Perhaps some other future Nobel laureate would have made the necessary connections to draw on Markowitz’s work and distill it into some approximation of the Capital Asset Pricing Model. Maybe another person, equally pragmatic in recognising their own unknowns as far as calculus was concerned, would have made the necessary intellectual insight to utilise binomial trees to frame an approach to pricing options in response to the Black-Scholes-Merton model. It is possible too that someone else would have bequeathed upon the investment community a way of arriving at a single figure with which they could communicate with absolute conviction that their fund was well worth funding. Qui sas, qui sas, qui sas.
The fact of the matter is that Bill Sharpe did do all of the above. He would be the first to disagree with any kind of personality-driven interpretation of historical development, but it is undeniable that this industry owes Sharpe a considerable debt of gratitude for providing some of the controversial cornerstones that shore up modern financial markets. Clarification, concentration, and simplification make for viable enterprise. There is more than a pun at play in this article’s headline. Now, consider whether that ‘thank you’ for your livelihood and plenty of heated debate in the bar after work should be fashionably grudging at all.
If we can go back to the beginning, you originally started out with the aim of becoming a medical doctor. What changed that ambition, and what drove that in the first place?
The second one is easy. What drove it was that my mother wanted me to be a doctor. I started out at UC-Berkeley, as a pre-med in my freshman year, and took chemistry and physics as I recall. I didn’t particularly like either one, although I took them in high school—chemistry at least. I also had this revelation that I didn’t care for the sight of blood. So it seemed to me that those were portents!
At the end of my freshman year, I decided to change – both to go to UCLA and to go do a business major. In my first semester I took, among other things, accounting and economics; I knew nothing about economics and very little about accounting. The accounting course was basically bookkeeping. I absolutely loathed it. As for economics, I just found it was beautiful. It was poetic. I loved the rigour and the formulas and such, so I thought, “Well, gee, I love this stuff.” I decided to become an economics major – not being at all clear how I could earn a living in it – graduated, and went looking for jobs in banking. The interviewers kept looking at my transcript, which was rather good, and kept saying, “Don’t you think you should go to graduate school instead of being a business person?”
I would physically flip the page over for them and say things like “I was head of the sailing team” and try to explain to them that I had done other things and I was actually a human being who might make it in business! They all advised me to go to graduate school, and I did. I took a master’s degree, then I went into the services – I’d been in the Reserve Officers’ Training Corps – and then worked for the RAND Corporation. I decided that, well, I really wanted to teach, so I took one of the four courses I would have had to take in education to get a junior college credential. I thought that was really moronic and decided to switch to a PhD, which, even when I was working full time, was a better investment.
Looking back now, do you think there was anything specific in your own personal history that made economics resonate with you? Was it the time – was there anything specific at that point in time that made this appeal to you far more than the other options that you had?
In high school, I liked maths and things that were tidy and rigorous. I just thought the combination of logic with a little mathematics (it was not highly mathematical, of course, in that era), say with demand and supply curves – the idea that you could make assumptions about individual behaviour and individual firm behaviour, and then play social scientist and see what that implies for things like prices and such, it was microeconomics.
Macroeconomics, at that point, I thought was sort of like black magic. I thought microeconomics was simple with plausible assumptions. From that, one made inferences that were, in many cases, surprising, and, in some cases, counterintuitive, and it flowed beautifully from simple assumptions to strong interesting conclusions about the real world. The relevance and rigour both, the use of logic — it sort of had it all.
In particular, in my background, my parents were both in education. My mother was an elementary school teacher and principal. My father was president of Golden Gate College, now University. Both of them were in education, but neither of them was in anything like economics.
The social implications of economics, the fact that it has import for society as a whole, was that something that appealed to you?
Not in any sort of socio-political sense. I suppose it could have been something about what happens in cultures of amoeba or something! I have obviously got an interest in society as such, but it wasn’t really a socio-political or sociological aspect. It was really just the idea that starting with the principles of behaviour of individual entities, you could find out something about what happens to aggregate quantities.
Let’s move on to the RAND Corporation in the 1950s. What was the environment like there at that point? How did it affect you? Did it affect the way you later worked toward the goals you set for yourself?
First of all, it was a fantastic environment – really smart people, very much an atmosphere of intellectual inquiry. That was sort of the governing aspect. We were working on different kinds of problems; I was in the logistics group. We were doing things that one would call operations research, but there was no aggregation aspect.
