RR #200 - Prof. Eugene Fama

(upbeat rock music) - This is the Rational Reminder Podcast, a weekly reality check on sensible investing and financial decision-making from two Canadians. We're hosted by me, Benjamin Felix, and Cameron Passmore, Portfolio Managers at PWL Capital. - And of course, welcome to Episode 200, incredibly, 200 episodes. And of course, it's the guest that we've been talking about for a few weeks now, and we could not have welcomed a better guest, right, Ben, for our 200th episode? So we welcomed and had a phenomenal conversation with Professor Eugene Fama. - It really was a phenomenal conversation. There were

a lot of surprising answers. We kinda asked questions that we know the answer to, which is why we're asking them, a lot of the time. But today, with Professor Fama or Gene as he asked us to call him, there were a few answers that were not what I was anticipating, which was great. It expanded my knowledge. - And as you agreed in your discussion with him, like money, for example, as a topic, is really hard. It was a really interesting part of the conversation there. - Right, we talked about, well, crypto and monetary theory at

the end for a bit. And that's one of the areas where some of the things that Gene said were not what I was expecting, and it was very interesting to hear his thoughts. Well, one of the things people don't know about, or a lot of people don't know about Gene, is that, in addition to his highly influential papers on market efficiency and asset pricing, he has highly cited papers in many, many areas of economics, not just stuff that's related to asset markets or stock and bond markets, but he's got a bunch of stuff on monetary

theory and money and banking from the '80s, I believe, that are highly cited. Anyway, so it was interesting to talk to him about some of that stuff. - So I know most listeners know about Gene, but he's certainly probably best known as the father of modern finance. And of course, in 2013, he was awarded the Nobel Prize in Economic Sciences for empirical analysis of asset prices, along with Lars Hansen and Robert Shiller. Probably best known for his empirical work on portfolio theory, asset pricing and of course, the efficient-market hypothesis. He is currently the Robert R.

McCormick Distinguished Professor of Finance at the University of Chicago Booth School of Business, and he joined there in 1963. When you think of that career of studying all of this, it's incredible. He's had more than 100 articles published in academic journals, and is clearly among the most cited researchers in economics. - You said he's currently the Robert R. McCormick Distinguished Professor, and one of the things he told us is that he doesn't plan to retire, so... Currently and for a while to come still. (laughs) - Yeah. - He also talked about how he works in

terms of how he allocates his time for working. That was fascinating. - Yeah, he talked about the successful research career he's had with his long time colleague, Professor Ken French, who of course, was our guest in Episode 100. Gene and Ken are both long-time board members of Dimensional Fund Advisors. Anything else, Ben, to add? - No, I mean, yeah, Gene goes without an introduction, so I think we've said enough. And it's, as we've already said a few times, a phenomenally good conversation. We covered a lot of ground. (Cameron laughing) From listening to Gene's past interviews,

we kind of knew that he gives concise answers. So, I mean maybe as a point of interest, I'll say how many questions. We asked roughly 60 questions, which is twice what we would normally consider a high amount of questions to ask a guest. Usually 30 is like, "Okay, that's a lot." - That's a lot. - And this time, we asked 60 and we got through it in a pretty short amount of time, considering the number of questions. And every answer was crisp and full of... Well, the amount of insight you'd expect from someone with this

many years thinking about all this stuff. - And it's hard, because there's so many questions that you could have followed up with. And I'm sure as people are listening to it, they say, "Ah, what about this? What about that?" And such a thoughtful, incredible person and career. - We did a few though. I mean, on the size, for example, Gene talked about how there really is no theoretical reason for there to be a size premium. So I heard that and then later on in the interview, I circled back and was like, "Hold on. Why is

it in the model then?" And he gave his answer. And then, likewise, when we were talking about monetary theory, there was something that, well, the thing that I mentioned where it just wasn't what I was expecting, so we dug into that for a bit. Yeah, anyway. We're doing too much talking. We should go to this phenomenal conversation for our 200th episode with Gene Fama. Professor Gene Fama, welcome to the Rational Reminder Podcast. - Thank you. - Gene, what does it mean for a market to be efficient? - Well, a simple statement is that prices reflect

all available information. - What are the main implications for investors if markets are efficient? - Well, you can't expect that activities like picking stocks are actually gonna generate superior returns for you. So you get the risk-adjusted return appropriate to the risk level that you take with your portfolio, when you can't expect more. So that's another way to phrase the efficient-market hypothesis. So your risk-adjusted returns, expected returns, are basically zero. - So what are the empirical tests that support market efficiency? (Eugene chuckling) - So the best... (chuckles) I'll warn you in the beginning, I chuckle when

