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Speaker 1: I PC. This is Masters in Business with Barry Ridholts
Speaker 1: on Bloomberg Radio this week on the show, What a delight.
Speaker 1: I don't even know how to begin to explain this.
Speaker 1: Uh Bill Sharp, just a legend in finance, couldn't have
Speaker 1: been more charming and more delightful. There was a lot
Speaker 1: of back and forth in terms of when you guys
Speaker 1: going to be on the West Coast. I'm here, I'm there,
Speaker 1: I'm busy week and he no longer keeps in office
Speaker 1: at Stanford, and he didn't want to come into San Francisco.
Speaker 1: He lives down in Carmel, and the guys in Andrews
Speaker 1: and Horowitz were nice enough to give me a podcasting
Speaker 1: room to sit in and record the show. I can't
Speaker 1: begin to tell you what a delight it was to
Speaker 1: chat with him. He's just beyond knowledgeable. He really helped,
Speaker 1: you know, the original concept of the show is speaking
Speaker 1: to the minds who helped shape finance and investing in business,
Speaker 1: and who who better than Bill Sharp. So many of
Speaker 1: the principles that we just accept as as running the
Speaker 1: middle ordinary things in finance, Bill Sharp helped to create.
Speaker 1: He helped to create the first index fund, He helped
Speaker 1: to create the capital asset pricing model. He helped to
Speaker 1: create our understanding of risk. Just stop and think about
Speaker 1: those achievements. We we talked about a number of really
Speaker 1: fascinating things, and I can't begin to tell you how
Speaker 1: just delightful it is. So my job was to kind
Speaker 1: of get out of his way and just keep nudging
Speaker 1: him in into expounding more and more. Uh, some of
Speaker 1: the stuff is a little wonky. It's not your usual
Speaker 1: market based conversation because he's the guy who built the
Speaker 1: underlying infrastructure. Suffice it to say, it's absolutely fascinating. So,
Speaker 1: with no further ado, my conversation with Nobel Laureate William Sharp,
Speaker 1: Professor Bill Sharp, Welcome to Bloomberg, hosted here at Andres
Speaker 1: and Harrowitz. Your dissertation was called the single index model.
Speaker 1: Tell us what that was about. Okay, it'll be won't
Speaker 1: be as short as possible answer, but it'll be a
Speaker 1: lot shorter than the dissertation the Harry Marco What's His
Speaker 1: work was what we would call normative, in the sense
Speaker 1: that he was asking the question, your portfolio manager, there
Speaker 1: are securities, there's a client. How do you build a
Speaker 1: portfolio that's good for the client. And in his structure
Speaker 1: he allowed for not only as tom to the expected
Speaker 1: return on general motors, let's say, the risk of general
Speaker 1: motors stock, but also the extent to which general motors
Speaker 1: would move with Ford, with general foods, etcetera. A whole
Speaker 1: lot of number precisely, so, a whole lot of numbers.
Speaker 1: And he had a procedure to find a so called
Speaker 1: set of efficient portfolios given that set of numbers, and
Speaker 1: it took a big computer, a lot of time, a
Speaker 1: lot of money to do it. Um. He also, in
Speaker 1: his in his work, had suggested you might simplify the
Speaker 1: relationships among securities, and he proposed a number of versions,
Speaker 1: one of which was, well, you could say general motors
Speaker 1: moved with the market to a certain extent, general foods
Speaker 1: moved with the market to a certain extent, each of
Speaker 1: them had movements on their own and leave it at that.
Speaker 1: In other words, a very simple model in which there
Speaker 1: was one single index, let's call it the market for
Speaker 1: for now, which created all the correlation, all the co
Speaker 1: movement among securities. So he essentially before we talked about data,
Speaker 1: he had to come up with the concept of, well,
Speaker 1: here's what the market actually well, and I hope I
Speaker 1: recalling correctly. In his version, this was just, oh, you
Speaker 1: might want to make this simple assumption. And then there
Speaker 1: were a couple of other authors that were writing with
Speaker 1: that same kind of structure, but there was no sense
Speaker 1: this would be the market portfolio was just a thing,
Speaker 1: a single index. But but so I. So what I
Speaker 1: did was I I took that concept and I did
Speaker 1: three things in the dissertation. One was I wrote a
Speaker 1: computer algorithm that could take advantage of that simplified structure
Speaker 1: and find efficient portfolios, you know, for orders of MAGA
Speaker 1: it to less computer time. Then if you expanded it
Speaker 1: to the full structure. So the first part of the
Speaker 1: dissertation was an algorithm and a foretrend program. Probably the
Speaker 1: first dissertation and economics at U c L A. That
Speaker 1: included programs um The second I UH Fred Weston had
Speaker 1: a friend who was an investment advisor, a real human
Speaker 1: investment advisor, and so I worked with him to try
Speaker 1: to get him to make probabilistic forecasts for a group
Speaker 1: of securities, and then we ran them through the algorithm
Speaker 1: to see what it implied. And then the third I
Speaker 1: did what my training as a micro economist, which is
Speaker 1: most of my training, UH, would cause me to do,
Speaker 1: what if everybody did this? What if everybody did what
Speaker 1: Harry said? What would the world look like? So Harry's
Speaker 1: portfolio theorem would basically guide all the investors. That was
Speaker 1: my assumption. So I turned from what should you do
Speaker 1: normative to how might the world work? Positive theory, which
Speaker 1: is what economists at that time in particular generally did,
Speaker 1: and said, well, if everybody did this and markets cleared
Speaker 1: and prices adjusted, what would be the relationship in equilibrium
Speaker 1: between expected returns on securities and some measure of risk?
Speaker 1: And the conclusion was again this was positing, this single
Speaker 1: index model. Conclusion was that the thing, the common factor
Speaker 1: that would matter would be the market portfolio. And that's
Speaker 1: when the term beta came to be, and that expected
Speaker 1: returns would be related only to beta's in a linear manner.
Speaker 1: For that matter, were you aware at the time how
Speaker 1: innovative and groundbreaking and influential this idea would be going forward?
Speaker 1: Was it? Oh, I think I have a pretty nice
Speaker 1: dissertation here the ladder, the ladder. And then I finished
Speaker 1: the dissertation in June and then started teaching the University
Speaker 1: of Washington in September, and you know, I'd written up
Speaker 1: the algorithm for a paper, and uh now I was saying,
Speaker 1: this is really a nifty result. This they'd you know,
Speaker 1: x equilibrium result. But it's sort of like, Okay, I
Speaker 1: pulled a rabbit out of a hat. But but I
Speaker 1: put it in beforehand with this single index model assumption,
Speaker 1: and I said, boy, it would be nice if I
Speaker 1: could get that beautiful answer without making this assumption which
Speaker 1: almost directly created the answer. So I I noodled around
Speaker 1: and talked to colleagues and um thought of it, and
Speaker 1: then all of a sudden, without you know, within two
Speaker 1: or three or four months, said wait a minute, I
Speaker 1: don't have to make that assumption. I can get that
Speaker 1: result in a general situation. And at that point that
Speaker 1: was the capital has at pricing model m HM and uh.
Speaker 1: I first wrote it up and submitted it for publication
Speaker 1: in sixty two, and it was rejected by a referee.
Speaker 1: And then how fantastic is that? Yeah? And and I
Speaker 1: finally published it in sixty four. It took you three
Speaker 1: years to get your paper on capital, and that ultimately
Speaker 1: is what leads That was what was cited in the
Speaker 1: Nobel Prize. Yeah. Isn't that fascinating that such an interesting
Speaker 1: innovative idea rejected for a few years. Well, you know,
Speaker 1: I happen to know. I found out who the referee
Speaker 1: was and his argument was, well, that's an unrealistic assumption.
Speaker 1: And and I was taught by Milton Friedman indirectly in
Speaker 1: others that you don't evaluate a theory of that sort
Speaker 1: on the assumption. You evaluated on the conformance of the
Speaker 1: results with the real world, because there is always making something.
Speaker 1: The thesis is, we're gonna start with these assumptions. Where
Speaker 1: does it lead to? It leads us here? Isn't that interesting? Exactly? So?
Speaker 1: Um So? In any event, and as a matter of fact,
Speaker 1: when it was finally published in the journal Finance, you know,
Speaker 1: I asked as far as the refereeing. I asked it,
Speaker 1: what could we have another referee? Please? None? They changed
Speaker 1: editors and such. But when it was published, I thought, well,
Speaker 1: this is the best thing I'm ever going to do,
Speaker 1: and in that I was correct. And and I sat
Speaker 1: by the phone. We didn't have computers in those days,
Speaker 1: no no text or emails, waiting for the phone to ring.
Speaker 1: And it didn't ring, and it didn't ring, and I thought,
Speaker 1: you know, months passed and that, man, I've just written
Speaker 1: the best paper I'm ever going to write, and nobody cares.
Speaker 1: But eventually people started paying attention to That's a that's
Speaker 1: a fascinating story. It's very it would be very different now. Well,
Speaker 1: things seemed to ricochet around the world nearly much more rapidly,
Speaker 1: although I don't think people recognize the depth with a
Speaker 1: gravitas of certain ideas right away. But what are you
Speaker 1: thinking during that period where you know you've found something
Speaker 1: unique and valuable and nobody else's is recognizing that yet? Well, um,
Speaker 1: needless to say, you have some Maybe it wasn't I
Speaker 1: knew it was unique. Well, there's some dispute about that too.
Speaker 1: Others were going down similar paths and in various ways.
Speaker 1: But but I thought it was valuable, and I was,
Speaker 1: and I thought, well, if this isn't valuable, let me
Speaker 1: give you a little broader context. In those days, economics
Speaker 1: was theory and equilibrium and all that sort of stuff.
Speaker 1: Finance was very old timey by any modern standards. And
Speaker 1: so I was one of the first economists that went
Speaker 1: into the field of finance Fred Weston before me, to
Speaker 1: try to bring some of the rigor of economics to finance.
Speaker 1: And so there was a kind of a cultural issue
Speaker 1: as well, that's interesting and so, but the journal Finance
Speaker 1: had a number of I would call let's call them
Speaker 1: scientific articles. It was not unusual, but it was. It
Speaker 1: was a change for the field of finance, not only
Speaker 1: in practice, but also in academics, which is, you know,
Speaker 1: there was no field call financial economics. Now there is.
Speaker 1: How how influence was Harry Markoitz and your work with
Speaker 1: him it rans on you eventually creating the capital asset
Speaker 1: pricing model. Well, obviously, if I hadn't read Harry's work,
Speaker 1: if I hadn't done the work in the first part
Speaker 1: of the dissertation, I wouldn't have moved to go to
Speaker 1: the stage of asking the question what if everybody did this,
Speaker 1: so in that sense crucial um it was interesting. Harry.
Speaker 1: Two or three years later he said, oh, I just
Speaker 1: re read your paper, and now I see you didn't
Speaker 1: assume that you actually derived it. So so you know,
Speaker 1: I will say that that part I think was pretty
Speaker 1: much my work. Fascinating, although, as I say others, we're
Speaker 1: beginning down a similar path. Jack Traynor, you know, he
Speaker 1: didn't publish, but he was going down and he came
Speaker 1: at it differently. Where was Jack Traynor? He was well,
Speaker 1: he wasn't at heart. He he was working I think
Speaker 1: as a student at Harvard. I believe he was at
Speaker 1: Arthur D. Little when he was doing that work. He
Speaker 1: came at it a different way. And I became aware
Speaker 1: of his work a year or two after I had
Speaker 1: submitted Mind for publication. So I put a footnote in saying,
Speaker 1: you know, here's this other work you should be aware of.
Speaker 1: So you become a professor emeritus and decided to spend
Speaker 1: some time consulting at William F. Sharp Associates. What sort
Speaker 1: of consulting work were you doing back? Actually was when
Speaker 1: I finally bit the bullet and became an emeritus professor.
Speaker 1: My wife and I started a research slash consulting firm
Speaker 1: in eighty six which went through an eight nine and
Speaker 1: I took leave. Then I went back and then I
Speaker 1: thought I need to do it full time. UM and
Speaker 1: so that lasted six years in different manifestations up. What
Speaker 1: we were trying to do is bring I hate to
Speaker 1: call it modern finance theory, I hate that term, but
Speaker 1: financial economics theory, you know, empirical work to bear on
Speaker 1: the problems faced by the manager of the General Motors
Speaker 1: Pension Fund, the manager of the Stanford University endowment, so
Speaker 1: professionals who were managing large pools of money in an
Speaker 1: institutional setting. And so the idea was bring to bear
Speaker 1: the research that existed, and do new research and bring
Speaker 1: in new things that could help those folks. So so
Speaker 1: that that was the target in terms of the problems.
Speaker 1: And so we set up this firm and again in
Speaker 1: various manifestations, and we worked with General Motors, pension Fund, endowment, etcetera.
Speaker 1: So a lot of really substantial institutions with a lot
Speaker 1: of money. What were the sort of problems that they
Speaker 1: were encountering in the real world that your theory helped resolve? UM. Well, first, uh,
Speaker 1: what were the risks, Where were the risks? What was
Speaker 1: their performance? How did it you know, was it good
Speaker 1: or a bad relative to the risks that we're taking.
