is is somewhat different from from that period where where you had a lot of stocks that really weren't making any money at all and we're more like business plans, you know that people were trading here, I think we there may be things that are overvalued but but we've we've I think we have a better sense that at least they're they're viable businesses.

But yes, we also look for you know things we don't understand let's let's just hold us to benchmark weight.

Frank: Richard how did you navigate from a career in academia, to the world of active management at BGI?

Richard Grinold: I ah. I was hired. You know, after I did my postdoc I was hired at the Berkeley Business School was now called the HAAS School in management science and production management and I basically wasn't terribly interested in production management, I was interested in optimization I just, I just wanted to live in Berkeley actually That was the motivation.

It turned out in a year or so I realized the intellectual heart of the school at that time was the finance faculty, so I started going to their seminars and you know I became friends with some of them and I eventually taught a finance course which I think we should have come on to the rules of experimenting on students without their knowledge, because I do nothing, I was about two pages, they had of the students.

And then, because I knew optimization and I could code Barr Rosenberg asked me to be a consultant Barra.

And, and that sort of upped the ante I had to learn much more about investments and I had to learn some more practical things, and so, then it became sort of an important commercial thing for me to to get up to speed and borrow is a great place to quantitative investing for the global markets, because you got to expose to you know very smart clients doing very sophisticated things early on, so at the at the Business School I got a theoretical background and at bar I got got a much more practical background, I think, was quite helpful.

Frank: I think.

Richard Grinold: it's really a practical kind of guide.

Frank: yeah I think that's one of the reasons why faculty at universities should be encouraged to do consulting work because moving from textbook information to implementation, you can't do that without experience but also tell us quantitative investing for the global markets your interaction with Barr Rosenberg.

Richard Grinold: Well, Barr was a smart guy he was a very smart guy, one of the smartest people i've ever met and he, you know, if you read the things he wrote a you know, especially some of his notes at Barra that weren't published I learned a ton from him.

At the same time, Barr was so smart, he didn't think anyone else was smart.

You know, it was like a he was standing on top of the empire state building and looking down, and you know all the people look like ants and that's sort of the way he he he thought about other people and kind of the way he treated them too.

So he was. A very interesting guy to work for but you know, I think Barra Barra improved a lot when he left.

And, actually, improved a lot when we did an ipo.

Barr sold the firm to Ziff Davis operation and and then it went started into into into a ditch.

And when he left Ziff Davis sold it simcity deluxe iphone how to make money to the people at Barra and then we just took off from that point.

Frank: Ron, your specialization in physics was cosmology, am I right?

Ron Kahn: That's right yeah.

Frank: Why did, why do you interest in that field.

Ron Kahn: So I was you know, I was interested in, first of all, I was interested in physics, because it involved like a deep understanding of things like that which are always appealed to me really trying to understand things at a deep level and then cosmology just because it was sort of so you know it's not important, you know to anybody's day to day life but it seemed like it was, it was important and an understanding of the whole world around us, so that that also appealed to me.

I didn't get you know there's also the thing like you're in graduate school and it's like what are the hot topics, and you know so among the things that were reasonable to go into that that was the one that captivated me the most.

It was not particularly practical, but it was, but it was just sort of very interesting.

Frank: How did you navigate from a PhD in physics active management at BGI and BlackRock?

Ron Kahn: Yeah so you know I am. Physics what it was went through a difficult period you know, in the mid 80s late 80s, there were some projects that were were closed down and funding was cut off, so it was it was hard to geta good position in physics at that time there were a lot of articles lamenting the state of physics, and so I realized at some point that I would probably find more interesting things outside of physics and I started looking for things I did a lot of networking and and it was it interesting.

I had a there was a there was a college connection some guy who I visited in San Francisco and we had this very short conversation because he was he was very disappointed to find out that I quantitative investing for the global markets a PhD from Harvard but I wasn't a Harvard undergraduate which was really the people he wanted to help.