On the other hand, we were encouraged to spend one day out of every five on anything we wanted. So we’d have long discussions in people’s offices about, say, a smog tax. I did the paperwork with a couple of colleagues on the proposal for a smog tax for the LA Basin back before people were talking about such things. It was a marvellous place.
Those were the early days of operations research, when we thought quantitative methods roughly construed would solve all problems. It wasn’t the nirvana that we thought it would be, but we certainly, I think, did a lot of good for the Air Force, for public policy in number of fields. I worked on the economic aspects connected to computers. I ended up writing a book on that subject, totally supported by the RAND Corporation. We did a study for the New York airports on congestion pricing, which never made it to the airplanes, although they’re now talking about it for cars here. It was certainly a great formative experience in my career.
Was that your first direct experience in working with computers? When did that love affair begin for you?
As soon as I got my hands on one. Yes, it was my first experience; I don’t think I did anything with computers in my master’s program. We had what I think were the world’s first timeshare computers, called JOSS, which was the JOHNNIAC Open Shop System, which was programmed on our JOHNNIAC machine designed by John von Neumann and used Teletype terminals. I also learned FORTRAN—I believe it was FORTRAN 1! Maybe it was FORTRAN 2. I became a really good keypunch operator and figured if things went bad in the economics business, I could always earn a living as a keypunch operator. I used an IBM 704 – I think that was the machine I was programming on originally. I ended up learning just enough to write an interpretive compiler for BASIC when I was at the University of Washington, so that’s where that started.
It was at RAND that you met with Harry Markowitz for the first time, am I correct?
I started a dissertation on transcript pricing, building off the work of Jack Treynor, and had a 50- or 60-page internal RAND paper on it. Jack came to UCLA, and Armen Alchian, my mentor, says, “Go seek out Jack – he can be your chair.” Jack didn’t think it was a dissertation, so I went to Fred Weston, with whom I worked at UCLA. Fred would have each of the students study a body of work and present it to the seminar; either he assigned me Markowitz or I looked at Markowitz and thought he was interesting. I was familiar with his work before he came to RAND.
Fred remembered that I presented Harry’s work at a seminar, and really liked it. “Markowitz has just come to RAND,” he said. “You can find him and introduce yourself.” So that’s how that started.
Can you encapsulate the influence Harry Markowitz had on you as a result?
As an undergraduate, I had taken an investment course from a wonderful man, but I found it very frustrating because it was a very old-fashioned investment course. You looked at the earnings, looked at the accounting statements, and the PE ratio. It just lacked rigour, and any sort of an underlying theory. So when I found Harry’s work, where he proceeded from first principles, it was rigorous and I thought it had all the attributes that attracted me to microeconomics. It didn’t of course have the aggregation. It said, “Here’s what you should do if you want to build a portfolio that is better than other portfolios you can build,” and so I went the next step. In my dissertation, I did a few things, the last of which was to ask: What happens to your aggregate? What do you get in equilibrium? Again, it had a profound influence. If not, I would not have gone the direction I went, had it not been for Harry’s work obviously. Nor would half of the financial economists of the era.
When you first read about the efficient frontier, how did you react to that concept initially?
Well it’s simple but not obvious, and powerful. I also loved the critical line algorithms. It was wonderful, both as an application of a quadratic program and in terms of Harry’s own algorithms, which had marvellous properties. I was an algorithms guy. I’d been exposed to Phil Wolfe’s approach to quadratic programming, as had Harry. Phil was at RAND and I’d been doing linear programming there.
I was part of a big project that used linear programming, which I used in my stillborn dissertation in transcript pricing. It was all couched in linear programming terms, shadow prices, etc., so it all fit very nicely. Again, I can’t say which influenced which, because I was exposed to all of it. Linear programming at RAND, dynamic programming, Feldman was there, Harry’s work I read, been exposed to Fred Weston, and as I say Phil Wolfe and his quadratic algorithms, all that was swirling around.
Given that all that was swirling around, was the Capital Asset Pricing Model an inevitability?
Yes. What microeconomists do is they assume, perhaps dramatically, that individuals maximise something – profit, company, utility – and then they say, what will happen if you put a bunch of these maximising agents together in the market economy? What can you say about resultant pricing and, in this case, risk and return? That was sort of the obvious next thing to do, so I did it. And as it turned out, other people did it too.