Speaker 1: People really were not all that clued into risk adjusted
Speaker 1: performance or had at that point. It was better understood
Speaker 1: at one level, but not. For example, one of the
Speaker 1: things that came out of that was what's called returns
Speaker 1: based style analysis. How do you get your hand You've
Speaker 1: got this portfolio've got a hundred different money managers out there,
Speaker 1: how do you get your arms around it. Who's doing what?
Speaker 1: How does this piece fit in with that piece? Are
Speaker 1: you getting enough average returns out of this manager to
Speaker 1: justify being in the portfolio and being in at that
Speaker 1: level or less or more? Um. We take that for
Speaker 1: granted that you could today run a spreadsheet, crunch froom
Speaker 1: numbers and bang you can figure that out. This was
Speaker 1: not simple to do best so so for example, returns
Speaker 1: based anile analysis. And again the idea was to get
Speaker 1: your arms around the whole portfolio, time managed portfolio and
Speaker 1: evaluated as a whole. Try to figure out whether or
Speaker 1: not you've got the right pieces and you've got them
Speaker 1: in the right magnitudes and uh, you know, at the
Speaker 1: end of the day, are you adding value? So so,
Speaker 1: there are a number of problems and uh, we got
Speaker 1: to deal with the real world very sophisticated clients and um,
Speaker 1: try out some new ideas, developed some new ideas. It was,
Speaker 1: it was, it was pretty heavy. So you joined the
Speaker 1: Rand Corporation in nineteen fifty six and you meet Harry
Speaker 1: Markowitz who ultimately helps you with your dissertation. Tell us
Speaker 1: about what it was worth like working with the markt
Speaker 1: Witz on your PhD project. Well, let me if I
Speaker 1: may do a little more of the backstory. Make sure
Speaker 1: when I first went to RAND out, I came out
Speaker 1: of the service with a master's degree to RAND. And
Speaker 1: when I first went, Harry was not there at that point,
Speaker 1: um and um. So I was working on logistics issues,
Speaker 1: big models and computer programs and what have you. And
Speaker 1: I decided I wanted to teach. So I the path
Speaker 1: of least resistance was to take some education courses get
Speaker 1: a junior college credential. I took one education course at
Speaker 1: night and decided no, I'd rather get a PhD and
Speaker 1: teach at the university level. So I was able to
Speaker 1: get a pH d at u c l A while
Speaker 1: working full time supporting my family at rand because they
Speaker 1: were very generous in that regard. Uh I started a
Speaker 1: dissertation a totally different subject, transfer pricing, using a lot
Speaker 1: of operations research methodology, and when Jack Hurst Lifer, whose
Speaker 1: work I was building on, came to U c l A,
Speaker 1: my adviser said, once you go talk to Jack, and
Speaker 1: I did, and Jack read my half dissertation and said,
Speaker 1: I don't I don't think there's a dissertation here that
Speaker 1: has to be a little frustrating you. Half that was,
Speaker 1: but I've remember Jack's dead now, but telling him more
Speaker 1: than once that he did me a great favor because
Speaker 1: then I went to Fred Weston, financial economist at U
Speaker 1: c l A, and said, what am I gonna do?
Speaker 1: He said, well, you really liked this work Harry Marco,
Speaker 1: what's did? Harry has just come to rand On. Donce
Speaker 1: you go talk to Harry. So I did, and I
Speaker 1: worked out an arrangement between Fred and our i'm an
Speaker 1: Alchin and at U c l A, and Harry, who
Speaker 1: was not at U c l A, that I'd work
Speaker 1: with Harry. So it was a little more. He wasn't
Speaker 1: a professor at US, but he effectively acts as your
Speaker 1: PhD advisor. Well, it was a little more collegial because
Speaker 1: you know, we're both working around together. Um, and he
Speaker 1: didn't have any authority, but but yes, basically he and
Speaker 1: I chatted about this and that and wanting to try
Speaker 1: that and and the rest and and so forth. So
Speaker 1: there are worse PhD thesis advisors than Harry markets every day.
Speaker 1: I'm thankful that that worked out. Let's let's talk a
Speaker 1: little bit about capham and and how how that has
Speaker 1: evolved over time. First, when you first introduced it, has
Speaker 1: your thinking evolved on that since or is it still
Speaker 1: what it was when you first thought it up? Now
Speaker 1: it has evolved and people are sometimes surprised of this.
Speaker 1: And let me if I may sort a little bit
Speaker 1: of time. The capitalistic pricing model builds on Harry's view
Speaker 1: of the world, which is that you think about the
Speaker 1: world in terms of mean and variance, expected return and
Speaker 1: risk variations mean variance. So here here's what we here's
Speaker 1: what's average, here's how much you can how much volatility
Speaker 1: around that is exactly what you should expect and and
Speaker 1: what you might not get despite your expectations. And then
Speaker 1: what I did in my equilibrium model is say, well,
Speaker 1: what if everybody thinks about the world that way, and
Speaker 1: they come to market and they trade with each other
Speaker 1: and prices at just what would one expect to find,
Speaker 1: and not surprisingly you find that securities in that world
Speaker 1: would be priced so higher expected return goes with higher beta,
Speaker 1: which is a measure of how things move together, and
Speaker 1: it's related to this variance mean variance structure. And you know,
Speaker 1: the economical line is it's market risk that matters. That's
Speaker 1: what you get rewarded for. Other risks you don't get
Speaker 1: paid for. That's sort of the bottom line. About the
Speaker 1: time Harry was first working, can Arrow and Gerard Debreu
Speaker 1: independently developed what came to be known as state preference theory,
Speaker 1: which basically is a model of prices in an equilibrium framework.
Speaker 1: And the basic idea there, to just make it overly simplified,
Speaker 1: is that you know, how much would it cost you
Speaker 1: to buy a security that pays you a dollar three
Speaker 1: years from now, if at that point the market is up,
Speaker 1: you know, give or take what would that cost, and
Speaker 1: there's some number present value of that, and then you
Speaker 1: just think of the world. There's a whole bunch of
Speaker 1: those and when you put that into a security market context,
Speaker 1: you again get the result that it's market risk that matters,
Speaker 1: but it may matter in a different way instead of
Speaker 1: in a certain kind of diagram. Instead of a straight line,
Speaker 1: it may be a curve. Just to widely oversimplify. And
Speaker 1: the great thing about that view is that it extends
Speaker 1: very beautifully to multi periods. It's it's much more general.
Speaker 1: And so that approach, which now in PhD programs and
Speaker 1: finance often is called pricing kernel k E R N
Speaker 1: E l UH. It has the same character, it has
Speaker 1: many of the same pragmatic results, but it's more general,
Speaker 1: and so that's what I use. I do not use
Speaker 1: the capitalized pricing model in my models in my work,
Speaker 1: which surprises some people. So you're you're using what is
Speaker 1: effectively the natural evolution of that. Well, they happen to come.
Speaker 1: They sort of the paths combined in some sense with
Speaker 1: Jack kerchlife for at U C l A, and Mark
Speaker 1: Rubinstein is his student there um. But so in some
Speaker 1: sense they're not sequential. But yes, I do, and I
Speaker 1: try to because I this is taught, as I say,
Speaker 1: at the PhD level, I think it ought to be
Speaker 1: taught at the NBA level and the undergraduate level. So
Speaker 1: in two thousand and seven I published a book the
Speaker 1: name of which I can't quite remember, that came out
Speaker 1: of some lectures I gave at Princeton, trying to make
Speaker 1: the case that yes, you can teach undergraduates and NBA
Speaker 1: students this approach rather than mean variance slash C A
Speaker 1: p M. Although again, as they say qualitatively, they're not
Speaker 1: wildly different. So the two thousand and seven book is
Speaker 1: called Investors and Markets Portfolio Choices, Asset Prices, and Investment Advice.
Speaker 1: Assuming Amazon is giving me the correct information. Amazon never failed,
Speaker 1: So so let's talk a little bit about that. Um,
Speaker 1: you know, we used to have memories and now we
Speaker 1: have these devices with us, and it's like I've outsourced.
Speaker 1: I knew the name of that book, and I couldn't,
Speaker 1: for the life of me recall it because it's just
Speaker 1: so easy to pull it up. Um paper, you you
Speaker 1: discussed earlier capital asset prices, the theory of market equilibrium
Speaker 1: under conditions of risk. I'm gonna pull a quote from
Speaker 1: that that I think is really powerful. Diversification enables the
Speaker 1: investor to escape all but the risk resulting from swings
Speaker 1: and economic activity. This type of risk remains even inefficient combinations.
Speaker 1: That's very powerful. How did you come upon that because
Speaker 1: it's not obvious or intuitive. Well, as I say that,
Speaker 1: you know that statement really needs another sence. Okay, as
Speaker 1: wonderful and I appreciate your finding it. The um the
Speaker 1: basic idea is that is the risk for which you're
Speaker 1: going to be rewarded if you expose you If you
Speaker 1: expose yourself more dramatically to down markets, then in a
Speaker 1: long run you should do better. In the short run,
Speaker 1: you can be wiped out at least widely injured. Um So,
Speaker 1: so that's the basic notion. And in the single index
Speaker 1: model that's assumed in the more general capitalizet pricing model,
Speaker 1: that's a conclusion. So so the assumptions, and you you
Speaker 1: discussed this earlier about how it was potentially problematic um
Speaker 1: for some of the people who were refereeing your your
Speaker 1: initial papers. Were the assumptions fairly straightforward in order to
Speaker 1: test the thesis or did you have to go out
Speaker 1: on a ledge a little bit with with some of
Speaker 1: your assumptions? Well, you know, I mean the the models,
Speaker 1: either the dissertation or the subsequent one. You know our models,
Speaker 1: You make some assumptions, and then you do some some
Speaker 1: calculations and some operations. You get a conclusion. I did, ah,
Speaker 1: a test that is so crude. I don't even we
Speaker 1: don't remember how crude our data sets were. I mean,
Speaker 1: I did a test in my dissertation. I used annual
Speaker 1: returns on thirty mutual funds. That was my database, and
Speaker 1: I had to put it on index cards and go
Speaker 1: to the library and write down all the numbers and
Speaker 1: use a hand, you know, a desk calculator. Um. So,
Speaker 1: over the years, of course, we did more sophisticated test
Speaker 1: had better databases. And that said, even today with all
Speaker 1: that we have, um it's hard. You know, there's a
Speaker 1: lot of noise and what happens in security shorts, so
Speaker 1: it's hard to find what might be at the core
Speaker 1: and a truth for the long term. Let us say, so,
Speaker 1: let's let's talk a little bit about risk, because you
Speaker 1: are certainly known for your work beyond the capitalist pressing
Speaker 1: model on risk. What is the appropriate way to think
Speaker 1: about risk as an investor? Well, let me give you
Speaker 1: an anecdote for that question. The first time I met
Speaker 1: Peter Bernstein, who is legendary and I suspect many of
Speaker 1: your listeners his work. No, he was. He was a sweetheart.
Speaker 1: First time I met him, we had lunch somewhere in
Speaker 1: New York and he was then managing money for wealthy
Speaker 1: clients and we were talking about risk and risk aversion
Speaker 1: and risk tolerance and and he said, well, do you
Speaker 1: know when I know what the true risk tolerance of
Speaker 1: a client is? And I, being young and naive, said no.
Speaker 1: When Peter he said, well, after the markets had a
Speaker 1: really bad day and I get a call at two
Speaker 1: am saying I can't take it. I can't take it
Speaker 1: from a guy who said, oh I can take risk.
Speaker 1: So so mes ng risk. But I think for most
Speaker 1: human beings, risk is losing a lot of money in
Speaker 1: a short period of time, unexpectedly. And then the question
Speaker 1: is how do you I mean, that's a little too
Speaker 1: amorphous to put in a nice, rigorous mathematical model, but
Speaker 1: presumable if you if you take something that could go
Speaker 1: up or it could go down, is more likely to
Speaker 1: go up than to go down. Then risk is a
Speaker 1: probability distribution, and the wider it is, the more risk
Speaker 1: there is. And you can start using measures like variants
Speaker 1: or standard deviation to try to try to simplify that,
Speaker 1: but um, it's it's just But yes, the downside is
Speaker 1: what we worry about. We don't worry about upside risk.
Speaker 1: That's okay, it seems to take care of itself. But
Speaker 1: they very often go together. To Peter Bernstein's point, when
Speaker 1: we discuss risk tolerance, we think about it objectively, but
Speaker 1: in reality, really asking people is how do you feel
Speaker 1: about what has happened over the past month? And it's
Speaker 1: and I know there have been lots of studies about this.
Speaker 1: When the market is strong, people's claim to have Oh,
Speaker 1: I have a very high restolerance, and when the market's
Speaker 1: getting shell acted, so I listen, I can't lose any money.
Speaker 1: I have a very low restolerance. It's amazing there's no
Speaker 1: objective way to self measure ourselves. But that seems to
Speaker 1: be the case. Yeah, this is, you know, strange you asked.