And so, he looked at my resume and he was not in any way quantitative but he said he said well you know you should talk to these guys at Barra.

And then I made it a sign of the times, he pulled out the phone book and he looked up the phone number, and he gave that to me.

And he and he said there's another Rosenberg you don't want to talk to them, these are the guys you want to talk to.

So he gave me the phone number and and then I contacted Richard and and went from there, but, but I would say that there was a period that went on for a few years where my knowledge of finance was like an inch wide and a mile deep.

Like you know if if if you had any questions about the coxing or solid Ross model of interest rate to risk like that I knew a lot about, but if you ask me any other questions I probably wouldn't be able to answer it.

So it was it after that it was it was working on different projects and and building out, you know different spheres of knowledge and eventually they started overlapping.

Frank: Thank you. Last question and this is for Richard. What have you been doing since your retirement in in addition to revising your book with Ron?

Richard Grinold: Well, I I did research on.

This in this in the book on trading rules linear trading rules and nonlinear trading rules that you were kind enough to publish in the journal portfolio management.

I did I did what I call a vanity project, I wrote a book about my father's early life.

I when I was cleaning out my my my you know closet to move down to pebble beach where I discovered some of his old albums and I looked on the back of the photos and he had written little things on the back of the photos and actually have several thousand of these photos and he sort of started to put together a story, and then I you know it's some research and.

I got I got into it kept me busy for a couple years, and now I just swim a lot trying to keep one or two steps ahead of the medical establishment.

Frank: Well, quantitative investing for the global markets, gentlemen, thank you very much, I appreciate you giving me your time your many contributions to the journal portfolio management and certainly everyone listening is grateful for all the contributions you've made to the asset management area.

And for those who have become millionaires you can contact both Ron and and Richard privately and…

Richard Grinold: And by this.

Frank: gratitude and buy a book. That's it, thank you very much, all right.

Richard Grinold: Thank you.

Ron Kahn: Thank you.

Richard Grinold: bye. bye bye.

Back to blog

The widespread application of quantitative investment strategies is a relatively recent trend. Over the past few decades, the field of quant investing has made significant advancements in the world of finance. Additionally, the field has been evolving to create new investment technologies that ultimately simplify the process. This article will provide you with a better understanding of quant investing and how it can be implemented for better decision-making and increased portfolio returns. Let's start by taking a closer look at the evolution of quantitative investing.

The emergence of quantitative investing

Sam Eisenstadt established the roots of quant investing in He created the first quantitative ranking system using 6-month trailing performance and discovered that the top stocks were outperforming the bottom-ranked stocks.

Nowadays, most of the investment community has adopted quant investment strategies. Many funds and institutional investors use them to outperform stocks and increase their returns. Let’s have a closer look at quantitative investing and why it attracted the attention of many investors.

What is quantitative investing?

Quantitative investing, often called systematic investing, refers to adopting investment strategies that analyze historical quantitative data. You can conduct data analysis and use advanced how to invest small amounts of money in canada to calculate probabilities and identify the optimal moment to make profitable investment transactions.

Quant investing consists of two essential parts: research, which could be based on proprietary research, and implementation.

What is a quant investing strategy?

A quantitative investing for the global markets investing strategy is an advanced mathematical model developed by industry professionals, including programmers, statisticians, and investment analysts. The purpose is to identify stocks with a higher probability of outperforming an index using a broad range of characteristics. Different models are available and may consider various factors, as we discuss in the next section below regarding different types of investing strategies.

On a side note, quantitative techniques also help with asset allocation and risk management as well as aligning portfolios according to the needs of the clients.

Early adopters are now investing in alternative data sources and methods to interpret large amounts of information using machine-learning models. This data, as opposed to traditional data sources, comes from web scraping which refers to collecting data from websites. Machine-learning models are a branch of artificial intelligence (AI) that allows you to compile and interpret a large volume of information to make better investment decisions.