there's bad news, so (chuckles) if you hear chuckling, you know bad news is coming. So the strongest evidence, from our perspective, is that if I look at managed portfolios, actively-managed portfolios, what I find, basically, is the distribution of returns around zero. Excess returns are kind of normally distributed around zero, before fees and expenses. After fees and expenses, it's a big negative-sum game for those who go into that active management game. So the distribution of outcomes looks a lot like what you'd expect by chance, if there were no ability to pick investments that have above normal

risk-adjusted returns. So that's the strongest evidence, I think, from the perspective of investors. - Hmm. - Hmm. Does market efficiency imply that returns are random? - No. Well, it depends on what you mean by random. The deviations of returns from their expected values, where the expected value is a function of the risk of the security, that the deviations of the returns from the expected values are zero. - Hmm. So what are the biggest empirical challenges to market efficiency? - Oh, so I don't know if it still is, but in the past there was momentum was

the biggest one. Aside from that, well, if you really wanna push it to the limit, the fact that insiders make money on their trades is a violation of market efficiency, as far as those investors are concerned. Insiders clearly have information that isn't already in the market price, and they can profit from it. What's kind of surprising is that their average profits are so low. (chuckles) So they're about 1%. - You made a quick remark there, but I want you to elaborate. Why would momentum no longer be a challenge to market efficiency? - I don't know.

I don't know, I haven't seen any updates of the evidence, that's all. - Okay. - So the evidence goes back maybe 10 or 15 years, so I haven't seen what it looks like for the last, well, since the last paper was published. Easy enough though, Ken French, on his website, has momentum portfolios, so you can go in there and check the last 10 or 15 years anytime you like. - Do the empirical challenges like momentum, does that change the way that investors should behave? - Not really, because it's such a short-term phenomenon and it's such

a high trading cost phenomenon that there's not really any way to take advantage of it. - Hmm. - So is there an efficient markets explanation for what happened with GameStop? (Eugene laughing) - I don't follow these individual little aberrations, you might say. But remember now, efficient markets is a model. We call it a model because it's not reality, it's an approximation. Models are approximations. It's an approximation that works quite well for almost everything you want to do in investing, but sometimes there are aberrations, so I didn't follow this GameStop thing very closely, but apparently that

was an aberration of a small stock that went crazy. - Yeah, that's about the story, yeah. (Cameron laughing) Do the ongoing flows into passive funds pose any potential challenges for market efficiency? - Well, you can't have 100% of the money going into passive funds, because then there's nobody there to trade to make the market efficient. So the people who actually have information that other people don't have, you want them to stay in the market and use that information. So the real question that nobody's ever answered is how many of those people are there out there?

How many does it take to make the market efficient? So most of the trading by these investors just offsets the dumb things that other active managers do, so... Active management doesn't always make the market more efficient. Sometimes it makes it less efficient, because people make bad bets. So the informed people have to offset these uninformed people who make the bad bets. So it takes more informed to offset the uninformed, the more uninformed there are. - Hmm. Interesting. Do you think that the inelastic markets hypothesis changes anything for the relationship between flows and pricing? - No,

that's a hot no answer. My colleague two doors down from me, Ralph Koijen, is working on that, right? I wanna see how that evolves, to see what it's investment implications at. So basically, that's a different point The point there is that the domain for individual securities is not flat at a given price. That trading, actually, when people move into a security, that actually has a permanent effect on the price. Now, they were really finished testing that out, but their initial stuff looks very challenging, not for market efficiency, but for the idea that trading doesn't have

a big effect on prices. So we'll see how that all works out, but it's a very interesting line of work. - Hmm. - So interesting. So Gene, what are the shortcomings of the CAPMs, or the capital asset pricing model, as an asset pricing model? - I spent my early life, a long time ago, as one of the initial testers of the model, and for the first like 10 or 15 years that the model was around, it did pretty well on the data. And then, these so-called anomalies started to pop up, where people were uncovering things

that were inconsistent with the model's predictions. And little by little, basically, at one point, Ken French and I wrote a paper that said, "There are just too many anomalies here. This model is dead." And the basic problem, in the end, was if you look at a long period of data, the relation between market betas and average returns is basically flat. And according to that model, it shouldn't be flat. Average returns should increase with market beta. And there's not much evidence of that in the data. So that's kind of the first order implication of the model,

and it doesn't stand up very well. It would've been great if that model really stood up to the data, because it's such a simple model, you can teach it to almost any student in 15 minutes and they get the story. And now, the world looks a lot more complicated than that. - How did you and Ken choose the size and value factors to create the three-factor as a pricing model? - Well, we chose them based on the fact that these were, at the time, the two biggest anomalies for the CAPM to deal with. So the