Speaker 1: Just yesterday, I spent an hour with a woman who
Speaker 1: she's actually a wife of a friend of mine in
Speaker 1: totally different context, and she's a financial advisors to individuals,
Speaker 1: generally young techie types, and her question was your question,
Speaker 1: how do I how do I talk about risk to
Speaker 1: my client? How do I estimate his or her tolerance
Speaker 1: for risk? And you know there are questionnaires and psychological
Speaker 1: this and that, and she's tried those two and they're
Speaker 1: not very satisfying. It's it's it's very difficult. It's so
Speaker 1: colored by what just happened. Let let me ask you
Speaker 1: the same question differently, and this time I will invoke
Speaker 1: the sharp ratio. The sharp ratio treats upside volatility equal
Speaker 1: to downside volatility, but as you just point out, they're
Speaker 1: really not equal. We were much more concerned about downside
Speaker 1: volatility and downside deviation. UM is downside deviation a better
Speaker 1: metric than than standard volatility. How should we really conceptualize this? Well,
Speaker 1: let me go back and Harry's early work. He had
Speaker 1: a section I think was in the book Uh saying well,
Speaker 1: maybe we ought to use He focused on what's called
Speaker 1: semi variance. Variance is risk squared. Let's call it up
Speaker 1: and down semi variance as a measure of the downside
Speaker 1: UM And it's like very you know, it takes all
Speaker 1: the possible downsides and waits them and squares them and
Speaker 1: such um and and people have come up. I think
Speaker 1: Frank Sartino has a ratio which had at one point
Speaker 1: that uses downside uh and yes, I mean there's no
Speaker 1: doubt about it. And certainly the behavior literature tells us
Speaker 1: that people wait downside much more heavily than they wait upside.
Speaker 1: So so that all is very appealing and attractive. It's
Speaker 1: extremely difficult to build equilibrium models because the mathematics gets
Speaker 1: really squirrely UM and I've tried and failed. Others have tried,
Speaker 1: perhaps failed as well. But the but in many cases,
Speaker 1: the distribution, if you want to think of it that way,
Speaker 1: is symmetric enough. So if you measure the square deviation
Speaker 1: from the mean, which is standard deviation and or related
Speaker 1: that's variants UM or you measure the square deviation on
Speaker 1: the downside, you get similar numbers and uh more securities
Speaker 1: in your portfolio than more likely that is to be
Speaker 1: the case in most circumstances. So so although we've talked
Speaker 1: about that and thought about that at the portfolio security level, UM,
Speaker 1: because the mathematics gets so so ugly, we tend to
Speaker 1: stay with variants and in the sense that maybe it's
Speaker 1: it's it's close enough approximation. So you just referenced um
Speaker 1: investor expectations as part of a risk model of thinking
Speaker 1: about what actual risk is. How has your thinking on
Speaker 1: investor expectations evolved over the years? UM, While I was,
Speaker 1: I'm reminded of I think it was George Stiegler who
Speaker 1: wrote about firms maximizing profits, etcetera, and said, anytime I
Speaker 1: visit the manager of a real firm, I have to
Speaker 1: go back and reread my textbooks. UM, the same thing
Speaker 1: with investors. I guess my view as we all know
Speaker 1: that your neighbor is a is not a very sophisticated investor.
Speaker 1: Introspection will tell us that we're not sophisticated investors. But
Speaker 1: you've got to think about security markets. It's not democracy.
Speaker 1: Not every investor gets the same votes. Rich investors have
Speaker 1: a lot more votes, and they have a lot more resources,
Speaker 1: They do a lot more research, and they presumably can
Speaker 1: be more intelligent about trying to estimate risk. Nobody can
Speaker 1: really estimate risk, because you know, it only manifests itself
Speaker 1: in an outcome every day or a minute. But you know,
Speaker 1: I think I would prefer to think of the market
Speaker 1: as setting prices, taking into account as best one can
Speaker 1: information about the uncertain future. There are some other aspects
Speaker 1: that will probably talk about we don't even have to
Speaker 1: think the markets that intelligent. But um, but no, I
Speaker 1: mean when you go meet a real investor or introspect
Speaker 1: on your own investment, you say, how can. But there's
Speaker 1: also in this book I referenced the two thousand seven books.
Speaker 1: I did a lot of simulations, and it's really fun.
Speaker 1: You can simulate a world in which you have a
Speaker 1: little bit of information. I have a little bit. None
Speaker 1: of us really knows what we're doing, and yet magically
Speaker 1: the prices end up incorporating all the information. I mean,
Speaker 1: this is not a new finding. But what's fun is
Speaker 1: to write just a smallish and you know, simulation program
Speaker 1: and see how how remarkably efficient. Uh, it's the idea
Speaker 1: that all of us is smarter than any one of us.
Speaker 1: Collect The collective understands what's going on any single person.
Speaker 1: And it's remarkable how easy it is to demonstrate the
Speaker 1: power of that. Let's talk about your most recent product, jecked,
Speaker 1: you started working on something a few years ago. Uh
Speaker 1: that I I mispronounced rizz riz pat my term. Tell
Speaker 1: us what rizmat is. Well, this is sort of I've
Speaker 1: sort of moved. As we spoke about earlier, There was
Speaker 1: a phase in my life in which I focused on
Speaker 1: the problems of managers of large institutional funds, pensions, endowments,
Speaker 1: and then when I went back to Stanford in the
Speaker 1: early nineties, I started focusing four oh one case we're
Speaker 1: coming into being, So I started focusing my research on
Speaker 1: the problems of the individual investor trying to figure out
Speaker 1: how to accumulate, how to invest in their for O
Speaker 1: one K plans, let's call it uh, and then followed
Speaker 1: that up with Financial Engines as a firm devoted again
Speaker 1: at that point to the individual's accumulation phase. And for
Speaker 1: the last few years I have been focusing pretty much
Speaker 1: singlemindedly on the individual's decumulation phase, meaning how they draw
Speaker 1: that money down over time. I mean, my prototype is
Speaker 1: Bob and Sue Smith. She's sixty five, sixty seven, They've
Speaker 1: started social Security, They've got some savings from rollover iras,
Speaker 1: what have you? And uh, what do they do now?
Speaker 1: How do they buy an annuity? If so, what do
Speaker 1: they invest in mutual funds? If so, where it's do
Speaker 1: they buy some other sophisticated financial product. If they do
Speaker 1: their own investment, how do they invest how do they
Speaker 1: decide how much to spend each year. Uh and so
Speaker 1: this is trying to get my arms around many at
Speaker 1: least of the problems and issues associated with that set
Speaker 1: of decisions. And uh so the project involves an e
Speaker 1: book um which is very large. It would be for
Speaker 1: we're physical and suite of software. And it's all public domain,
Speaker 1: will be when it's released. So you're gonna give this
Speaker 1: away to whoever wants to use as I see it's
Speaker 1: it's under a Creative Commons Attribution License number something and
Speaker 1: meaning people can use it. They just can't resell it
Speaker 1: for commercial benefit. They can do anything they want as
Speaker 1: long as they spell my name right. So, so tell
Speaker 1: me about what what motivated this? How did this come about?
Speaker 1: You're in your early eighties. Theoretically you should be golfing
Speaker 1: or fishing, but you're still deep at work in the
Speaker 1: theory of financial asset pricing and management. What made you
Speaker 1: say three or four years ago, I know I'll create
Speaker 1: this giant project and give it away. Well, I don't
Speaker 1: golf and I don't fish, but I could sure go
Speaker 1: to more symphonies and operas and sail. I don't have
Speaker 1: a sail boat anymore. I have a boat but go
Speaker 1: out on the boat more often. UM. Well it uh,
Speaker 1: it's kind of the same thing that motivated my last
Speaker 1: two phases. Here's a really important problem. It's a problem
Speaker 1: which is appealing because it affects you know, ordinary people
Speaker 1: and uh, and it's really nasty. It's the nastiest, hardest
Speaker 1: problem I've ever looked at. And I can't say I've
Speaker 1: found some magic solution because I haven't. You're you're saying
Speaker 1: this is this is harder than capital asset pricing. This
Speaker 1: is harder than risk analysis. This is the hardest project
Speaker 1: you've ever seen. It is for two reasons. One because
Speaker 1: you can't just say, well, let's assume there is one
Speaker 1: period left in the world, and you know, you have
Speaker 1: to say there there are many years, it's continuing rolling
Speaker 1: and you never So you've got a multi period problem,
Speaker 1: which means you have to have a multi period pricing theory.
Speaker 1: And you don't know how many periods there are going
Speaker 1: to be, and you have the actualially shoes to deal with.
Speaker 1: You don't know how long people can live. Um, and
Speaker 1: so there are many, many issues. So it's a multidimensional
Speaker 1: problem in some senses. Where we chose to treat the
Speaker 1: others as a single dimension. And uh, so it's it's
Speaker 1: good and juicy in terms of hard to do. Um
Speaker 1: And uh, you can, as far as I can see,
Speaker 1: you can only deal with it with computations. And I
Speaker 1: write programs for fun. I love programming, so um and
Speaker 1: it's it's important. So it had all the things that
Speaker 1: you know turn me on as an economist. So so
Speaker 1: what is you said? You're not too far away from
Speaker 1: compleating this. This eventually goes on. By the time this broadcast,
Speaker 1: it should be online, I would hope. So yeah, it'll
Speaker 1: be probably first on my website at Stanford. We may
Speaker 1: move it to another site at Stanford. But so what
Speaker 1: was what did you learn doing this project? What's the
Speaker 1: takeaway for how people should draw down? Because one of
Speaker 1: the standard things we hear is well, you're gonna draw
Speaker 1: a five to seven percent a year for the next
Speaker 1: twenty years, and that's just such a rough rule of
Speaker 1: Thumb's YEA, Well, if I may give you a little
Speaker 1: bit of the structure. Uh, the project has the word
Speaker 1: matrices in it, and the book has programs and matrix algebra.
Speaker 1: It's only somebody in a financial engineering program would love this. Probably, Um,
Speaker 1: but the idea is, think about a matrix and boy,
Speaker 1: I think use the word table, spreadsheet, call it is
Speaker 1: spreadheat and every row is a possible scenario for the
Speaker 1: next fifty years. And there are a bunch of rows.
Speaker 1: In fact, there are a hundred thousand rows, because there
Speaker 1: are a lot of things that could happen to have
Speaker 1: a hundred thousand different scenarios, and each column is a year. Okay,
Speaker 1: so you've got that let's call it spread sheet. But
Speaker 1: you've got a lot of these spreadsheets. So, for example,
Speaker 1: there's one spreadsheet that's built out of actuarial tables that
Speaker 1: that basically says, okay, in this scenario, Bob and Sue
Speaker 1: my protagonists or whomever you want, determine who there are,
Speaker 1: how old they are, they live, both of them live
Speaker 1: for the first three years, then Bob dies, so lives
Speaker 1: five more years, then Sue dies, and then what's left
Speaker 1: goes to the estate. So that's the sort of what
Speaker 1: I call personal states matrix. So you have a hundred
Speaker 1: thousand different things that could have you know, in terms
Speaker 1: of mortality, let's call it. Then you have another one
Speaker 1: of those spreadsheets for what happens to the returns on
Speaker 1: the market portfolio, which in my version is a world
Speaker 1: bond and stock portfolio index fund low cost. So each
Speaker 1: of those is this year it did eight percent, the
Speaker 1: next year it did twelve percent, the next year it lost.
Speaker 1: So you have a hundred thousand different story for the market.
Speaker 1: You've got another one for inflation, hundred thousand different inflation stories,
Speaker 1: another one for what happens to tips Treasury protected securities.
Speaker 1: Those are my two investments. And then you say, and
Speaker 1: then you've got Bob and Sue, or you've got one
Speaker 1: for socialists. Then the other sort of fill up with incomes.
Speaker 1: So in this scenario, in this year, how much do
Speaker 1: Bob and Sue get from social Security? And there's another one,
Speaker 1: so you got a whole bunch of those. Another one
Speaker 1: on how much do they get from Let's take the
Speaker 1: strategy you alluded to so call four percent rule. Put
Speaker 1: your money in whatever investments, take out four percent the
Speaker 1: first year. Every year, keep taking out an amount with
Speaker 1: the same purchasing power as what you took out initially
Speaker 1: until you either die or run out of money, and
Speaker 1: good luck to you. UM and I and a couple
Speaker 1: of my colleagues at financi Engines have written about that rule.
Speaker 1: It's it's not the worst possible rule, but it's right
Speaker 1: up there. It's it's just a simple rule of thumb
Speaker 1: that people use but clearly subopticate precisely. But you know,
Speaker 1: do I have Can I say I have an optimizer
Speaker 1: that will tell you the optimal rule? No, I do not,
Speaker 1: um nor does anybody else. If you, if you were
Speaker 1: to give me multidimensional utility functions, don't ask multi dimension
Speaker 1: utility functions. Okay, So here is the utility of income
Speaker 1: for me in next year, and then here's another one
Speaker 1: the following year. Then in principle I might be able
Speaker 1: to give you an optimal strategy, but nobody does. Nobody
Speaker 1: has those utility functions. What I can do is infer
Speaker 1: I said, look, if you choose this strategy or this
Speaker 1: combination of strategies, then I can tell you, first of all,
Speaker 1: it's not efficient. You can do better. Or if it
Speaker 1: is efficient, I can say, well, you're acting as if
Speaker 1: these were your utility functions, and you could perhaps look
Speaker 1: at those and work backwards and right. So let let
Speaker 1: me make sure I understand what we have. So you
Speaker 1: have a variety of scenarios of longevity and mortality and
Speaker 1: all the variations there too, various market returns, various inflation returns,
Speaker 1: various tips returns, and you have hundreds thousands of each
Speaker 1: of these. And now you combine all these and you
Speaker 1: end up, aside from the extraordinary number crunching, with a
Speaker 1: huge assortment of possible outcomes for possible scenarios, and almost
Speaker 1: like an exponential Monte Carlo simulation. It is moontcano. I
Speaker 1: don't like to use that term U and so so
Speaker 1: what's the takeaway of that for the investor? Okay, first
Speaker 1: of all, let me say if I were teaching, I
Speaker 1: wish you were it would be in my class, because
Speaker 1: you know that that's that you've a quick learner. But
Speaker 1: we knew that you happen to I'm familiar with your
Speaker 1: entire body of work, and it is what you're describing.