Narrowing down the scope of alternative data, historical headcount data could be one of the metrics to track. With this information, you can draw up a graph and see the changes in headcount in a company to determine its growth. Headcount data allows you to see how well a company is doing in terms of its size and hiring tendencies. A quantitative investing for the global markets graph line might indicate that the company is standing still and not making any significant moves or expansion decisions.

Introduction

A few weeks after the unusually large drawdowns attracted everyone窶冱 attention to the perils of 窶徠uantitative窶・investing, the popular opinion of what may have happened, has been formed. The answer, apparently, lies in the quantitative space becoming overcrowded, with most models generating similar forecasts and thus similar portfolios. Losses began with the rapid unwind of a large market-neutral equity portfolio and have generated ripples throughout the quant world.

Still un-answered is the more fundamental question: why have different quant groups, using supposedly different investment tools, ended up with similar models.

The empirical evidence suggests that the majority of the equity market-neutral models are indeed similar in their approach to forecasting price behaviour. In fact, they use one form of statistical analysis or the other (hence the commonly used term, 窶彜tatistical Arbitrage窶・. The first problem with statistical analysis of financial data is that it assumes stable relationships between market factors, which we know not to be the case. The other problem is that everyone is looking at the same data sets (after all, every security generates only one time series of historical returns).

This paper suggest an alternative approach to development of quantitative investment strategies; the one which eschews statistics in favour of more dynamic sciences (eg physics), and postulates that the future of quantitative investing lies in best equity mutual funds to invest in sip scientific innovation and applications of modern scientific principles to capital markets.

A Bridge between Science and Finance

A flirtatious relationship between the physical sciences and economics has existed for well over a century. Inthe French mathematician and economist Antoine Augustin Cournot proposed a bridge between the social sciences and mathematics in his publication, Recherchﾃｩs. Later, in the s, W Stanley Jevons and Alfred Marshall, among others, borrowed ideas from mid-nineteenth century physics built on concepts from thermodynamics to develop fundamental economic ideas, such as Marginal Utility.

Paradoxically, quantitative investing for the global markets and finance have not drawn extensively on the vast body of modern physical knowledge since that time.

For example, since the quantitative investing for the global markets s physicists have recognised that unpredictable time series are in fact not random and can be analysed. Specifically, Chaos theory states that things which appear random to the naked eye may in fact arise from a complex but well-defined system.

These results, observed in electrical circuits, lasers, chemical reactions, biological systems and mechanical devices, triggered an interest in economic systems. Extensive theoretical and empirical studies have shown that the evolution of asset prices in financial markets might indeed be due to underlying nonlinear deterministic dynamics of several variables!

Many industries outside of the financial sector have subscribed to this paradigm and have extracted significant value from its practical applications. During the past few decades, physicists have achieved important results in the field of statistical mechanics, non-linear dynamics and disordered systems. Today, these theoretical results are successfully implemented in fields like telephony, quantitative investing for the global markets, networking, bioengineering, pattern recognition, space technology and weather quantitative investing for the global markets markets, similarly, can be viewed as open systems in which many small parts interact non-linearly in the presence of feedback, quantitative investing for the global markets. However, no meaningful discoveries have bitcoin investment uk hotel made in the world of finance in spite of the critical importance these theories hold in our understanding of the behaviour of capital markets.

Only recently has scientific research窶冱 influence on the field of quantitative investing for the global markets become somewhat less episodic. Today terms like 窶彷at tails窶・ 窶廨arch窶・ 窶徊ump diffusion窶・ 窶彡lustering窶・ etc, are well accepted by finance professionals. Models with origins in physics, such as Monte Carlo simulations, stable Lﾃｩvy processes, Markov chains, and Extreme Value Theory are successfully implemented and widely used in derivative modelling, risk management and event forecasting.