At that point did you have an inkling as to how influential this was going to be?
Kind of yes and no. I did the dissertation, so it was out there for people who knew about it and order it from the library. It was filed in June 1961. Then I went to the University of Washington. I presented at the University of Chicago. I tried to generalise it. I submitted it in early 1962, and it eventually got published in 1964 after many rejections. And after it did get published, I thought, “Ha ha, my phone is going to ring,” but my phone didn’t ring. I thought, “I don’t know how good it is, but I bet you it’s the best thing I’ll ever do!” I thought I had peaked. I thought it would at least cause people to be interested, and the phone rang, but not often. Partly, I didn’t understand how long it took people to read journals. I was initially disappointed that it didn’t cause more of a stir, but then it started to get more attention.
One of our contributors is Ed Thorp, and he has often said that during that period, studying finance was seen as a poor cousin to economics, which was looked on with a certain amount of disdain from some of the great economists of the day. Can you add your perspective on that?
If anything, it’s an understatement. I went to a business school to teach finance. Three of us were hired at University of Washington in finance that year. We obviously spent time together because we were in the same class, as it were. I was absolutely appalled. One person came from Indiana, the other, I think, Texas; they were strictly traditional, only talking about price earnings ratio, accounting things – all the stuff that I hated!
They, of course, thought I was totally uneducated because I didn’t know any of that stuff, so I felt very much out of place – although there were some other people who were learning about the new things, and would do some really good work, like Steve Archer and Chuck D’Ambrosio in particular. I felt underprivileged. I begged and pleaded with the economics department to let me teach some introductory courses, which they did, so I could get some respectability. Slowly but surely, things came our way and people started talking about the field called financial economics, which of course nobody had even used as a term beforehand. So I felt the real intellectuals were over in the economics department, and I sat in on seminars and tried to get a little legitimacy.
Speaking of the time at Irvine, I read in your biography for the Nobel committee that you had gone to the University of California, Irvine in 1968 to help create a School of Social Sciences with an interdisciplinary crossover and a more quantitative approach to things. Now, what can you say about that period of time? Was that something that was a little ahead of its time, because you refer to it as an experiment?
There was a lot going on. I went there for two reasons. Jim March was there. I have and had huge respect for Jim. He was really a pioneer, a great innovator. Also, one of my friends from undergraduate school, Marty Shapiro, was there in political science. I had great respect for him. It just seemed like a wonderful idea. What happened was complicated, but Jim had hired a bunch of junior fellows (Harvard was sort of the classic prescription). They had sort of created or he had encouraged an ethic, interdisciplinary über alles which evolved to the point that “interdisciplinary is good, journals are disciplinary, and therefore publishing in a journal is bad because you can’t be interdisciplinary if a journal will accept it.” So you have these brilliant young people who were doing very creative stuff but were not exposing themselves to the test of peer review.
There came a point when they’d been there six years and had to be either given tenure or let out under the UC standard rules. The net result was they weren’t given tenure and there was a great upheaval. Those of us who were still pretty much in disciplines and believed in doing research that could meet peer review were kind of on the outside. Ultimately, we were the only ones standing. So at pretty much the same time I gave up, Jim gave up. Marty went somewhere else as well. Ed had been over at the Maths department at that point. Sheen Kassouf was in the school; he was very much in financial economics, he wasn’t as engaged in the school, and he was doing his business things to some extent, and working with Ed.
So there wasn’t a whole lot in the graduate school administration; they hadn’t yet built the finance school that they did ultimately. That was before Ed went over to the GSA. I taught a large economics course and I taught seminars – economics of computers and financial economics and God knows what – and the only PhD students I had I brought with me. And it just didn’t turn out to be a very good setup for me at that time.
Moving on to the 1970s and 1980s, when did you first become aware of the Black-Scholes-Merton model for options pricing and how did you react to it at that point?
I think I became aware of options pricing because of Paul Cootner’s work because I was hanging out with them, reading their work. That’s the first I saw of it. I probably saw that before I saw Black-Scholes. When I was in Stanford, I gave my paper at a Carnegie seminar. Myron was there. That might have been the first time I was aware of it. I probably met Bob and Fischer and Myron, or maybe afterwards at the Wells conference at Rochester. I got into that circle. So I’m sure I must have seen it when it was circulating before it was finally published; it probably circulated for two or three years I think.