CAPM couldn't explain small stock returns, and it couldn't explain the difference between value and growth stock returns, so we said, "Okay, we'll add to the market portfolio these two factors that will basically absorb those two effects. - So where does the three-factor model struggle to explain the differences in returns? - Well, momentum, the whole thing we started with, blows it up. So nothing explains momentum except momentum. So if you wanna explain momentum, you put it in a momentum factor, otherwise you haven't got a chance. - How did you guys choose to add profitability and investment

to the five-factor model? - There's some justification for that in terms of, you know, just as normal valuation theory, which says, if you hold constant other variables, then you should observe a positive relation between profitability and expected returns. But holding constant is important there. It's not a one-dimensional story. So we put that in there based on that. I'm not sure... We also put in an investment factor. So when you consider all these things together, what you should see is that average returns very positively with profitability and negatively with investment. And there's evidence of that in

the data, but the investment part of that is kinda weak. My hope is to have less factors that you need, rather than more, because the simpler the world is, the easier it is to deal with it. So I'd be really happy if those two factors dropped out of the story, (chuckles) because there wasn't much empirical support for them. I'd be real happy if it turned out that, in the long term, the CAPM worked really well, because then our life would be a lot simpler (chuckles) in that case. But so far, it hasn't worked. - Is

the five-factor model still an empirical model, or is- - Definitely an empirical model. - Okay. - It's got some weak underpinnings in valuation theory, but I'll emphasize, their weak. - Hmm. - So are high profitability and low investment firms riskier than their low profitability and high investment counterparts? - If you tell me they're two firms with the same profitability, and one does more investing than the other, and they have the same price, well, then one of them has to be riskier, if the market's pricing thinks efficiently. So that's kind of the notion. - Where does

the five-factor model fall short? Is it still momentum? - It's investment. The investment dimension of that is kinda shaky. And profitability, well, that's somewhat better. We haven't looked at that since we wrote that paper. That must be almost 10 years now. - So why isn't momentum in the model? - Well, because I can't tell a rational story for it. So if I can't tell a rational story for it, well, it's just a violation of market efficiency. There are such violations. - What do you think about the- - You don't want your asset pricing models to

be tautologies, basically, that you just throw in. This has been a problem. People have kinda lost interest in asset pricing because of the proliferation of factors. So people come out with papers where there are 100 factors. Of course, when you put them together, you find out that they're really aren't 100. That a lot of them are more or less the same thing. But that kinda kills all interest in asset pricing, because it becomes too flexible at that point. - Hmm. So you don't think all of the factor research that's happening right now is a good

thing for asset pricing? - No. Well, I think it stopped actually. I think people have stepped back and said, "Hey, is this really interesting or not? And how are we gonna shovel our way out of it, if it isn't?" - Hmm. What do you think about the q-factor model? If you've looked at that. - So the q-factor model is basically value, isn't it? Where you replace the book, or something like that? - Yeah. it's got- - Remember now, (chuckles) if you look at the investment business, right, 90% of it is marketing, right? So they come

up with a new name for an old idea, is, basically, lots of what goes on as research in the investment sector. - Yeah, and to piggyback on that, how sure can we be that factored premiums are not simply the product of data dredging? - Ah, that's a really good question. So in our stuff, what we do is, when we come up with a model, based on US data for a particular time period, then we take it out-of-sample for a different time period. So when we originally did the three-factor model, for example, that was based on

data starting from... I think it was starting from '63 onward. And then what we did, we went and hand collected the data that we needed going back to '26, so we could test it out-of-sample. And then we said, "Okay, that's out-of-sample US. Now, let's look at foreign markets, and see if we see the same thing." So we're looking for robustness, basically. Out-of-sample stuff that confirms what you observe in-sample. And for that model, we found it everywhere, basically. The same's true of the five-factor model. We found that pretty much everywhere too. It's much more difficult to

go backward in time, because you don't get good profitability data if you go back much past compusec going backwards. - Gene, can you talk about that time, going back and building that dataset, going back to, I think it was the '20s, 'cause you didn't have the data back then that we have now. Like, how big a deal was this? - (chuckles) Well, you had to go by hand into the books. The books existed with the income numbers in them, but they weren't in a machine readable form. But CRSP, Center for Research in Security Prices at