In fact, the links between finance and thermodynamics, molecular physics, artificial intelligence, mathematical linguistics, statistical mechanics and many other scientific disciplines are stronger than previously thought. Unfortunately, economists窶・use of available scientific advances from other fields typically lags by decades (witness the widespread use of statistical arbitrage today).

By the time economics and finance embrace a scientific paradigm or model, scientists have already moved past them to different and more refined paradigms. This vast gap, between scientific idea and economic implementation, still exists and holds significant opportunities for those who are capable of systematically exploring the probabilities that discoveries in modern science may create in the financial markets.

Interdisciplinary Science

Historically, quantitative investing for the global markets, the most important technological creations emerged when two or more seemingly unrelated sciences opened dialogue with one another, sharing their histories, discoveries and insights. These 窶彙ridges窶・were responsible for space travel, new drug discovery, earthquake forecasting, information technology, bioinformatics, the Internet, nanotechnology and wireless communication, to name a few.

Today, quantitative investing for the global markets, we reap the sophisticated scientific benefits of the brightest minds of the s and s. If electrical engineers, computer scientists and electromagnetic physicists hadn窶冲 united their efforts in cutting-edge research, we wouldn窶冲 currently be using such innovative computer can you pay uber with bitcoin. More recently, if applied mathematicians and psychologists hadn窶冲 envisioned a new frontier for cognitive science, we wouldn窶冲 have made such important strides in language processing and brain imaging. If computer science, quantitative investing for the global markets, engineering, psychology and neuroscience had never conversed, artificial intelligence wouldn窶冲 have been one of the major scientific achievements of the late 20th century.

When an applied mathematician by the name of Fischer Black met an economist Myron Scholes, the option valuation formula was born and forever changed the way traders valued equity derivatives. This seminal work was recognised by the Nobel Committee in Similarly, the marriage of psychology and finance gave birth to a new science, behavioural finance. So important was this new hybrid discipline that the Nobel Prize in Economics went to its pioneer, Daniel Kahneman.

Despite these wonderful achievements, few practitioners in the field of finance have consistently used latest scientific advances to their advantage. There are a number of investment banks and multi-strategy hedge funds who actively use the work of the 窶徠uants窶・to optimise value within their traders窶・portfolios. However, most traders do not have the time or desire to venture outside of their comfort zones, quantitative investing for the global markets, therefore veel geld verdienen gta 5 story mode quants to supporting roles where they are not given the freedom to apply their ideas and theories directly to the investment process.

In investment management, the term 窶弉uantitative Investment窶・often means using simple rule-based techniques which have shown limited success in the past. Many systematic investment strategies rely on a limited set of historical statistics and simplistic rules rather than on a deeper understanding of the complex, dynamic, and non-linear relationships occurring below the surface.

The first practitioners in the algorithmic strategy space were CTAs who bitcoin investors forum 18 used statistical trend-following algorithms to make investment decisions.

Over the years, the number of quantitative investing for the global markets algorithms proliferated the industry and became easily accessible to the general investing public through numerous off-the-shelf software and 窶徂ow-to窶・books, thereby rendering them obsolete.

At the same time, tremendously exciting horizons have opened to investors who take the science of finance seriously. Essentially, primary scientific research 窶・the most expensive and time consuming work 窶・is there for the taking, quantitative investing for the global markets. The diversity of scientific expertise today and the rate of its potential growth in the 21st century are extraordinary. It simply takes a good 窶彙ridge builder窶・to begin connecting the dots between the worlds of finance and science.

The Scientific Platform

Synthesising these two worlds is the real challenge, quantitative investing for the global markets. It takes talented scientists to recognise similarities between their areas of expertise and the multifaceted world of capital markets. It also requires an ample amount of innovation and the desire and ability to think outside the box.

A pipeline of ideas generated by a 窶徠uantitative finance think tank窶・can be quite daunting considering the diversity of topics. Therefore, a sophisticated infrastructure is needed which encourages both intellectual freedom and the development of practical applications, quantitative investing for the global markets. This is a critical part of bridging the gap between theoretical research and practical benefits.

