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