the University of Chicago had been collecting the stock return data, going back to '26, from the very beginning of those files. So we had the stock returns, we just didn't have the supporting accounting information. So that was collected by hand. - Hmm. - Wow. - Not my hand, thankfully. (Eugene, Cameron and Ben laughing) - You mentioned the out-of-sample testing. How important is the theoretical work, to make sure that it's not data drudging? - You would like to have a good theoretical model that encompassed these things. For the size and value factor, it's not there. Well,

the value more so than the size. The size factors doesn't have much theoretical underpinning to it. That should be encompassed in other things. As I said, profitability and investment, there's some foundations for that in valuation theory, but they're kinda weak. They're kind of, to say the least, week. It's not a fully specified model in the same way that the CAPM is. So now, Bob Merton, back in 1973, developed a... Well, he basically gave us the architecture for multifactor models and how you develop them, he just didn't put any names on the variables. He said this

is the form of such a model. Any such model, any model you develop, will show up in this form. You have to put names on the variables. 'Cause putting the names in the variables is the high factor. And that's where we were going with the five-factor model, basically. - Hmm. So that's the ICAPM. Is it possible to know what those state variables are that investors are worried about? (Eugene laughing) - Well, possible in what sense though? I mean, can you go into their minds and take out what dimensions of returns are of special interest or

disinterest? What things do they have positive tastes for, and what things do they have negative tastes for and how are those tastes generally? Does everybody have positive tastes for one thing and negative tastes for another? So it's not that easy, you know? - Okay. - Merton was one of the smartest guys, if not the smartest guy I've ever known, and he didn't even attempt to do it. He did not even take a crack at it. He just gave us the mathematical framework, and said, "Run with it, guys." (chuckles) - Hmm, okay. I see what you

mean in your papers when you refer to them as unknown state variables now. (laughs) - Thanks. - That makes a lot of sense. Wanna move on to expected returns for a bit. What do you think makes sense to use as an estimate for expected stock returns? Just market returns. - Okay, that's a very good question, because I don't know what to use, except for the historical average return. The problem is the historical average return is a number whose deviation from the true expected value has a big variance. You just don't get a lot of information,

even with a huge sample of data, about what that true expected market return is. So I think the market return from back to '26 to now has probably been in the neighborhood... Return in excess of the risk-free rate has been in the neighborhood of maybe 4% or 5%. But the uncertainty around that numbers means that two standard deviations away could be much closer to zero or much, much higher. Even though you have, now, almost 100 years of data on this, you still don't get a very precise estimate of the expected value. And that's a effect

of life in investing that there's just no way to get around to that, to handle it in any better way. We just don't know the expected premium of stocks over bills, for example. - Hmm. - And what about the expected factor premiums? - Same thing, 'cause as long as you have stock returns in there, the variance that is associated with them is going to be very high. So the expected values of any premiums that you put in are always very uncertain, no matter how much data you have. Or another way to think about it is

you'll never get enough data to know that, for certain, you'll get a positive expected premium. Even if I tell you the expected value of the premium, you don't know that in any finite sample, you will get that, because the variance is so high. - (chuckles) Yeah, and we don't know the expected value, so it's a... - You don't, so it's a double whammy, right? - We touched on randomness earlier in an efficient market. Do you think long-term investors should think about returns as random, or as predictable? Long-term investors. - Predictable, in the sense that, I

think, stocks have higher expected returns than bills. Predictable in that sense. It's not predictable in the sense that I know, for sure, that stocks will do better than bills over any length of time. It becomes more likely the longer the period, but it's still never set. So I don't know if that answers your question or not, though. - I'm kind of thinking, like John Cochrane, for example, talks about long-term predictability and that, in the very long run, stocks are a little bit less risky than you'd expect if they were completely IID. - Yeah, oh, okay.

Right, so there's some negative autocorrelation that's built-in there, that lowers the variability, long-term relative to short-term? No, (laughs) those numbers themselves, the autocorrelation numbers themselves, are estimated with a lot of uncertainty, so you can't really get a precise hook on that either. But he's right on that. - Hmm, interesting. So if you're thinking about long-term returns, it's really IID and use historical- - No, no- - As the- - Yeah. - Okay. - What it looks like, the reason it's not IID, at least Ken and I wrote a paper on this too. And John did too.