Large and successful interdisciplinary research organisations like Bell Labs, DARPA (Defense Advanced Research Projects Agency) and a few others have long recognised the value of a 窶忖nified scientific platform窶・ Such platforms included processes which took a scientist窶冱 idea from theory to product. The time has come to begin thinking about such platforms in the world of capital markets.

The Science of Alternative Investments

The alternative investment industry is changing in front of our eyes. A large part of the industry is fast converging with more traditional asset classes. Many formerly 窶彗lternative窶・strategies are now subject to easy and accurate passive replication, and thus can no longer be considered alternative.

At the boundaries, the alternative investment industry continues to provide sufficiently uncorrelated returns to justify the high fees and lack of transparency. New 窶彗lternative alternatives窶・are developing in brand new markets 窶・weather, quantitative investing for the global markets, power, emissions, insurance, etc.

窶廣lpha generation through innovation窶・should be the ongoing mantra for all alternative investment businesses. In our view, such innovation can be very rewarding when modern scientific knowledge is applied to solving complex non-linear problems that exist in the world of finance.

A new paradigm is waiting to be developed. Such a paradigm must address the need for optimal asset allocation, continuous creation, rotation and updating of diverse investment strategies. Only five years ago, this would not be possible. In recent years, many limitations of technology and computational capabilities have been lifted which opens up an entire new range of opportunities for generating the alternative, algorithmic, alpha. In contrast with more popular rule-based market neutral or trend strategies, such algorithmic alpha would be a product of the truly innovative, repeatable, uncorrelated and dynamically adaptable investment process.

Horton Point conducts research, analysis, and management of next generation quantitative investing for the global markets investment strategies combined with the thoughts, ideas and advancements from other areas of human progress. Through this forward-thinking and reflective model, the firm aims to capture a new generation of investment returns, which it calls Algorithmic Alpha.

This article first appeared in www.oldyorkcellars.com on 27 November

Quantitative investment strategies have evolved into complex tools with the advent of quantitative investing for the global markets computers but the strategies' roots go back over 80 years. They are typically run by highly educated teams and use proprietary models to increase their ability to beat the market. There are even off-the-shelf programs that are plug-and-play for those seeking simplicity, quantitative investing for the global markets. Quant models always work well when backtested, but their actual applications and success rate are debatable. While they seem to work well in bull markets, quantitative investing for the global markets, when markets go haywire, quant strategies are subjected to the same risks as any other strategy.

The History

One of the founding fathers of the study of quantitative theory applied to finance was Robert Merton. You can only imagine how difficult and time-consuming the process was before the use of computers. Other theories in finance also evolved from some of the first quantitative studies, including the basis of portfolio diversification based on modern portfolio theory.

The use of both quantitative finance and calculus led to many other common tools, including one of the most famous, the Black-Scholes quantitative investing for the global markets pricing formula, which not only helps investors price options and develop strategies but helps keep the markets in check with liquidity, quantitative investing for the global markets.

When applied directly to portfolio management, the goal is like any other investment strategy: to add value, alpha, or excess returns, quantitative investing for the global markets. Quants, as the developers are called, compose complex mathematical models to detect investment opportunities. There are as many models out there as quants who develop them, and all claim to be the best. One of the best-selling points quantitative investing for the global markets a quant investment strategy is that the model, and ultimately the computer, makes the actual buy/sell decision, not a human. This tends to remove any emotional response that a person may experience when buying or selling investments.

Quant strategies are now accepted in the investment community and run by mutual funds, hedge funds, and institutional investors. They typically go by the name alpha generators or alpha gens.