Also, I was an associate editor of the Bell Journal of Economics and Management Sciences, and Bob had an article there, which I must have seen when he submitted it. I don’t know which I saw first. I certainly was aware of the work in that area. But I couldn’t understand the continuous time stuff.
Why don’t you think you could understand it? Why didn’t it sit well with you?
Because of Sputnik at UCLA for a PhD, the university requirement had been changed from two languages to one language in maths if you want it. And the maths was calculus. So I took my only college maths course, calculus, which was what I took in junior college (when I was working at RAND). I thought I could pass that test, so my formal post–high school mathematics education consisted of one junior college course in calculus. And I also took one other course. It wasn’t really statistics, but logic. It was a simple course given in economics to give us just enough mathematics so we could read some of the literature of that era.
You set about applying a discrete time approach to the problem
Mark Rubinstein likes to tell this story. It started, actually, when a bunch of us were sitting by the Dead Sea at a conference in Israel. We were talking about option pricing. Of course, we had papers and pencils with us. I said, here’s a way it seems to me that you can think about it. I did a little binomial, and I started doing calculations and, son of a gun, this thing converged with Black-Scholes, very rapidly, and showed it to John Cox, blah blah blah, and it was sort of a historical event.
At that point, keeping in mind the computational power that was available, was the binomial approach something that really needed an increase in processing power in order to make it viable for the markets to utilise?
You get into the fundamental view. Is the world discrete and we approximate it with continuous functions, or is the world continuous and we approximate it with tree functions? You can have that debate.
When I first went to the business school, I met with the dean and said we should have our own timeshare computers. At that point, Hewlett-Packard had just introduced a new machine. The dean said, “What should we have?” so I suggested the HP model. He said he could get one of those, or he’d pay for it. I said, “Great, but who is going to run it?” And he said, “You.”
I installed the machine and added the user group – you know, academic users – for that particular machine. It was a pretty good system; you could do some serious work. I remember for my articles I had to program a pen plotter, and used it with fundamental program instructions in order to get decent graphs. It was early times, but of course we had the Central 360 systems for bigger computations. I viewed it more as a way to understand things.
But then of course it had the advantage that you can evaluate options with all sorts of bizarre characteristics, whereas the option pricing formulae weren’t able to do a lot of things, and it could do anything. All it took was a little more time if you want to make it more precise. (Although it might be a lot more time!)
Did you have any sense how “quantitative” finance was going to become in the following decade?
No. If I had, I might have applied for a transfer to another field!
I must admit – partly because it’s self-serving – it bothers me that a lot of the economics understanding sometimes is missing. One of my pet peeves – I have this debate periodically – I don’t like the term “risk neutral expected value,” the terms people use for the pricing kernel. They’re just forward prices for God’s sake! If you understand that, you understand it’s not probability. People aren’t risk neutral. It’s forward prices. It’s a lot simpler to think about pricing financial instruments with forward prices, and that makes very clear just what the assumptions are that are allowing you to do that – the strengths and limitations of those assumptions. There are people who come to financial engineering from the mathematics and physics side who really don’t have the understanding of what basic economics are and the strengths and limitations that go with that. But again much of that is self-serving, because I don’t have the luxury of being with the advanced maths. You have to have a certain amount of economics intuition and foundation. A lot of people have both, and they are better judges of what’s good and what’s bad.
What are your views on research on behavioural finance?
I’m a huge fan. I’m a fan of behavioural finance or behavioural economics or, even more fundamentally, cognitive psychology. There’s a guy in the Oregon Research Institute up in Eugene. He’s one of the early pioneers. He started doing experiments with investment advisors. I loved his work but can’t remember his name! Of course there’s Danny Kahneman, Amos Tversky – I had Amos talk at a couple of conferences I ran. I’m a great admirer of that. I actually was presenting some work, called the distribution builder, with Eric Johnson and Dan Goldstein in Stockholm in 2001.
Dick Thaler was in the audience. I said, “I think of this as behavioural finance, but I don’t know if it is.” Thaler said, “Yeah, I’d call it behavioural finance,” so I’ve been anointed by Thaler as a behavioural finance person.
I’m working with a master’s student at the naval postgraduate school here. He’s a student in operations research. I’m working with him on some experiments as we speak, to try to infer something about people’s multiperiod utility functions. As a matter of fact, I just discovered, reading Dan’s blog on your site, he was summarising some results from Hastie and Dawes, which I should know about, but I don’t. I’ll probably order it!