It's not the same paper, but the paper we wrote basically said, if the expected returns vary through time, but they're mean reverting, in other words, they don't go off to infinity plus or minus, they tend to come back to a constant mean, then you're gonna overwhelm... If I look at long periods, I'm gonna observe some negative autocorrelation generated by this variation in the underlying mean. And the way the empirical work, this goes back to the early '90s, I think, the way the empirical work turned out, that seemed to be a good story for the behavior

of stock returns. But there was never anything in that. That was a message for investors. 'Cause you're talking about variation in the underlying expected value that's really not so big, relative to variation around the expected value. And with a ton of uncertainty about estimating the process that generates that temporary expected value. - So let's shift to portfolio structure. Is there a single optimal portfolio for all investors, like in the market, with mean-variance portfolio theory? - Well, if I think about market clearing, right, markets have to clear, everything has to get held. So what that says

is that in aggregate, this is like a definition, investors hold the market portfolio, where the market portfolio is not just stocks, it's everything, so it all gets held. So that's the central portfolio of every asset pricing model. Every asset pricing model starts with that, and says, "Deviate from that, according to your taste for different dimensions of risk." But yes, center portfolio is basically this overall market portfolio. So that's a good place to start for any investor, I think. - You mentioned the market portfolio. Is the stock market a good proxy for the theoretical? - No,

because there are too many other assets out there, you know? So you gotta bring the bonds in too. - Okay. So the global stock and bond markets is a better proxy for the market? - Right. And then, you know, I gotta start asking myself, "What other investments do we have access to, and shouldn't those be part of the market?" So there's some uncertainty about what I should do about government bonds. So are government bonds an asset or a liability for... You know, you and I, we can go long government bonds, but we're really on the

short end too, 'cause we're gonna be the ones that pay them off, so... - Oh, wow. - Not clear that the net supply of government bonds, from our perspective, is that anything other than zero? 'Cause we were on both sides. - What about other assets, like private equity or alternative investments? - Right, so those are... In principle, everything that could be put into your portfolio is part of the market. Now, the question is, do you really have access to those things in an efficient way, in the sense that you can do it with relatively low

cost? So we don't have good models to answer that question. The other thing that's really bad... Now, some of my colleagues work on this, Steve Kaplan in particular, is what is the expected return on private equity? The data don't give you a good answer to that, because they're so self-selected, you only get to see the ones that survive, pretty much. So you don't get to see how much money was put in there that blew up and was totally lost. And that's very important. Very important. So I don't know what, if I were on your side

of the table, and I had to advise investors what to do, I don't know what I'd do about private equity, because I don't think the data are good enough for me to give you a good answer. - So why is the cap-weighted market portfolio a good starting point for- - Well, that's what the population has to hold and aggregate, right? That is the market, as far as the population is concerned. In aggregate, we have to hold all the assets out there, cap-weighted. Now, you can deviate from that. You can deviate from that. But when you

do you, you don't have the market portfolio anymore. - What determines that? What determines when an investor should tilt their portfolio away from the market? - Taste. Your attitudes towards different dimensions. I think of them as different dimensions of risk, but attitudes towards different dimensions of risk are what do it. You know, in Merton's perspective, it's basically our attitude towards whatever these underlying state variables are, that generate premiums in various dimensions. - So is it just taste or can it be other outside risks, like labor income and stuff like that? Or is that a taste?

- Well, (chuckles) our asset pricing model is not too good about putting labor income in, and considering its correlation with asset returns. In the '70s, people were worried about that. And basically, they threw up their hands. They said, "We know how to put this in there, as another untraded asset." Nothing you can do about your human capital. You're stuck with it, pretty much. But you wanna know the correlation of your human capital returns with the other returns in your portfolio, and take that into consideration in your asset decisions. Well, the way we finessed that was

we said, "Well, for most people, the return in the human capital was uncorrelated with everything else." (laughs) So we don't have to consider the correlation matrix there much. Basically, I think that's the way it stands now. I'm not sure that's satisfactory though. I'm not sure that's satisfactory. Surely, for example, you do not wanna invest a lot in the stock of the company you work for, because you're likely to go if that stock goes. - Right. John Cochrane's done some interesting work on that recently, or kind of summary work in his paper, Portfolios for Long-Term Investors.