Speaker 1: I had a few moments to think about beforehand, so
Speaker 1: when we discussed this previously, So as much as I
Speaker 1: appreciate that the it's on you not make the the
Speaker 1: the um the intellectual firepowers on that side of the table,
Speaker 1: we won't. We will argue offline. But there's a whole
Speaker 1: series of analytic routines which you can apply. Once you've
Speaker 1: done this for a particular strategy or set of strategies,
Speaker 1: you could add them together. And so, for example, I've
Speaker 1: talked about multidimensional probability distribution. What's the range of things
Speaker 1: that I could incomes I could get next year, what's
Speaker 1: the range of the following year. Well, there're at least
Speaker 1: two ways to show that. One is you show one
Speaker 1: distribute Susan and I have a particular pet way to
Speaker 1: show it that I think individuals can relate to better.
Speaker 1: And then you it's an animated graph. You show one
Speaker 1: and then the next comes up, and then the next.
Speaker 1: And another way is what's called an income map, where
Speaker 1: you're sort of like looking down from the sky on
Speaker 1: a on a terrain three dimensional exactly. And I have
Speaker 1: a bunch of analytic tools and in the software you
Speaker 1: can just say, well, let's try this one with that
Speaker 1: and that and that, and you can say, well, let's
Speaker 1: look at what happens if they're both alive, separately from
Speaker 1: what happens if one is alive. Because with ensuring annuities
Speaker 1: you have different payouts. With social security, you have different payouts.
Speaker 1: So you can you can do diagnostics, you can do
Speaker 1: as I say, and for well, this would be optimal
Speaker 1: for somebody with a utility function like this, or this
Speaker 1: is suboptimal. You can get the same probability distributions cheaper
Speaker 1: if you do it more efficiently, so I can diagnose that.
Speaker 1: Um so, so this sounds very sophisticated and complex. Well sophisticated, yes, complex.
Speaker 1: Unfortunately on the website, is this going to be easy
Speaker 1: for the average person or or adviser to plug into
Speaker 1: this and say, here's so I can figure out what
Speaker 1: I should be drawing down each year. I don't think so. No,
Speaker 1: um what I'm hoping I mentioned financial engineers. There are programs,
Speaker 1: and there are a lot of them, typically master's programs,
Speaker 1: sometimes an engineering or math or sometimes economics, sometimes business
Speaker 1: schools for financial engineers. Though, and these people, for example,
Speaker 1: you know this may sound You mentioned something that about
Speaker 1: run time, where you run time on one of these
Speaker 1: really complex analyzes with all these scenarios can be under
Speaker 1: a minute, sometimes well under a minute, because it's programmed
Speaker 1: in a language which is designed for matrix operations, matt
Speaker 1: Lab from Math works. And it turns out in almost
Speaker 1: all of these programs, most of the students on their
Speaker 1: resumes say they know Matt Lab, so the programming aspect
Speaker 1: isn't going to frighten them. And the majority of them,
Speaker 1: as far as I can tell with the breakdown, the
Speaker 1: majority of graduates of those programs go into unit you
Speaker 1: guessed at Wall Street creating connovatives. Not a single one
Speaker 1: that I could find in the summary went into working
Speaker 1: with a financial advisor who's working with retirees or near retirees.
Speaker 1: But I would hope that this would be in some
Speaker 1: sort of electives in those programs, and or that good
Speaker 1: technical people would be able to go through my material,
Speaker 1: go through the programs, learn how to use them, and
Speaker 1: then provide the back office for say a financial advisor.
Speaker 1: And that's the reason I was meeting with this UH
Speaker 1: person yesterday. She's a single person and she doesn't advise
Speaker 1: any retirees, so it wouldn't work. But I'm trying to
Speaker 1: find and I have a friend who does advise retirees
Speaker 1: and I'm trying to see if I can get him
Speaker 1: to incorporate that in his practice. So, but it sounds
Speaker 1: like the way you've built this, you want universities and
Speaker 1: graduate level programs. I know Columbia has a School of
Speaker 1: Financial Engineering within them runs it. You want, you want
Speaker 1: these folks to build upon what you've done, and I would.
Speaker 1: I would like there to be an elective on retirement income,
Speaker 1: and there currently isn't. There's no such um I have
Speaker 1: not done an exhaustive survey, but I'm willing to bet
Speaker 1: there is not because it sounds like, I mean, there
Speaker 1: maybe one on accumulation, but decumulation. It sounds like it's
Speaker 1: the sort of problem that's ready made for somebody's PhD
Speaker 1: dissertation or is it too complex for that. Well, I'm
Speaker 1: not sure you know every your standards for PhD dissertation.
Speaker 1: I'm not sure that there would be a lot new.
Speaker 1: I mean you can certainly propose new. I mean I
Speaker 1: in this I have techniques that nobody's ever used. I
Speaker 1: have constructs that nobody's ever implemented. So there are things
Speaker 1: in there that that could give rise to new financial
Speaker 1: products and investment and insurance products. Um. So I don't
Speaker 1: know about a PhD dissertation. I'm thinking more M S
Speaker 1: M a financial engineering level. Uh, it's certainly not NBA
Speaker 1: level material and people will be able to find this
Speaker 1: at Stanford dot edge you slash. It'll be originally my website. Uh,
Speaker 1: if you just go online and say w F sharp
Speaker 1: or something, you'll find it and um and then as
Speaker 1: I say we may, it may get a website of
Speaker 1: its own at Stanford. What could your future hold more
Speaker 1: than you think because it merely we work with you
Speaker 1: to create a strategy built around your priorities. Visit mL
Speaker 1: dot com and learn more about Merrill Lynch. An affiliated
Speaker 1: Bank of America, Mary Lynch makes available products and services
Speaker 1: offered by Merrill Lynch Pierce Federan Smith Incorporated or registered
Speaker 1: broker dealer. Remember s I pc UM. Let's talk about
Speaker 1: Financial Engines for a moment. A prior guest was Jeff
Speaker 1: Magian Calda. He was a CEO, you were the chairman
Speaker 1: from to two thousand and three and the co founder. Uh.
Speaker 1: Financial Engines are one of those companies that the average
Speaker 1: person walking down the street has probably never heard of.
Speaker 1: But it's a publicly traded company. I think they manage
Speaker 1: about a hundred and twenty or a hundred and forty
Speaker 1: billion dollars these days. Tell us about Financial Engines and
Speaker 1: how the idea came about? Well, listen, you know everybody
Speaker 1: has a founding story and they obviously get better as
Speaker 1: they're told more often. I thank you so much for
Speaker 1: saying that, because my wife gives me grief all the time.
Speaker 1: How come your stories don't sound anything like the honey
Speaker 1: it's called to get over time, that's it's it's your
Speaker 1: working on you. So so I'll try to I'll try
Speaker 1: to give it to you as I believe it happened.
Speaker 1: I had As I mentioned earlier, I had a phase
Speaker 1: when I had a research slash consulting firm trying to
Speaker 1: help people managing large pension and endowment funds. And after
Speaker 1: I went back to teaching full time, I decided that
Speaker 1: four O one case were for good or ill the
Speaker 1: wave of the future, and there are a whole lot
Speaker 1: of people who needed help. And this is the risk
Speaker 1: of laws past in seventy four or so. When when
Speaker 1: were you coming to the realization that have these four
Speaker 1: own k things are problematic for so many people. Probably
Speaker 1: pretty much in the early nineties. And I went back
Speaker 1: to Stanford, so I had time to work on anything
Speaker 1: I wanted to and in terms of my research, so
Speaker 1: I focused my research on that problem and I was
Speaker 1: writing pieces. I had an early Internet account, back before
Speaker 1: most people knew what it was I was writing little
Speaker 1: programs to put on the Internet for people to use.
Speaker 1: And uh, a friend of mine, Joe Grunfest, the professor
Speaker 1: of the law school at Stanford, said let's have coffee.
Speaker 1: I've got an idea. So we did, and he said,
Speaker 1: you know you're not gonna affect enough people with this work.
Speaker 1: We need to start a firm. And I said, been there,
Speaker 1: done that, No, thanks very much, Hell yeah, tell you what,
Speaker 1: Let's just at least talk to my friend Craig Johnson.
Speaker 1: And Craig had a firm that came he came out
Speaker 1: of the legal side, but they had developed a practice
Speaker 1: specializing in helping people bring ideas to fruition via startups,
Speaker 1: in particular academics ideas. And so Craig and Joe and
Speaker 1: I talked about, well, let's see if we can't set
Speaker 1: up a firm to provide financial advice. Two people in
Speaker 1: four oh one k plans through their employer. So this
Speaker 1: was very much accumulation phase too, use the term I've
Speaker 1: used before, and uh, more or less the rest is history.
Speaker 1: You mentioned Jeff Imagine called Joe had had some contact
Speaker 1: with Jeff and said, I think Jeff would be great
Speaker 1: to lead this effort and UH, so we talked to
Speaker 1: Jeff and I remember I think it was Craig said, well, Jeff,
Speaker 1: I hope you understand that in a year we might
Speaker 1: replace you, you know, as the way these things happened.
Speaker 1: And Jeff said, I can take that chance. So we
Speaker 1: started with Jeff, and then Craig uh brought Ian part time,
Speaker 1: really experienced CFO people, head of engineering to help us
Speaker 1: get started and to help us find people to hire.
Speaker 1: Jeff went out beating the bushes to get venture capital. UM.
Speaker 1: I went along on one or two of those presentations,
Speaker 1: decided it was too brutal for me. But and so
Speaker 1: now that's that's how it all came about. And now
Speaker 1: Financial Engine So they eventually pivot towards managing on an
Speaker 1: institutional basis, and so they're the UM provider of record
Speaker 1: for various companies, substantial companies. UH and it's fairly low
Speaker 1: cost and it's fairly well structured indexes, primarily for corporate
Speaker 1: for a one K plan. Let me say first, I've
Speaker 1: been retired from the firm for quite a while, so
Speaker 1: I don't really know much about what they're doing now. UM,
Speaker 1: but basically we actually went through I think depending on
Speaker 1: how you count four or five business plans um and
Speaker 1: there was we at one point we're on the A
Speaker 1: O L side, we were going to do it directed
Speaker 1: to retail, to call it B two C and all
Speaker 1: the rest of that um. But what we settled down
Speaker 1: providing advice to all the employees in a firm once
Speaker 1: the firm signed up, and then providing management of accounts
Speaker 1: to a subset of the employees who wanted that. And
Speaker 1: certainly my goal, uh and I think that of almost
Speaker 1: everybody in the firm from the start, and I hope
Speaker 1: still is to do it as low enough costs actually
Speaker 1: keep bread on the table and paint a little more
Speaker 1: than bread. And well it's worth well because they've they've
Speaker 1: accumulated a substantial amount of clients and assets, and people
Speaker 1: generally seem to be happy with and and you know,
Speaker 1: we we tried to bring to bear. The whole idea
Speaker 1: was that was what I'd done it my former incarnations
Speaker 1: try to bring financial economics, let's call it broadly, to
Speaker 1: bear on that problem. You know, what we knew, what
Speaker 1: we thought we knew about markets, index funds being very
Speaker 1: attractive investments, et cetera. Trying to help the accumulator, let's
Speaker 1: call it, get some sense of the risk return trade
Speaker 1: offs in terms of if we do this portfolio, then
Speaker 1: the range of things that happened in terms of the
Speaker 1: amount of money would have to buy an annuity. Let's
Speaker 1: say as an example, that retirement is this. If we
Speaker 1: do that, it's that. And trying to give them a
Speaker 1: chance to experiment and find something that makes sense for
Speaker 1: their situation. And and and I we did a lot
Speaker 1: of over the years. Certainly I was involved affirm, did
Speaker 1: a lot of research and some of my early work
Speaker 1: on the decumulation phase was done with Jason Scott and
Speaker 1: John Watson, UH, two PhDs in the research at the firm.
Speaker 1: So you referenced index funds, you worked on the first
Speaker 1: index funds or certainly one of the first index was
Speaker 1: going to say so, so, given given how the world
Speaker 1: has changed, tell us a little bit about what you
Speaker 1: did back then, and then we could fast forward and
Speaker 1: talk about whether index funds are going to eat the world? Okay, Um, certainly, UM,
Speaker 1: the you know I was, I was. I become friends
Speaker 1: with Bill Files at at Wells Fargo Investment Advisors, UM,
Speaker 1: and he had talked to my class. Uh and I
Speaker 1: had of course been pushing the idea of index funds
Speaker 1: or something equivalent, and I had a call out of
Speaker 1: the blue from a young man who had just finished
Speaker 1: an NBA program at Chicago, Chicago, and said, we look,
Speaker 1: you know, I think I've got this right. My my
Speaker 1: dad run owns run Sampson I luggage company, and they
Speaker 1: have to find a manager for the pension fund I
Speaker 1: believe it was. And I learned about the capital asset
Speaker 1: pricing and adel and all, and it seemed to me
Speaker 1: that made sense to just put this in the market somehow.