I’m not of the school of “let’s design an experiment and show how idiotic people are when they make choices in uncertainty.” I’m more of the school that you need to help people understand the trade-offs so they make decisions that are more informed. And we really help them understand and frame things sensibly, so they may behave more in line with some of the assumptions of expected utility maximisation, but with more complex functions than economists traditionally used.
My current work on post-retirement economics – we’ve got to very much have better assumptions about people’s utility in general and particular for Joe Blow, and find a way to make it sensible information about Joe Blow’s preferences, so we can design post-retirement financial plans for Joe Blow. So that’s a hugely important area. I’m a big fan of the FRMI work that people are doing now. I don’t subscribe to the nihilistic view that some researchers take: “Aw, people are stupid and let’s just show how stupid they are!”
At Financial Engines, we know how framing will affect people’s choices. We don’t know in great detail but we’ve now worked over a decade with people, trying to frame trade-offs so the framing does not lead people to the conclusions. Hopefully, we help them to understand the trade-off and not anchor them in something that is not best for that particular person. We’re very cognisant of this and we factor that into the way we work with people.
Let’s talk a little bit about the development of what has come to be known as the Sharpe ratio. In the early 1990s, you revised your original work to acknowledge that the risk-free rate changes over time. How long had you been planning on introducing that revision? How did it come about?
I think of that as a minor thing. The original idea was somebody comes to you and says, “I want one number to measure the desirability of an investment.” I’ve certainly been dedicated to the notion that there’s risk and return. I was resolutely, in the early days, in the mean variance frame of mind, so I said in the mean variance world, you have to summarise, and again I originally addressed this from a prospective is one period to date forecast. I said, well, if you remember Markowitz says it’s an efficient frontier and it’s curved, and Tobin says if there’s a risk-free rate, you can borrow and lend. So you find the tangent point away from the risk-free rate, and that’s the optimal combination of risky securities, and you just lever up and down. You have to choose between either A or B, when A is a point in mean variance space and B is a point in mean variance space. You can borrow and lend. Assume you have to choose A or B where you don’t get to combine them and there’s only one period, and these are forward-looking estimates. You reframe the question, you chose A plus borrowing and lending, or B plus borrowing and lending and if that’s the choice you are facing, then you choose the one with the higher ratio of expected excess return to standard deviation. That’s what I called the reward to variability ratio. So, I said here’s a measure, if you want to combine risk and return in one measure, at least formally in that setting, prospective one period, with mutually exclusive choices, here’s the measure. So I suggested that (while it would involve about five million untenable assumptions), if you are determined to use one number as a performance measure, you might use that. And at that point and since the derivation was in one period setting, there was no issue about the risk-free rate changing over time. It just was.
You are ahead of me. I don’t remember if the original paper even addresses the issue of what you do with a time series of results, whether you use the risk-free rate each period or you use an average of the risk-free rate. Maybe I used the latter. But at least when I did the JPM article, by that time it was certainly clear to me, maybe clear to many other people, that if you are using history, probably the excess return over the changing risk-free rate, these probably are a more stable series and a better predictive series of the forward-looking excess return distribution than doing it any other way.
At some point, I, and other practitioners probably, must have concluded it made more sense, but I know for years, I prescribed that you take the risk-free rate each period, you take the return on the asset each period, you take the difference, you take the mean and standard deviation of that difference series. But the problem is, of course, the people forgot all the assumptions that went into that.
My sort of philosophical point is if you are determined to summarise things with one number, it’s better at least to take risk into account somehow than just return. This is not the world’s worst way. But of course if you are just evaluating a component of what will be a portfolio of components, obviously you need to do something else. I said on many occasions, we have computers now – you don’t have to evaluate using one number and then rank them. That’s not the best way to use information at hand.
Have you ever encountered any representatives of the finance industry who are so devoted to the idea of single-number distillations and have enshrined the Sharpe ratio as a lynchpin of their career and industry?
First I ran into lots of people who are vending products, who loved to trumpet their Sharpe ratio, and they are the people who have been given good Sharpe ratios by their historic performance, vis à vis the S&P 500. You got a lot of that. Obviously, you’ve got all the mean variance issues. You’ve got all the peso problems. I spend more effort disabusing people of the notion that the Sharpe ratio is an adequate measure than they have touting it, leading to headlines like “Sharpe Says Sharpe Ratio Is Not Appropriate!” A little extreme!