Behind the Curtain of Quant Strategies

Just like in "The Wizard of Oz," someone is behind the curtain driving the process. As with any model, it's only as sebastian cichowski energa invest as quantitative investing for the global markets human who develops the program. While there is no specific requirement for becoming a quant, most firms running quant models combine the skills of investment analysts, statisticians, and the programmers who code the process into the computers. Due to the complex nature of the mathematical and statistical models, it's common to see credentials like graduate degrees and doctorates in finance, economics, math, and engineering.

Historically, these team members worked in the back office, but as quant models became more commonplace, they moved to the front office.

Advantages of Quant Strategies

While the overall success rate is debatable, the reason some quant strategies work is that they are based on discipline. If the model is right, the discipline keeps the strategy working with lightning-speed computers to exploit inefficiencies in the markets based on quantitative data. The models themselves can be based on as little as a few ratios like P/E, debt-to-equity, and earnings growth, or use thousands of inputs working together at the same time.

Successful strategies can pick up on trends in their early stages as the computers constantly run scenarios to locate inefficiencies before others do. The models are capable of analyzing a large group of investments simultaneously, quantitative investing for the global markets, where the traditional analyst may be looking at only a few at a time. The screening process can rate the universe by grade levels like or A-F, depending on the model. This makes the actual trading process very straightforward by investing in the highly-rated investments and selling the low-rated ones.

Quant models also open up variations of strategies like long, short, and long/short. Successful quant funds keep a keen eye on risk control due to the nature of their models. Most strategies start with a universe or benchmark and use sector and industry weightings in their models. This allows the funds to control the diversification to a certain extent without compromising the model itself. Quant funds typically run on a lower cost basis because they don't need as many traditional analysts and portfolio managers to run them.

Disadvantages of Quant Strategies

There are reasons why so many investors do not fully embrace the concept of letting a black box run their investments. For all the successful quant funds out there, just as many seem to be unsuccessful. Unfortunately, for the quants' reputation, when they fail, they fail big time.

Long-Term Capital Management (LTCM) was one of the most famous quant hedge funds, as it was run by some of the most quantitative investing for the global markets academic leaders and two Nobel Memorial Prize-winning economists, Myron S. Scholes and Robert C. Merton. During the s, their team generated above-average returns and attracted capital from all types of investors. They were famous for not only exploiting inefficiencies but using easy access to capital to create enormous leveraged bets on market directions.

The disciplined nature of their strategy actually created the weakness that led to their collapse. Long-Term Capital Management was liquidated and dissolved in early Its models did not include the possibility that the Russian government could default on some of its own debt. This one event triggered events, and a chain reaction magnified by leverage created havoc. LTCM was so heavily stock investing companies with other investment operations that its collapse affected the world markets, triggering dramatic events.

In the long run, the Why should i invest in xrp Reserve stepped in to help, and other banks and investment funds supported LTCM to prevent any further damage. This is one of the reasons quant funds can fail, as they are based on historical events that may not include future events.

While a strong quant team will be constantly adding new aspects to the models to predict future events, it's impossible to predict electrician or plumber make more money future every time. Quant funds can also become overwhelmed when the economy and markets are experiencing greater-than-average volatility. The buy and sell signals can come so quickly that high turnover can create high commissions and taxable events. Quant funds can also pose a danger when they are marketed as bear-proof or are based on short strategies. Predicting downturns using derivatives and combining leverage can be dangerous. One wrong turn can lead to implosions, which often make the news.

The Bottom Line

Quantitative investment strategies have evolved from back-office black boxes to mainstream investment tools. They are designed to utilize the best minds in the business and the fastest computers to both exploit inefficiencies and use leverage to make market bets. They can be very successful if the models have included all the right inputs and are nimble enough to predict abnormal market events.

On the flip side, while quant funds are rigorously back-tested until they work, their weakness is that they rely on historical data for their success. While quant-style investing has its place in the market, it's important to be aware of its shortcomings and risks. To be consistent with diversification strategies, it's a good idea to treat quant strategies as an investing style and combine it with traditional strategies to achieve proper diversification.