Speaker 1: And he said, do you know anybody who can do that?
Speaker 1: And I said, well, so I put him in touch
Speaker 1: with Bill Fauss that Wells Fargo, and they had come
Speaker 1: up originally with a scheme in which they had maybe
Speaker 1: five hundred stocks, but they were equal weighted, not in
Speaker 1: market cap weights, which had I known about it, I
Speaker 1: would have told him instantly was a really dumb idea.
Speaker 1: But why do you why do you say that that's interesting? Well,
Speaker 1: because in the first place, it's not representative the market,
Speaker 1: it's not consistent with the capital asser pricing, right. It
Speaker 1: involves all kinds of turnment over to balance everything short
Speaker 1: um and uh as opposed to doing an annual semi
Speaker 1: annual rebalance. Yeah or not. Well, with a market based portfolio,
Speaker 1: you only rebalance for new issues and things of that sort.
Speaker 1: So um, you know, if it's broad enough. So fortunately
Speaker 1: that idea that somebody all was far ago figured that out,
Speaker 1: and so I believe that was their first implementation. Now
Speaker 1: you mentioned the first, there was work going on at
Speaker 1: a bank in Chicago. I think Jack Trayner was involved
Speaker 1: in that. And um there was also a venture that
Speaker 1: I was supposed to be on the board of the
Speaker 1: Teamsters Union wanted to do an index fund and we
Speaker 1: were gonna establish but that fell through for reasons having
Speaker 1: to do with the Teamsters Union in San Francisco. So um,
Speaker 1: so I think Wells was, if not the first, certainly
Speaker 1: one of the very first institutional index funds. Jack Vogel
Speaker 1: of course came along on the on the well I
Speaker 1: would call it personal side rather than institution. What it
Speaker 1: was a mutual fund at the time, but it was
Speaker 1: certainly an idea that was that was in the in
Speaker 1: the ether because of the academic work. And so what
Speaker 1: did you do with the Wells Fargo? Did you help
Speaker 1: them put that together or was it just and by
Speaker 1: the way, who was the PhD from Chicago with Samsonite?
Speaker 1: Do you remember? The name was actually an Nba and
Speaker 1: I don't remember his name, and I apologize for that.
Speaker 1: Could have been Samsonite, I don't know. I've heard, but
Speaker 1: I've heard the name sam Unite from other people telling
Speaker 1: the story, and I don't remember if it was David
Speaker 1: Booth or someone else. You know, the person who caught
Speaker 1: me was actually the son I believe of the owner
Speaker 1: founder of Samsonite, where he who is classy, had taken
Speaker 1: I don't know that Chicago could have been Jane Farma
Speaker 1: but um, but in any event, um, I'm sorry to
Speaker 1: go back to the question. We were so so from
Speaker 1: making the introduction to Wells Fargo, what what else was
Speaker 1: your role in the development of that initial index fund
Speaker 1: moving them towards market cap waiting. Is that one of
Speaker 1: the contributions you had or did they find it on
Speaker 1: their own right? Well, they I think found that in
Speaker 1: that particular instance. I don't believe I was consulting with them.
Speaker 1: Then I did consult subsequently for quite a while, and uh,
Speaker 1: we did all sorts of things. I remember. Um, we
Speaker 1: developed a yield tilt fund. And there's an argument that
Speaker 1: could be made, and I made it in my textbook,
Speaker 1: and my colleague Bob Blitzenberger and Kristen Ramaswamy, then one
Speaker 1: of our PhD students, did quite a bit of work
Speaker 1: on this that you know, if you have differential taxation
Speaker 1: of dividends and gains, as we did, and at the
Speaker 1: time the differential was big um, then you can imagine
Speaker 1: a sort of a sorting out where it pays individuals
Speaker 1: to and non acxible entities to tilt away from market
Speaker 1: proportions towards higher yield because you don't pay taxes and
Speaker 1: their price presumably to reflect their inferior tax position, and
Speaker 1: for people who pay taxes to tilt in the other direction.
Speaker 1: So you can make that argument. And there were academic
Speaker 1: papers and then papers from Miller and Myron Scholes saying
Speaker 1: that's not true or the evidence doesn't support it. And
Speaker 1: even so we brought to market this yield tilt fund.
Speaker 1: It was an institutional fund that had a dividend tilt,
Speaker 1: if you will, because of the tax differential. Yeah, that
Speaker 1: was it was. It was an equilibrium argument in a
Speaker 1: society with differential taxations. So so, really, do I get
Speaker 1: to credit you for creating smart data before any No,
Speaker 1: we'll talk about that separately, but but I will tell
Speaker 1: you the dividend tilt came out, and high yield stocks
Speaker 1: just relative to other stocks, just went into a tailspin.
Speaker 1: I mean, it was one of those periods when I
Speaker 1: call them value. We'd call them value stocks now maybe,
Speaker 1: but they just got creamed by growth stocks for whatever reason.
Speaker 1: And the client we didn't have many clients, and the
Speaker 1: clients we had sort of started departing. Finally, somebody turned
Speaker 1: out the light when they closed the door, and the
Speaker 1: yield tilt TONU funds did not last very long. Um,
Speaker 1: but I suppose that was one of the first institutional
Speaker 1: quote value funds. So so what years is? Are we
Speaker 1: talking seventies or eighties? We're talking I I think I'm
Speaker 1: guessing eighties, but don't early eighties, but don't hold me
Speaker 1: to that. So with that point, technology were starting to
Speaker 1: real nobody really wants to look at value. Yeah. I
Speaker 1: actually some while ago, for some other reason, I looked
Speaker 1: that up and I couldn't find any traces of it
Speaker 1: on the internet, so it was buried. But do you
Speaker 1: want to talk smart d sure. Let's let's let's talk
Speaker 1: about the idea of creating indicas by using methodologies other
Speaker 1: than market capitalization. First, I think I've been I'm in
Speaker 1: print somewhere from saying this in public. The term smart
Speaker 1: beta makes me sick. I mean beta is it's We
Speaker 1: defined it in finance for decades as a measure of
Speaker 1: the extent to which a stock or something moves with
Speaker 1: the market. That's the definition. Whether it's smarter dumb is
Speaker 1: a relevant. Now. What all that is is what we've
Speaker 1: known about and written about for years called factor tilts.
Speaker 1: So you have a multi factor model of Fama, French
Speaker 1: and everything value. So there's a factor model, and you tilt.
Speaker 1: You hold more exposure to yield, let's say, or to
Speaker 1: value than to growth. You hold more exposure to small
Speaker 1: relative to large, and so all of those arguments say,
Speaker 1: you know, there are two classes of argument for those strategies.
Speaker 1: One is the market screws up, you know, and you
Speaker 1: know there are dumb investors who think growth stocks are
Speaker 1: so wonderful they overpriced them. And there are smart investors
Speaker 1: who know that and underweight those growth stocks. And then
Speaker 1: there are there are there's myself and my friends who
Speaker 1: are in the middle, you know, and meaning a balanced portfolio.
Speaker 1: By it all that the market doesn't screw up, and
Speaker 1: if it does, you'll never figure out how. And so
Speaker 1: so basically that argument, and this gets to a very
Speaker 1: simple argument I wrote about years ago. Um, if you
Speaker 1: take all the people who own shares in the US market,
Speaker 1: let's say put them in one room, and you say,
Speaker 1: here are the indexers broad based market index you know,
Speaker 1: before costs the market. If the market does ten percent,
Speaker 1: all every one of them will do ten percent. The
Speaker 1: rest of the room, the active managers. I don't care
Speaker 1: if they're smart beta yield till you know whatever, some
Speaker 1: of them will do better. Someone will do. They have
Speaker 1: to earn before costs the same that the other guys do,
Speaker 1: and after costs they're gonna earn less. Now that doesn't
Speaker 1: mean that some of them may not routinely do better
Speaker 1: than the indexers. But if so, somebody's routinely doing worse.
Speaker 1: So there's you know, that that story they're smart, that's
Speaker 1: the smart beta. There's you know, sort of dumb, that's
Speaker 1: we indexers. And then there's the really dumb you know,
Speaker 1: who are the people on the other side. Of the
Speaker 1: trades with the smart beta people. And of course one
Speaker 1: presumes eventually that don't really dumb people will say I
Speaker 1: think maybe I'll buy an index fund, and then the
Speaker 1: game's over for smart PA. But you know those factors, yes,
Speaker 1: do we know? We know that there are extended periods
Speaker 1: when value beats both, and they're extended periods when growth
Speaker 1: beats value um. And if you are oppression and can
Speaker 1: tell which which is coming, then tilt away and have
Speaker 1: a good time and you'll be you'll be very wealthy.
Speaker 1: There's very little evidence that people can tell in advance
Speaker 1: what's coming next, and over time it's pretty much averages
Speaker 1: out um. And but we've got a lot of data,
Speaker 1: we've got really fast computers, we've got a lot of
Speaker 1: smart people. We've got a lot of good marketing people.
Speaker 1: So you're gonna be hearing about this. And my friend
Speaker 1: Bill Files that I mentioned earlier, one said he never
Speaker 1: met a back test. He didn't like, you know, somebody
Speaker 1: will come along. If we had just done this the
Speaker 1: last ten years, you would be so rich. So someone
Speaker 1: wants I don't know who who it was described it
Speaker 1: as smart marketing. Yes, that so I just one caveat.
Speaker 1: There's a very subtle argument which a friend of mine,
Speaker 1: I won't bother you with his name. It's an academic
Speaker 1: who works in the industry, you know, has made that.
Speaker 1: My arithmetic argument which I referenced, which was arithmetic of
Speaker 1: active management was the title of the piece. Um. Well,
Speaker 1: but we smart institutions know when to buy a new issue.
Speaker 1: You know, there are issues coming and going, bond repurchases,
Speaker 1: expirations of bondsman maturation, what have you. And some of
Speaker 1: us active managers can play that game and other indexers
Speaker 1: can't do it. So maybe and that's the claim for
Speaker 1: how they managed to outperform. Yeah, and there's and there's
Speaker 1: and there's another argument that you sometimes hear, well, the
Speaker 1: smart active managers do better than the market, and yes,
Speaker 1: somebody has to be doing worse, and it's the dumb
Speaker 1: individual investors. Um and and yet when we look at
Speaker 1: the league tables for how well active mutual fund managers
Speaker 1: have done in a good year, the beat their benchmark.
Speaker 1: But in most years, right, it's always somebody different from
Speaker 1: year to year. There the people who wait a minute,
Speaker 1: I know about Warren Buffett, and I know, but you
Speaker 1: hear about the outliers, but their outliers and the vast majority.
Speaker 1: Van Goard has done a ton of studies. Yeah, yeah,
Speaker 1: you know. If I bet you know this. If I
Speaker 1: bet with you on a coin flip, and I always
Speaker 1: call heads, there will be some periods of ten ten
Speaker 1: in a row, I won't win them all, but I'll
Speaker 1: win more than half. And that's when you go open
Speaker 1: a hedge fund. That's exactly that, and then when I lose,
Speaker 1: I close it and I opened a new hitge fund.
Speaker 1: We know how that works. It's amazing that the academic
Speaker 1: literature on this is pretty unambiguous. People can debate around
Speaker 1: the fringes, but the concept of now there's a whole
Speaker 1: behavioral side of it, and people um Meyer Stateman at
Speaker 1: Santa Clara talks about people have an expressive need with
Speaker 1: their portfolio. It's not purely utilitarian. What do I need
Speaker 1: to do with my By the way, his mayor's work,
Speaker 1: you know, I haven't seen him for a while, but
Speaker 1: he's a very smart guy, and I his work is excellent,
Speaker 1: and and his mentor was Peter Bernstein. So to bring
Speaker 1: that back full circle, I didn't realize it was that close.
Speaker 1: I know they were, they were close. So so we
Speaker 1: we mentioned the consulting with Wells Fargo. You also consulted
Speaker 1: with Merrill Lynch in the nineties seventies. These are are
Speaker 1: companies that have gone through enormous changes over the passage decades.
Speaker 1: Are you surprised how how that side of the business
Speaker 1: has evolved. These big firms are not what they once were.
Speaker 1: How do you how do you see that? Well, let
Speaker 1: me differentiate a little. The Meryl work with Jack Trayner
Speaker 1: and Gil Hammer and others was we were basically providing
Speaker 1: services for institutional money managers like pension funds, etcetera. We
Speaker 1: did the first beta book where you could look up
Speaker 1: the beta of a stock. We did the some of
Speaker 1: the first performance measurement and analytic performance measurement. So this
Speaker 1: was Maryland's providing this service to large clients of theirs. UM.
Speaker 1: So that was all on the performance measurement, if you
Speaker 1: will side. And it's not like today where anyone could
Speaker 1: log into a Bloomberg terminal conscious number. Back then it
Speaker 1: was serious computer power in order to do that, and
Speaker 1: nobody had to get it on Google, Yahoo. Do you
Speaker 1: name it UM and UM and then UM. The Wells work,
Speaker 1: they were money managers, so it was index funds and
Speaker 1: things of that sort. So so they're very different gigs,
Speaker 1: if you will. So let's talk a little bit about
Speaker 1: the sharp ratio, which is something that comes up frequently.
Speaker 1: I hear that from people all the time. But what's
Speaker 1: the fund sharp ratio? You've written that the sharp ratio
Speaker 1: has been misused by a lot of the investing public.