If you are running a hedge fund, you are taking a lot of tail risk. Especially if the risk hasn’t manifested itself, you are going to trumpet it to the skies. As far as academics, practitioners, and performance measurers who post the Sharpe ratio, I think more people tend to come up with some measure of their own and trumpet it as a better single measure than the Sharpe ratio. But many of those turn out to be highly correlated with the Sharpe ratio, so they are not giving you a lot of new information. And there are people who tried to take the constraint of using one number and get more into it, working off positive utility functions that are more complex than quadratic, which is basically the one way of getting the Sharpe ratio.
Let’s talk about the Nobel. How did you feel about being honoured with that award? What was your initial reaction? Has it affected things in any way?
Certainly, I feel differently than Taleb does! I bought his book and read the first part of it, which is quirky to say the least. I really like the first book; the second book is a little quirky. Then I made the mistake of looking myself up in the index and reading about all he said about all of us—Black, Scholes, Markowitz, Merton, and myself. It’s pretty brutal. So I don’t feel that way. I feel financial economics absolutely can hold its own with the rest of economics as an intellectual scientific discipline. It’s great that the Nobel committee felt that way and continues to feel that way. It’s a great honour for the field.
If you talk to the people involved in the selection process in any of the Nobel Prizes, they’ll tell you that people like to personalise science to get the general public really interested in science.
It’s very difficult to say “A was the one; we wouldn’t have this without A.” That’s not how the world works. There’s always an issue of which personality you use to bring public attention to a part of scientific endeavour or accomplishment.
I think financial economics in general and the work that has been honoured, whether it be Danny Kahneman or any of these economists in or near the financial side, has been great. Personally, did it change my life? Sure! There are many requests for talks, especially requests on talks about anything you want to talk about. I can probably charge more for the very rare expert witness work that I do, and I’m asked to do so more often. I’d be foolish to assume a number of aspects of my life would have been the same without it. I always say, if they offer you one, take it.
Have you got any candidates in mind to receive the award?
I don’t know anywhere near enough about economics, broadly construed – and it’s now very broadly construed – to have any notion about who would be the most deserving in labour economics, economics history, or industrial economics or any other field of economics. I don’t even know who within a subfield would be more appropriate, or if the best industrial economist is a whole lot better than the best labour economist.
I can’t really answer the question. I could probably put some money down within financial economics as to who would be more likely or less likely. I also have some of my own favourites within financial economics. Those are not necessarily the same names. I definitely don’t want to say who my favourites are. There are people I think are richly deserving.
As you may know, we are asked to nominate people along with 900 other people. I tend to nominate the same people until they get awarded. Then I switch to nominating somebody else. And, of course, I’m not that familiar with the work of some of the younger people.
Can we talk a little bit about your view on current market conditions?
That’s easy, because my default assumption is that prices are as good an indicator, as good an estimate of the present value of highly uncertain future prospects, as I at least know how to get. I always assume that whatever the prices are, that’s probably the best estimate I can come up with. So I’m very boring.
People ask, “What should I do now?” I’ll just say, “Just hold the market portfolio then tilt it based on the differences between you and the average investor in the world.” I give the same answer all the time whether the market is up or down.
Let’s talk about Financial Engines. What was the motivation for you to get involved and set things up?
To give you a little prehistory, I had been working both academically and in a little organisation that I set up on the problems of the Defined Benefit Plan Sponsor, and then went back to Stanford in the early 1990s. Right or wrong, we were shifting from a DB to a DC culture. I had believed it was because of demographics and risk sharing, not because of a particular piece of legislation. So, it seemed to me a wildly uncharted territory was what helped individuals make these decisions. It was a very rich area for academic research, so I shifted my research agenda to that area. I was doing academic research, putting things on the Web and such, building little decision tools and teaching courses, writing a book, and all the rest of that.
My colleague in law school, Joe Grundvest, former SEC commissioner, said over the course of one too many meetings for coffee, ”You need to accelerate this. We need to get a company going.” I said, “I’ve been there and done that. There are many things I’m bad at – one of the things I have empirical evidence on is I’m bad at running a company!” Joe says no, no, no, and he engineered a meeting with Craig Johnson, sort of a venture capital/law guy, who helped found a bunch of companies basically based on academic ideas. So we had this notion of setting up a company dedicated to helping employees in 401(k) plans and similar instruments make sensible investment decisions for their retirement. So that’s basically what we started with and that was the goal and that’s what we do.