Speaker 1: So let's start with explain to us exactly what the
Speaker 1: sharp ratio is. First thing, I'm not as emaniacal as
Speaker 1: you might imagine. I called it, and I still think
Speaker 1: it's a better term reward to variability ratio, reward to variability,
Speaker 1: and I'll expand on that. Somebody else, and I don't
Speaker 1: really know exactly who it was, started calling at the
Speaker 1: sharp ratio, and the name stuck so so well, it
Speaker 1: certainly rolls off. It's uneasier than rewards a variability ratio.
Speaker 1: And I can tell you my wife's an artist, so
Speaker 1: she's not deep into finance. Let us say, and we're
Speaker 1: watching a sitcom, or no, it's not a sitcom, it's
Speaker 1: a drama billions, which is sure hitch fund manager, etcetera,
Speaker 1: which is sort of my guilty pleasure that that show,
Speaker 1: and they're sitting at the table and one of the
Speaker 1: people that's beend and saying, well, you know, we're losing
Speaker 1: customers because our sharp ratio is low. And my wife said,
Speaker 1: what yes, yes, So so I'm glad they didn't say
Speaker 1: reward to variability. Um. They might claim to fame. Um.
Speaker 1: The it's kind of the ant Well, let me go
Speaker 1: back the original context and a parallels some work of
Speaker 1: Jack Trayner's, which Jack went in a different direction. But
Speaker 1: the idea was, how do you evaluate X an ex
Speaker 1: post or anticipate X andy, but let's talk about ex
Speaker 1: post how well you've done. And the idea was, and
Speaker 1: I won't speak for Jack, my idea was to say, well,
Speaker 1: the expected return if we're looking forward, or the average
Speaker 1: return if we're looking backward, is a measure of goodness.
Speaker 1: That's a good thing, but there's also an issue. What
Speaker 1: was the journey like? So over the place very bill
Speaker 1: and and so the idea was, what did you get
Speaker 1: an expected return per unit of risk that you took
Speaker 1: or will take if it's forward looking. And my original
Speaker 1: setting was this is for a whole portfolio. And so
Speaker 1: the idea was you compare your situation with treasury bills.
Speaker 1: Let's call it the riskless asset. So in the numerator
Speaker 1: the top of the fraction you put my average return
Speaker 1: over the treasury bill, how much did I earn over
Speaker 1: for taking risk? And in the bottom you put how
Speaker 1: much risk did I take? And the idea is the
Speaker 1: more return you've got, the more reward per unit of variability,
Speaker 1: the better it was, and more reward relative to variability,
Speaker 1: and and you know, and so we'll talk about another
Speaker 1: issue with it, but stun the face of it, if
Speaker 1: you had to choose one number to evaluate some an
Speaker 1: investment prospectively or you know, after the fact um retrospectively,
Speaker 1: then you know it's not a bad number. That's a
Speaker 1: pretty good number. We've got computers now, we don't need
Speaker 1: to restrict ourselves to one in number. Um So, so
Speaker 1: then you go to, well, what if this is not
Speaker 1: my whole portfolio but a piece of it. If it's
Speaker 1: one fund and I've got twenty, or if it's one
Speaker 1: investment manager my pension fund, I've got a hundred, and
Speaker 1: this is not the right measure for that. Uh So,
Speaker 1: how do people how are people misusing it? Well, so
Speaker 1: so let me just finish that thought. So what you
Speaker 1: can do is come up with a benchmark. So this
Speaker 1: is a growth manager, I'll get a growth index fund
Speaker 1: as a benchmark, and in the numeraator I'll put on average,
Speaker 1: how did this fund do minus how the index fund did,
Speaker 1: and in the bottom I'll put the variability between the two.
Speaker 1: You know, there it's the variability of the difference. But again,
Speaker 1: so that's another measure nobody's ever kind of So that's alpha,
Speaker 1: the differential versus over over residual risk. Let's call it
Speaker 1: something like that. And again there are variations on that theme.
Speaker 1: Nobody's ever given a name to those kinds of measures.
Speaker 1: But again, but the only case in which for a
Speaker 1: single manager in a multi managed portfolio the sharp ratio
Speaker 1: maybe applicable is in a hedge fund that has zero beta.
Speaker 1: Has zero beta. Now, the other measure was Jack Trainer used.
Speaker 1: It was the same in the numerator, but the dominator
Speaker 1: had beta as opposed to as opposed to total risk,
Speaker 1: so it had in effect the part of the risk
Speaker 1: that's due to the market. And and there are some
Speaker 1: arguments in terms of capital asset pricing that that can
Speaker 1: be helpful, But in terms of just you say, look,
Speaker 1: here's my whole portfolio in the last twenty years, my
Speaker 1: average expercent and the standard deviation was why and the
Speaker 1: Treasury bill was z. You know, what do you think?
Speaker 1: And and I can compare that, say with investing in
Speaker 1: let's say a total stock market fund, if that's your comparison,
Speaker 1: and say which sharp ratio is higher? So how are
Speaker 1: people abusing the ratio? Let me count? Let me count
Speaker 1: the ways, because because I always see it in hedge funds,
Speaker 1: I always see it in back tests, I always see it.
Speaker 1: In fact, there are some people who hedge funds inhangements
Speaker 1: because if they're really truly hedged dent heads almost none are,
Speaker 1: of course none are. Most of them have some beta
Speaker 1: relative the stocks and some data relative the bonds, so
Speaker 1: you need to do a little regression analysis and do
Speaker 1: something more sophisticated. So the sharp ratio just oversimplifies what
Speaker 1: the risk of a hedge fund an unhedged hedge fund
Speaker 1: actually is. Yes, it's it's it's amazing because of all
Speaker 1: the metrics we see, it's the one that it seems
Speaker 1: to be the first question. It's easy. I mean, you know,
Speaker 1: it's it's easy, and uh, you know it's not without
Speaker 1: information because it has a good thing in the numerator
Speaker 1: and has a bad thing in the denominator. But you know,
Speaker 1: it's just not as sophisticated as it should be in
Speaker 1: a lot of applications. So so given all of that,
Speaker 1: how should the average investor think about risk adjusted returns? Um?
Speaker 1: Maybe not think about it? Do we do? We do?
Speaker 1: You are you suggesting we overemphasize risk adjusted returns? Well? Yeah,
Speaker 1: I think the average investor should all broad very broad
Speaker 1: based index funds to begin with. And you should think
Speaker 1: about both retrospectively and prospectively what on average you might
Speaker 1: get from this or did get, and how much variation
Speaker 1: there was, because that tells you something about how bad
Speaker 1: it could be the two together. Um, And so for
Speaker 1: that kind of a strategy of sharp ratio is not irrelevant.
Speaker 1: It's worth looking at. But don't you know, don't break
Speaker 1: it down into pieces, just take the whole thing. Quite
Speaker 1: quite fascinating. I mean, my if you want to know
Speaker 1: what you know, we mentioned my current work, UM, my
Speaker 1: ideal portfolio risky portfolio for an individual retiree. Certainly, UM,
Speaker 1: who isn't desirous of taking a whole lot of risk
Speaker 1: is a portfolio maybe four different index funds, US non
Speaker 1: US bonds and stocks in more or less market cap proportions,
Speaker 1: a global bond stock portfolio. So now that's interesting. You
Speaker 1: mentioned in market cap proportions because there are so many
Speaker 1: I want to miss I understand you correctly. There are
Speaker 1: so many more bonds. The bond market is so much
Speaker 1: bigger than the stock market, don't Yes and no if
Speaker 1: you look worldwide, huh, it's I don't have the current figures.
Speaker 1: I should have them. Um, but it's somewhere around bonds
Speaker 1: and um. So that would be thought of as a
Speaker 1: fairly risk averse portfolio. Well again, as as I mentioned,
Speaker 1: I'm focusing on retirees, and very few retirees seem to
Speaker 1: have the stomach for much more risk than that. And
Speaker 1: then I would mix that for those who want less
Speaker 1: risk with My preferred vehicle would be tips, because you know,
Speaker 1: is a real danger for a retiree. That's the biggest
Speaker 1: risk is that's there. They have two investments, one of
Speaker 1: which may require one of which the world bond stock portfolio.
Speaker 1: I continue to badge your my friends at some of
Speaker 1: the index fund companies to create it a single fund.
Speaker 1: So I don't have to do it with four different
Speaker 1: So when you're looking world stuck, you're doing emerging market
Speaker 1: developed x US and then what else. Well, that gets
Speaker 1: into the the nuances of indexing and prices and such.
Speaker 1: But yeah, I mean you certainly mean the funds that
Speaker 1: I happen to use our Vanguard funds, and we can't
Speaker 1: get Jack Boglar Bill McNab to create an index for you.
Speaker 1: I haven't I haven't seen Jack or Bill for a while,
Speaker 1: but I've been dealing with some of the others, including
Speaker 1: one of our former PhD students, and um, you know,
Speaker 1: they all say, yeah, it's a good idea, but I
Speaker 1: think one of the counter arguments, well, you can do it,
Speaker 1: and you do do it, but but it's a pain.
Speaker 1: It takes it takes three funds instead of takes four
Speaker 1: four funds. Yeah, so we we talked earlier about the
Speaker 1: ariskle laws and the rise of four oh one K.
Speaker 1: Tell us your perspective on how things have evolved from
Speaker 1: defined benefits and pensions to define contribution and self directed
Speaker 1: retirement funds. Well, it's when when that trend first started.
Speaker 1: I mean, as as you know well, and many do
Speaker 1: borrow and k was never intended to be your main
Speaker 1: retirement vehicle. It was supposed to be a supplement precisely,
Speaker 1: and so for good or bad reasons, we we moved
Speaker 1: in the private sector. Now, in the public sector we
Speaker 1: still have a preponderance, although it's changing slowly, of defined benefit.
Speaker 1: And that's an area which I've done some work also
Speaker 1: over the years and and involved with a project that's
Speaker 1: working on it to this day at Stanford. And that's
Speaker 1: a really serious problem of its own. But let's deal
Speaker 1: with the private world. Um, it's I mean, freedom is great.
Speaker 1: It's wonderful. You can decide how much to say, you
Speaker 1: can decide how to invest it. When you retire, you
Speaker 1: can decide what to do with the money, whether invested
Speaker 1: and newitize it. I mean, you've got this world of
Speaker 1: choice now constrained when you're retired, you're not constrained at all.
Speaker 1: Generally when you're working, you're constrained by the menu that
Speaker 1: your employer offers you. But um, but it's I mean,
Speaker 1: the good news is you've got you can choose lots
Speaker 1: of different things. The bad news is you can choose
Speaker 1: lots of different things. And I think it's incumbent upon
Speaker 1: employers do at least try to limit your choice set
Speaker 1: within the forellen Care four three B plan two sensible investments,
Speaker 1: and to provide some sort of assistance so that you
Speaker 1: to help you make informed choices. Now that's self serving.
Speaker 1: I'm not involved with the financial engines anymore, but um,
Speaker 1: but it's it's it's very frightening, and I think we
Speaker 1: have evidence. Just as we have evidence in the public
Speaker 1: sector that employers in the public sector are not sufficiently
Speaker 1: funding there to find benefit plans, we have evidence that many,
Speaker 1: many individuals are not sufficiently funding there for owen K
Speaker 1: four three B plans. Some don't even have access to any.
Speaker 1: And then it's sort of hard to know what people
Speaker 1: are doing with that money when they retire. But my
Speaker 1: guess is it's not a pretty picture. No, to say
Speaker 1: the least. Um. You mentioned pension funds. I want to
Speaker 1: run a pet theory by you that I that I
Speaker 1: have a lot of pension funds have over the past
Speaker 1: decade or two really ramped up their exposure to hedge
Speaker 1: funds and the only these the only explanation I could
Speaker 1: find is well, we have this expected return for bonds,
Speaker 1: and we have that expected return for stocks. But look,
Speaker 1: I expected return for hedge funds is so much greater.
Speaker 1: Is that remotely you've got it? And and as a
Speaker 1: matter of fact, it's some research out of the group
Speaker 1: in Europe. But but on US pension funds you have
Speaker 1: of this bizarre tail wagging the dog. The way public
Speaker 1: pension funds work is that the actuary comes up with quote,
Speaker 1: an expected rate of return for the fund and then
Speaker 1: does calculations using that. The assumption that you use that
Speaker 1: to discount everything you've got, including the contributions the state
Speaker 1: has to make too. Well, no, I'm talking about just yes,
Speaker 1: including contributions, but future contributions. But if if you just ask, well,
Speaker 1: what's what are the assets worth? Well, we know that
Speaker 1: we have market values. What are the liabilities worth? I
Speaker 1: have promised Joe and the police force that in five
Speaker 1: years he'll retire and he'll get X dollars per month
Speaker 1: till he dies. What's that worth? Well? Any economists would say, well,
Speaker 1: you get the actuarial tables, you figure out life expectancy,
Speaker 1: and then you discount those payments at the treasure rate.
Speaker 1: You claim you're going to make them their riskless, their
Speaker 1: bonds and they should be valued like bonds. No, the
Speaker 1: state actuaries take those claims, those payments, discount them at
Speaker 1: the expected return of the fund, which was seven and
Speaker 1: a half percent or so, which is it seems to
Speaker 1: be made up. It's just so who came up with
Speaker 1: that number? Okay? Well, but then I want to go
Speaker 1: I want to complete the thought to your Okay, So
Speaker 1: you know, the politicians, if you will, and maybe the unions,
Speaker 1: and maybe the people in the office of the Chief
Speaker 1: Actuary and the people running the pension fund are heavily
Speaker 1: pressured to make those liabilities values as small as possible
Speaker 1: so that they look good relative to the value of
Speaker 1: the assets. They value the assets at market. So that's fine,
Speaker 1: but the value of the liability. So let's see, if
Speaker 1: we increase our expected return, we can discount the promised
Speaker 1: payments at a higher rate, and their present value will
Speaker 1: be lower and our funding will be better and there
Speaker 1: by reducing how much money and precisely so so and
Speaker 1: that's exactly, and you've raised the point. So we really
Speaker 1: need to get that expected return. How how can we
Speaker 1: do it? Well, private equity, hedge funds, etcetera. We can
Speaker 1: take what we expect from the stock market and had
Speaker 1: three hundred basis points three more, because after all, their
Speaker 1: their golden instruments, and that'll get our expected return up.
Speaker 1: And as I mentioned as a study, I'm blocking now
Speaker 1: on the authors where they very carefully looked at pension
Speaker 1: funds in great detail, and you can see it. You
Speaker 1: can see them they're putting more money in those asset classes,
Speaker 1: not probably because they think they're wonderful or maybe they
Speaker 1: don't even think they can get three hundred basis points
Speaker 1: more than stocks net net, but because that will enable
Speaker 1: them to cook the books and make the situation look
Speaker 1: even better then it does now, which is a lot
Speaker 1: better than it really is, and put a plug in. Uh.
Speaker 1: There's a project at Stanford called pension tracker dot org
Speaker 1: where we go through this process for all the major
Speaker 1: pension funds in the country really and and city pension
Speaker 1: and county PENSI pension tracker dot org, and we compute
Speaker 1: not only quote actual aerial values, but also what we
Speaker 1: call market values where we try to correct for this.
Speaker 1: So so the United States pension plans public pension plans
Speaker 1: is a built on an assumption that's false, tweaked to
Speaker 1: show better expected returns than anybody should reasonably expect. Well,
Speaker 1: I won't go that far, but to show I mean,
Speaker 1: I'm sitting here with will Sharp, Nobel laureate and inventor
Speaker 1: of cap in the short ratio, and you've essentially made
Speaker 1: a case for fraud. These guys are defrauding the public
Speaker 1: and the taxpayer. I'm gonna draw that conclusion. Hey, I'm
Speaker 1: not a lawyer, so I'm not going to talk about fraud.
Speaker 1: And b I'm sure there are plenty of people within
Speaker 1: these organizations, let's say the state pension funds, who honestly
Speaker 1: believe that you really should plan to on average get
Speaker 1: a bonus of three basis points net from hedge funds
Speaker 1: and private equity. You know if you I don't happen
Speaker 1: to be among them. If this was the nineties, I
Speaker 1: could say, hey, there have been a lot of funds
Speaker 1: doing really well, and maybe you can make a good
Speaker 1: faith argument for that. But we have two decades of
Speaker 1: significant especially as three trillion dollars have flown into hedge
Speaker 1: funds from a tiny percentage of that UH and the
Speaker 1: number of hedge funds have scaled up ten x. Maybe
Speaker 1: at one point in time when there were a small
Speaker 1: number of hedge funds managing a small amount of money
Speaker 1: that alpha was legitimate, but at this point that's just
Speaker 1: a fantasy. Well, you know, there have been very, very
Speaker 1: careful academic studies. It's hard to do, it's hard to
Speaker 1: get the data. But my read, at least of some
Speaker 1: of the more recent ones is that if you can
Speaker 1: get in the top x per cent, and I say
Speaker 1: this is given the fact that we're sitting in the
Speaker 1: offices of one of these, and if you can get in,
Speaker 1: then maybe you can get an edge, probably not three
Speaker 1: D basis points, but not an edge. Um. But to
Speaker 1: be perfectly frank, the public pension funds can't get in
Speaker 1: a lot of these because they don't want the disclosures.
Speaker 1: So you know, but again I'm not I consulted with
Speaker 1: CalPERS for many, many years on risk analysis and performance analysis,
Speaker 1: and there are very good people and and a lot
Speaker 1: of these organizations, and some of them, I'm sure believe
Speaker 1: that that's true. But I don't believe it's true. And
Speaker 1: I think it's an unfortunate thing. And I think that
Speaker 1: the problem and you can look at the statistics on
Speaker 1: our on our websites, but um, it's it's it's crisis
Speaker 1: proportions and and footnote to Caliper's two years ago, they
Speaker 1: tossed out all their hedge funds and moved closer to
Speaker 1: a a more bill sharp type of investing strategy. So
Speaker 1: and not that it was a lot of money. It
Speaker 1: was I want to say, two or four billion dollars
Speaker 1: billion here, billion there. Eventually, while it's real money, it
Speaker 1: starts there, Well, what are they these days? Two seven years?
Speaker 1: I'm not some un godly You can find it on
Speaker 1: the site, so give us that that don't pension tracker
Speaker 1: dot org, pension tracker dot all. You know, I could
Speaker 1: talk to you about this stuff for all, for hours
Speaker 1: and hours. I have my favorite questions I want to
Speaker 1: get to. Before I get to them, I have to
Speaker 1: ask you about long term capital management. Since were talking
Speaker 1: about hedge funds, you got sort of an interesting perspective
Speaker 1: on what happened there. Tell us about it. Let me
Speaker 1: tell you a pre story. I worked with them with
Speaker 1: a private family um that actually was one of the
Speaker 1: early investors in long term capital. And there came a
Speaker 1: point at which long Term Capital said, and we were
Speaker 1: talking to Byron Chules who was involved there. Um, well
Speaker 1: we're giving you and others all your money back, and
Speaker 1: we said, we don't want our money back. We made
Speaker 1: a lot of money, you've been doing a great job, etcetera.
Speaker 1: And he said, no, I'm sorry, but we're cutting back
Speaker 1: on clients, were men at, etcetera. So reluctantly we took
Speaker 1: our money back and then everything broke loose. Um long
Speaker 1: term capital was You know, it's very hard to tell
Speaker 1: from the outside, but I take it just the simple
Speaker 1: version is that leverage can make you a lot of money,
Speaker 1: and it can lose you a lot of money. And
Speaker 1: there sophisticated and unsophisticated ways of getting leverage, but but
Speaker 1: they all have the same If you're really smart, they
Speaker 1: can make you a lot of money, and it's slightly
Speaker 1: more probable than they will lose you a lot of money.
Speaker 1: And they were running a hundred x or so. Is
Speaker 1: that different people of computer, different numbers, but I've heard
Speaker 1: thirty anyway, But it was way up there, and it
Speaker 1: was done in very convoluted, sophisticated ways. It wasn't just
Speaker 1: a matter of how much money have you barred from
Speaker 1: the bank. So they had all sorts of complex positions.
Speaker 1: And I'll tell you, you know, both the academics there
Speaker 1: and the practitioners, and I've known some of each. We're
Speaker 1: about as smart as you can get. And why it happened,
Speaker 1: who knows, But um, it's certainly certainly tarnished a lot
Speaker 1: of a lot of reputations. You know, it's funny you
Speaker 1: mentioned a lot of smart people. That was the title
Speaker 1: for lon Stein book when Genius failed. There was a
Speaker 1: tremendous amount of intellectual capital there and not enough appreciation
Speaker 1: for um. I want to I keep want to call
Speaker 1: it the sharp ratio, but not enough term recognition of
Speaker 1: the potential risk of all that level. From what I understand,
Speaker 1: their risk models were very complicated and very sophisticated, as
Speaker 1: you might well imagine, But sometimes simple is better than complicated.
Speaker 1: And you know, who knows what the motivations of any
Speaker 1: of the partners or employees might have been. But there
Speaker 1: are times when you know, if you think you've got
Speaker 1: an edge to take a gamble knowing you might lose,
Speaker 1: and uh, at thirty X there is not a lot
Speaker 1: of room for that's right, And and so maybe maybe
Speaker 1: they knew what chance they were taking. I don't know.
Speaker 1: We have been speaking to Bill Sharp of Stanford University,
Speaker 1: the capital asset pricing model, the sharp ratio. If you
Speaker 1: enjoy this conversation, be sure and check out the podcast extras,
Speaker 1: where we keep the tape rolling and continue to talk
Speaker 1: about all things risk, uh and return related. Check out
Speaker 1: my daily column on Bloomberg View dot com or follow
Speaker 1: me on Twitter at Ritolts. I'm Barry Hults. You're listening
Speaker 1: to Masters in Business on Bloomberg Radio. Welcome to the podcast.
Speaker 1: Thank you Bill so much for being so generous with
Speaker 1: your time. This is really endlessly fascinating, to great pleasure.
Speaker 1: I'm having a good time. I'm so glad to hear that. Um.
Speaker 1: So let's let's talk about, um, some of the standard
Speaker 1: questions I ask all of my guests, and and these
Speaker 1: are these are where I really get to learn, um
Speaker 1: about somebody in ways that perhaps they the public doesn't
Speaker 1: necessarily know about them. So what's the most important thing
Speaker 1: about you and your background that people don't know? Oh? My,
Speaker 1: that that that's difficult? Um, all right, obvious. Since we're
Speaker 1: talking finance, I'll tell you a financial story. UM. When
Speaker 1: I was an undergraduate, I took I was an economics mansion,
Speaker 1: but I took a course the beginning course and investments
Speaker 1: from a wonderful person men named John Clynden and and
Speaker 1: it was a very traditional finance course. And I was
Speaker 1: a junior, I think, and I said, well, you know,
Speaker 1: this is pretty good. And I had five hundred dollars,
Speaker 1: which at the time was a lot of money which
Speaker 1: I had saved up. I worked in garages and service
Speaker 1: stations and such to buy a car. And and at
Speaker 1: that time you could buy a car for five hundred dollars,
Speaker 1: but I, for various reasons, I wasn't going to buy
Speaker 1: the car for a few months. So I thought, well,
Speaker 1: I'll do a little investment, and so I did my
Speaker 1: securities research as I was told, and found a company,
Speaker 1: I think it was Learner Stores or Learner Brothers or
Speaker 1: something sure as a women's clothing store, and they were
Speaker 1: in new management, expansion to shopping models, whatever. And of course,
Speaker 1: not knowing at the time that things of that sort
Speaker 1: we're supposed to be incorporated in the price, I and
Speaker 1: went down to my local Meryll Lynch office and bought
Speaker 1: five hundred dollars worth of Learner Brothers Stores. Well you know,
Speaker 1: of course, what happened. In three months, It became three
Speaker 1: hundred dollars and so I had a work all summer
Speaker 1: before I could buy my car. And maybe that was
Speaker 1: the beginning of my suspicion that markets were efficient. I'm
Speaker 1: not sure, but it certainly you told me that that
Speaker 1: a career and investments was as a practitioner at least
Speaker 1: was not for me. So you've mentioned a bunch of mentors.
Speaker 1: Tell us who who are Who are the people that
Speaker 1: really mentored your thought process and your career. Well, it
Speaker 1: always comes down to two I've mentioned in in our conversations,
Speaker 1: um J. Fred Weston. Fred Weston was an economist from Chicago,
Speaker 1: University of Chicago in the finance department in the Business School,
Speaker 1: and I was his one of his research assistants as
Speaker 1: an undergraduate UM And then when I took my PhD,
Speaker 1: I found one of the five fields could be in finance.
Speaker 1: So even it was the business school, one of my
Speaker 1: five fields from my economics PhD was with Fred, and
Speaker 1: Fred and I were close and he was a huge influence.
Speaker 1: And then the other main influence was Armin Auchin, who
Speaker 1: was a micro economist, brilliant, quixotic, um, quirky um, who
Speaker 1: from whom I took micro economics. UH the beginning of
Speaker 1: the PhD program. I guess I took in the NBA
Speaker 1: program who taught me to think like an economist? And
Speaker 1: um So in many ways the C A P M.
Speaker 1: I can trace to those two people. Who else affected
Speaker 1: your thought process about investing? What? What investors have influenced
Speaker 1: how you look at the world of of pricing and returns? Well,
Speaker 1: I wouldn't say any investors, particularly have academics. I came
Speaker 1: at from an academic viewpoint, and again, um I was
Speaker 1: bringing economics into finance, as was Fred and as were
Speaker 1: some others. But in very early days, and in a sense,
Speaker 1: we were bringing uncertainty in the economics. Most economic theory
Speaker 1: was in a world of certainty, where you knew when
Speaker 1: you put these inputs in, you're gonna get those outputs out,
Speaker 1: and the prices were known, so there wasn't a lot.
Speaker 1: There were early traces of dealing with uncertainty within economic theory,
Speaker 1: but but only if you and so. I was part
Speaker 1: of a group, and there are many others, including traditional
Speaker 1: economists such as kenn Arrow Gerard Dubrow, who brought uncertainty economics.
Speaker 1: But both traditional academic finance and traditional academic economics were
Speaker 1: very different, and so in a sense it was a
Speaker 1: matter of finding a home and building this whole new
Speaker 1: idea of financial economics, the two together and in particular
Speaker 1: financial economic theory. Let's let's talk about books. This is
Speaker 1: the question that listeners ask more than any other. Tell
Speaker 1: us about some of your favorite books. Um, well, you
Speaker 1: know that's a hard question. You know, I don't really have.