The only major change over the years is that we found there was a substantial number of employees who didn’t want to even interact with the wonderful engine that make it really simple and understandable. They just want somebody else to do most of the work. Our standard arrangement now is we go to the employers and ask them to sign up for our services. What they get it is three things; first, we wire up every employee, so every employee can go online, interact, try different strategies, and get our advice and recommendations. They can see what are the chances that they will have a particular standard of living if they keep doing what they’re doing as opposed to if they follow our advice or maybe try something they want to play with.
Everybody gets that – the employees don’t pay, the employer pays. We don’t charge very much for that. We brought that cost way down. Everybody typically gets a personalised version of that on paper once a year that is specific to them. The third leg is those who chose to could have us manage their 401(k) accounts for them, in an individualised way. For that, we charge basis points. And so that’s the suite, and our preference is that the employer signs up for all three legs of that. That way, we can reach everybody and help everybody in ways that are best for each particular person.
So that’s what we do, and we’re still doing what we originally did. Of course, in the early days, that was when the world was going to be paperless and we were the vanguard of the Web that changes everything. Probably if you told us that we’d be doing the other two pieces, we’d have responded, “Everything will be online – we’re out there at the cutting or bleeding edge.”
Would you think there’s a behavioural observation that you can make from that development? How would you summarise it in terms of the kinds of approaches that people take to the idea of risk and handling it in their lives in relation to their financial prospects?
We started with the premise that one size does not fit all; that people are different, based partly on our understanding of behavioural literature at that time. We started with the notion of individualised solutions or strategies, what have you. What we found out is we were really right. People are very different. Even people working for the same employers are different – even people the same age working for the same employer are different. Basically, the evolution to three different delivery mechanisms or procedures is just part of adapting ever more so to the difference we found.
We analyse people’s choices when they make their choices. We preserve all the confidentiality. We do a lot of analysis of what our participants are doing, how they’re reacting to information, et cetera. I think we’re getting a pretty informed view. One of the things that leaks out is it’s really important to offer people individualised solutions because they want them. It’s very important to frame and communicate. We work really hard on exactly how we present – say, probably you will have a retirement better than x, where does x come from, how do you present that. We use weather icons and thunder clouds and rain; we do a lot of things. Over the course of time, we’ve evolved all of the interaction aspects. In the presentation, do you use blue or red, this size or that size, and it comes down to those very tiny decisions that can have an impact. So we’re kind of empirical and pragmatic behaviourists in many ways.
Ed Thorp asks (via email) whether you are aware, are you aware of any market inefficiencies in publicly traded markets that are exploitable, without inside information, by investors, and how much alpha can be extracted?
Say “hi” to Ed for me. It’s been too long! I’m tempted to say, “Yes, but if I tell you I’ll have to kill you.” The real answer is no, I’m not. When I see something that appears to suggest something of that sort, my predilection is to say, well, what’s an efficient market possible. Can I make up some sort of efficient market story? It’s a liquidity premium or this or that. I play the usual game of if it were that, how can one arbitrage that away or at least exploit it?
I don’t look for such things. I never have. I generally have a philosophical, sceptical view of such things. I know some really smart people who do that for a living, and some of them have done quite well.
I figure that somebody’s got to win, got to be better than average. Some people have got to be even better than average to get probability distributions that are net of cost and that are dominant in some sense, but I would be lost to try to guess who, ex-ante, is in that very small camp. So that’s not what I do personally or for a living.
Ed was asking if you would care to comment on Warren Buffett and Berkshire Hathaway, Renaissance with James Simons, and Citadel with Ken Griffin. Would they represent that group that you just referred to?
I’ll tell you the Warren Buffett story – the others I don’t know. I work with a family office. We’ve been doing hedge funds for a long time; we certainly have invested in a number of those, and over the course of time, for one reason or another, we’ve done pretty well. I’m not deaf on active management – far from it.
In the early days, sometime in the 1970s, at Stanford, our dean, RJ Miller, had an advisory board, which included various prominent people, who came every six months or 12 months to spend the day, learn about the school, and make suggestions – and I suppose give money, probably.