Speaker 1: I'm not going to say any of my own books
Speaker 1: because I find it, if anything, painful to reread. I recently,
Speaker 1: some while ago, did a book of readings of my works,
Speaker 1: selected works, and and to do that I had to
Speaker 1: read through all my own work books and papers and such,
Speaker 1: which I found very painful. Um, I completely understand. But
Speaker 1: you know, there there there are no books. I have
Speaker 1: books on myself that I would never part with, but
Speaker 1: but I don't reread them or I really even look
Speaker 1: up things. What was the most recent thing you read?
Speaker 1: Tell us something? Uh, it could be fiction, nonfiction, something
Speaker 1: from yesterday. I read the book I'm I've been reading
Speaker 1: the last few days or so. It's called something like
Speaker 1: the decline of expertise or the death of expertise. Perhaps
Speaker 1: it's so it's polemic, but it's challenging and it's thought provoking.
Speaker 1: And um and you know, I like to read um books.
Speaker 1: I love computer programming. I think computer science although I'm
Speaker 1: not a computer scientist, but is fascinating. And I I
Speaker 1: like to think about and read about the potentially impact
Speaker 1: of technology, in particular computer and related technology on everything
Speaker 1: cars and professions and finance and what have you. Um
Speaker 1: and and so I like to do a little bit
Speaker 1: of modestly futuristic and to some extent history of the
Speaker 1: development of computer and technology. I I read, and I
Speaker 1: read a lot. I'm you know, I'm a classical music fan.
Speaker 1: And and I used to play jazz badly. Um what instrument? Bass?
Speaker 1: I play piano now from just for myself, I don't
Speaker 1: I don't play out as we say, but um and
Speaker 1: and I'm you know, I'm involved as I with a
Speaker 1: Carmel Bok Festival plug plug everybody should Carmel Buck Festival.
Speaker 1: When when does that take place? Two weeks in the
Speaker 1: summer h July August July. It's it's fantastic. So it's
Speaker 1: eight year, well, it's a number venues. We have chamber concerts,
Speaker 1: main concerts. Uh, it's it's it's a big deal, eighty
Speaker 1: years and counting. And uh, professional musicians, professional corral, it's
Speaker 1: it's it's a it's a remarkable occasion. I'm still stuck
Speaker 1: picturing you as a jazz bassist playing in some smokey club. Well,
Speaker 1: yeah it was. It was trad jazz, not not nothing,
Speaker 1: nothing after nineteen or maybe a little bit into the
Speaker 1: thirties styles. Okay, so we just celebrated Ella Fitzgerald's hundredth birthday.
Speaker 1: I'd forgotten and I didn't get or anything. Yeah, um,
Speaker 1: I saw Wynton Marsalis do a a the jazz ban
Speaker 1: of Lincoln Center. Did uh I read about that? Yeah,
Speaker 1: it was. It was lovely, it was absolutely it was
Speaker 1: a series of different vocalists. So I'm also, by the way,
Speaker 1: just an inveterate upper buff I go to every single
Speaker 1: operation in the movie theaters and uh and and I
Speaker 1: can live performances. But we don't have live opera in Carmel.
Speaker 1: What's what's in a while? We have one? But so
Speaker 1: you have to either go to San Francisco or Seattle.
Speaker 1: Uh so that's not too far. But but I'm not
Speaker 1: gonna stay on the air. But I really like the
Speaker 1: opera in the movie theater. Okay, that I find that
Speaker 1: very satisfying. Um, not so much jazz anymore. You know, no,
Speaker 1: modern jazz is to progress. It's it's it's you know.
Speaker 1: So what about so to me, it's so funny you
Speaker 1: said twenties and thirties. I appreciate it. I just doesn't
Speaker 1: turn me on. What about some of the classic jazz
Speaker 1: of the fifties. To me, classic jazz is fifties and sixties.
Speaker 1: So it's Ornette Coleman and Miles Davis and and Milonious Monk,
Speaker 1: and I sort of I sort of lost interest around
Speaker 1: the big band era. I grew up in the tail
Speaker 1: end of the big band era. Um. But and then
Speaker 1: I I listened, followed played, um traditional jazz. So big band,
Speaker 1: Duke Ellington, Tommy Dorsey, Leon Miller, you name it. Okay,
Speaker 1: so I love that stuff. But the next Jerry Mullikan
Speaker 1: and Coltrane, and that was not your bag. No. I
Speaker 1: mean I used to go to a little bit of
Speaker 1: you know, some of the clubs in l A in
Speaker 1: that era. But now I never got really got hooked
Speaker 1: on that. There used to be a great jazz scene
Speaker 1: in San Francisco before my time. Well, there was a
Speaker 1: good and when during the revival of trad jazz. Uh,
Speaker 1: there was a great trad jazz scene in San Francisco.
Speaker 1: And when I was at Cow my freshman year, we
Speaker 1: used to go to a place in Oakland, down by
Speaker 1: the in the industrial district. Um Turk Murphy, Um trying
Speaker 1: to remember his his clarinet player. But but there was
Speaker 1: some really good you know, there was a big revival
Speaker 1: period of trad jazz, just as later there was folk music.
Speaker 1: I love the folk music era. Well, the next time
Speaker 1: you get to New York, we'll have to get you
Speaker 1: over to Lincoln Center or the their jazz their big
Speaker 1: band jail. Yeah no, that's a fact. Yeah, absolutely. Um.
Speaker 1: So we went over some of the changes, We went
Speaker 1: over some of the shifts, and you told us about
Speaker 1: a time you failed. So I don't have to ask
Speaker 1: that that question. Well, there are more, but let's leave
Speaker 1: it at that. Any other antecdotes tell um. So let
Speaker 1: me let me get to my two favorite questions. I
Speaker 1: asked all of my guests if a student or a
Speaker 1: millennial would come up to you and said, I'm thinking
Speaker 1: about a career in either um financial economics or investing
Speaker 1: what sort of advice would you give them? Well, I
Speaker 1: can tell you what I've told our millennial grandchildren. I
Speaker 1: haven't the foggiest nor presumably does anybody else. But you'd
Speaker 1: better get a really broad education. And I mean really
Speaker 1: broad because technology, you know, I don't think we've begun
Speaker 1: to see anything yet. Technology is going to intrude and
Speaker 1: take out any profession or trade that has any routine
Speaker 1: nature to it. We know, UH, is subject to mechanization, computerization,
Speaker 1: whatever you want to call it. So you need real breadth,
Speaker 1: and you need the ability to think and to be
Speaker 1: and to learn, and the willingness. I have this little sideline,
Speaker 1: um for this will be my fifth year. I teach
Speaker 1: kids in one of the towns near Carmel how to
Speaker 1: program code. How old? How old are the kids roughly
Speaker 1: twelve and uh. It's been a challenge, uh and I'm
Speaker 1: still experimenting with different different methods. But there's this wonderful
Speaker 1: language developed over decades at m I T called scratch
Speaker 1: for eight to sixteen year olds, which is a remarkable language.
Speaker 1: As a matter of fact, I have a blog in
Speaker 1: which I did a whole retirement income monitor Monte Carlo
Speaker 1: system and I wrote it entirely in scratch just to
Speaker 1: prove I could. It's not fast, but it's not bad.
Speaker 1: I had to write all my graphic routines. You can
Speaker 1: find it. It's something like retirement in some scenarios, blog
Speaker 1: spot or something. You find it, or it's there's a
Speaker 1: link on my website. But my view is that kids
Speaker 1: not necessarily become programmers, but to think logically and to
Speaker 1: begin to get an appreciation of how you can think algorithmically.
Speaker 1: You can do research or analysis or decision making analytically,
Speaker 1: and also to understand what's going on with the things
Speaker 1: that are probably going to displace you in whatever job
Speaker 1: you start at. I mean, I have no notion what
Speaker 1: you know? What is what? What is university education going
Speaker 1: to be like in twenty years? Um, it's hard to
Speaker 1: even fathom. I mean, I've read again books. You know,
Speaker 1: there are books about speculating on that which some of
Speaker 1: which I've read, and you know, the whole idea. I mean,
Speaker 1: one one author had this um argument that I think
Speaker 1: is is very valid. We're beginning to learn something about
Speaker 1: how people learn, how brains work, and his argument was,
Speaker 1: if you try to invent the absolute worst way to
Speaker 1: try to convey information and education to a student, it
Speaker 1: would be to have somebody stand at the head of
Speaker 1: the class and talk to him for fifty minutes. And
Speaker 1: so all the ideas of online learning with feedback and
Speaker 1: constant testing and branching and all that, I think they're
Speaker 1: fascinating and and there's there's there's there's something there. So
Speaker 1: the socratic method has something too it because it forces
Speaker 1: people to think, Yeah, my my son, who's an education
Speaker 1: told me when we were talking about different ways I've
Speaker 1: experimented with my kids in this summer program, he said,
Speaker 1: you've got to change from being these are these are
Speaker 1: this is jargon and ed apparently, from being the stage
Speaker 1: on the stage, being the guide by the side. So
Speaker 1: I went from in the first two years I taught
Speaker 1: the course, all right, here I'm doing this, and now
Speaker 1: you do that and you can experiment with some variations.
Speaker 1: Now pay attention, we're going to do this and you're
Speaker 1: going to do that, which is the way in which
Speaker 1: you pretty much teach scratch uh to something called code
Speaker 1: dot org, which is online free material bill Gate, Suckerberg.
Speaker 1: It's heavily supported and the student just logs on and
Speaker 1: starts solving puzzles and it's got feedback, it's it's it's
Speaker 1: very clever code dot org. And what I found with
Speaker 1: my kids. I did this last year. Then I did
Speaker 1: ten one hour sessions and it was great for the
Speaker 1: first and then I would sit and you want to
Speaker 1: help or what are you doing? And I only have
Speaker 1: twelve kids. But and then it you know, at about
Speaker 1: session five, I could have killed him. They were getting
Speaker 1: really antsy and itchy and bored and and so I
Speaker 1: slowly got them to come over to scratch and learn
Speaker 1: a bigger, broader language where you can be more creative
Speaker 1: and and uh. But the takeaway is the collaborative approach
Speaker 1: seems to be more effective than just lecturing. Today, I'm
Speaker 1: thinking of mix, thinking of mix, and and I know
Speaker 1: people who teach that grade level, no this, They've been
Speaker 1: doing it for decades. It's just that I'm I grew
Speaker 1: up in an environment where I was up in front
Speaker 1: talking and yeah we had interchange and I did small seminars.
Speaker 1: But but still and the idea of now having every
Speaker 1: five minutes, you know, here's something, test them. If they
Speaker 1: learn it, they get to go on. If not, they
Speaker 1: go back. And you presented a different way until they
Speaker 1: get it. I mean, the ability of computerized systems too
Speaker 1: engage in all of that. You just you cannot ignore that.
Speaker 1: And and how that's all going to shake out, I
Speaker 1: don't know, but I'm awfully glad I'm not entering, you know,
Speaker 1: the academy now. And and my final and favorite question,
Speaker 1: what is it that you know about economics or finance
Speaker 1: or investing today that you wish you knew back in
Speaker 1: the late fifties early sixties when you were first setting out? Well,
Speaker 1: would be perfectly frank, that's an interesting question. I've not
Speaker 1: thought about that. Um, it's easy to say, I, well,
Speaker 1: I should have taken more math than the first course
Speaker 1: in calculus. I don't know that. But um, but I
Speaker 1: faked my way through well enough. Um, I don't know.
Speaker 1: I mean, it's it's been a hell of a ride,
Speaker 1: I say, the least I would not want to have,
Speaker 1: sort of. I mean, there's nothing more fun than discovering
Speaker 1: something you had to anticipated. I mean, that is just
Speaker 1: the best trip ever. Um, and so so I I
Speaker 1: was lucky enough to have a lot of those experiences.
Speaker 1: And also, you know, teaching, Uh, teaching can be very rewarding,
Speaker 1: can be very frustrating and very boring, but when it's rewarding,
Speaker 1: it's really rewarding. So I don't think I I choose
Speaker 1: to do it differently. We have been speaking with William F. Sharp,
Speaker 1: Nobel Laureate, creator of the capital asset pricing model, the
Speaker 1: Sharp ratio, and other measures of risk. Thank you Bill
Speaker 1: for being so generous with your time. This has been
Speaker 1: absolutely a delightful a couple of hours. If you enjoy
Speaker 1: this conversation, then look up an Inch or down an
Speaker 1: Inch on either Apple iTunes, SoundCloud or Bloomberg dot com
Speaker 1: and you can see any of the other hundred and
Speaker 1: fifty or so such previous conversations. I would be remiss
Speaker 1: if I did not thank Michael bat Nick, my head
Speaker 1: of research, Taylor Riggs, my book or producer. And again
Speaker 1: I have to thank Andres and Horowitz for hosting us
Speaker 1: here in their absolutely delightful facilities. We love your comments,
Speaker 1: feedback and suggestions right to us at m IB podcast
Speaker 1: at Bloomberg dot net. I'm Barry Ritolts. You've been listening
Speaker 1: to Masters in Business on Bloomberg Radio. Our world is
Speaker 1: always moving, so with Mery Lynch you can get access
Speaker 1: to financial guidance online, in person, or through the Apple.
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