RJ asked me to give a talk about my work – efficient markets, the whole nine yards – to the advisory board. So I do so. When I finish, it’s time for questions, and Arjay says, “That’s all very interesting, but how do you explain Warren Buffett up here,” and Warren is sitting in the audience. I said at that time that Warren is a three-sigma event. I’ve been told over the years that he retold the story and at least somebody said it had grown from three- to four- to five-sigma events!
Buffett is not just checking PE ratio and running an AI algorithm or a heuristic of some sort, processing mounds of data on a PC. I think of him more as a corporate finance guy than an investment guy. He actually can lose money sometimes, probably not for very long or overall, but he’s a very smart guy and very creative.
There are some brilliant people. Problem is, if you take the cap-weighted average of people in the financial industry, you get a really smart, hardworking person, who also is really informed and has a lot of computational power.
You’ve got to have an image of who the average is, and, unfortunately, it’s cap-weighted and not equal weighted as to what affects prices, so it’s a hard game to beat consistently.
BIOGRAPHY OF WILLIAM F. SHARPE
After he earned his PhD in 1961 with a thesis on a single-factor model of security prices, which also included an early version of the Security Market Line, William F Sharpe started teaching at the University of Washington. He started research on generalising the results in his dissertation to an equilibrium theory of asset pricing, which produced the Capital Asset Pricing Model. Although the paper was submitted in 1962 to the Journal of Finance, it was initially not well accepted and was rejected from publication. The paper, which became one of the foundations of financial economics, was finally published in 1964, after a change in editorial staff.
Later in 1966, Sharpe developed what came to be known as the Sharpe ratio, which is a measure of the excess return (or risk premium) per unit of risk in an investment asset or a trading strategy. Sharpe had originally named it the “reward-to-variability” ratio; “Sharpe ratio” was a term coined later by academics and financial practitioners. The (original) Sharpe ratio has often been challenged with regard to its appropriateness as a fund performance measure during evaluation periods of declining markets. Sharpe revised it in 1994, allowing for the fact that the risk-free rate changes with time.
In 1968, Sharpe moved to the University of California, Irvine to help create a School of Social Sciences with an interdisciplinary and quantitative focus, which he calls an “experiment.” This experience was short-lived, as he moved again in 1970 to Stanford University’s Graduate School of Business.
Between 1968 and 1970, Sharpe also authored a book, Portfolio Theory and Capital Markets, summarising both normative and positive work in these areas.
While teaching at Stanford, Sharpe continued his research in the field of investment, in particular on portfolio allocation and pension funds. He became directly involved in the investment process by offering consultancy services to Merrill Lynch and to Wells Fargo.
In 1986, in collaboration with the Frank Russell Company, he founded Sharpe-Russell Research, a firm specialising in providing research and consultancy on asset allocation to pension funds and foundations. In 1989, Sharpe retired from teaching but retained the position of Professor Emeritus of Finance at Stanford, choosing to focus on his consulting firm, now named William F Sharpe Associates.
Besides the CAPM and the Sharpe ratio, Sharpe also developed the binomial method for the valuation of options, the gradient method for asset allocation optimisation, and returns-based style analysis for evaluating the style and performance of investment funds.He has published articles in several professional journals, including Management Science, the Journal of Business, the Journal of Finance, the Journal of Financial Economics, the Journal of Financial and Quantitative Analysis, the Journal of Portfolio Management, and the Financial Analysts’ Journal.
He has authored and co-authored a total of seven books in his career, which includes Asset Allocation Tools, Fundamentals of Investments (with Gordon J. Alexander and Jeffrey Bailey), Investments (with Gordon J. Alexander and Jeffrey Bailey), and Investors and Markets: Portfolio Choices, Asset Prices and Investment Advice.
In 1990, Sharpe was awarded the Nobel Sveriges Riksbank Prize in Economic Sciences.
The list of positions and honors that span Sharpe’s career is suitably lengthy, but in a nutshell, Sharpe has been the past president of the American Finance Association, a trustee of the Economists for Peace and Security, and a recipient of a Doctor of Humane Letters, Honoris Causa from DePaul University, a Doctor Honoris Causa from the University of Alicante (Spain), a Doctor Honoris Causa from the University of Vienna (Austria), and the UCLA Medal, UCLA’shighest honour.
‘Sharpe In Focus’ originally appeared in the May 2008 edition of Wilmott Magazine. For further information visit www.wilmott.com
ABOUT THE AUTHOR
Dan Tudball is the Editor of Wilmott Magazine, the leading publication for Quantitative Finance practitioners.