We actually talked to Sebastien Betermier, who's done some very interesting empirical work on how investors change their portfolios based on labor income, and it looks like they actually do what you'd expect theoretically. - Oh. I'd love to look for that. What's the name? - Sebastien Betermier. We can send you his papers. It's very good. I wanna touch on international investing for a minute. I've heard you say, in other interviews, that for a US investor, you don't really need to worry about international investing. And if I remember correctly, the reasons were there's expropriation that doesn't show

up in the historical data. So the data is better than what you can actually get, and that US stocks are no more volatile than global stocks. So therefore, a US investor probably doesn't need too much international diversification. My question is does that change for someone in a country like Canada, which is a much smaller portion of the global market? - Right, that's a good one. Sure it does. Sure it does. I mean, if a Canadian investor only invested in Canadian stocks, he'd be really heavy in mining stocks, right? - Right. Basically, one industry concentration would

be pretty high. We Americans, US Americans, (laughs) are very narrow in that perspective. So this was a statement for US investors, not for Canadian investors. Canadian investors clearly should be looking at investments, at least, in the US. So whether the US will ever expropriate Canadian investors seems unlikely. Now, people look at expropriation risk as if it's not there anymore. This kind of stuff just doesn't happen. Well, I'll bet there's a lot of expropriation that's gonna take place right now, between the US, Europe and anybody doing business with Russia, so... And the problem is nobody cares

about investors. Investors get expropriated, and so, each side always expropriates the other side's investors, but they don't fix it after the war. (laughs) It stays expropriated, even if you win. So that's the risk of international investing. And it's not gone. It's not gone. I mean, there's nothing more poignant, right now, than that, actually. - We mentioned that that doesn't show up in the data. Is there any way to see? Like, how do you... I've looked, and I haven't found any papers or anything. How do you find the- - There was... I think, Steve Ross and

Roger Emerson, maybe there was another guy involved, way back when, what they did was they said, "Look, there's this risk that nobody takes into account, that markets actually close entirely." So during the Second World War, for example, lots of markets just closed. And then they came back after the war. So they went and looked at, well, suppose we were holding the overall market portfolio back in whenever, what happened to us in the meantime, when these markets closed? How did we end up? And they had a paper on that. Now, that was a long time ago,

in the '70s, maybe. I don't know what would happen if you updated that. Haven't seen an update of that, but people have a worried about that, that you only get data because the markets are open, you know? (laughs) And when they close, you don't have the data, so you tend to ignore those periods. But an example I'd like to give is I think Argentina was the second biggest market at some point in the past, and that market has closed multiple times since then. - I'm curious, Gene. Has your own investment philosophy changed through your career?

- No, (laughs) but my problem is I never intend to retire. So my portfolio doesn't to have to cover my retirement. It's basically my charities that I kinda suffer if I don't make good decisions. And my kids, of course. But frankly, I'm a really a sloppy investor. I don't change my portfolio very often. - I wanna circle back to asset pricing models for a second. We finished that part of our discussion talking about how size doesn't have any real theoretical basis. Why does it still gain a place in the models? - Because there's clearly lots

of covariation in the returns on small firms that's different from what you observe for large firms, and in the past, at least... I know we haven't updated this for a while. In the past, at least, that seemed to show up in differences in average returns. Showed up as differences in average returns. Looks like it was possibly differences in expected returns, statistically. - Hmm. Do you have any- - It's pure empirical. It's pure- - Okay. - I have no justification for it from any theory. - Hmm. All right, I wanna move on to inflation. You have

a paper from many years ago on this, but I'm curious to hear your thoughts. What assets are hedges against expected and unexpected inflation. - (chuckles) Now? So there are these indexed bonds. That's about as close as you can get. There are indexed government bonds that haven't been very popular in the past, and there's a limit on how much of those you can buy. I think that's probably why they're not so popular, but that's as close as you can get is an indexed bond. So I wrote papers back in the '70s for a period of time

when very short-term government debt looked like it was a good hedge against expected inflation, and no sooner did that period, as soon as I write those papers, then thereafter, that hasn't worked. So for example, ever since the financial crisis, inflation has been going all over the place and the interest rates have stayed near zero. So short-term bonds haven't been a good hedge against expected inflation. Now, I think we're going into a very interesting period coming up now about... Well, that's a different topic. You probably don't wanna get into that. But I've been waiting for a

long time about what would happen when we actually came up against a period when there was inflation, serious inflation. And we seem to be doing that. Not that I wish that on anybody, but I just wondered what would happen when we came to it, because I don't see that the Fed has the tools to really deal with that. I think what happened when they went to the QE business is they decided that the QE business was more important than controlling inflation, 'cause inflation was very low. But now, they're faced with inflation, and their only tool

is to raise the short-term interest rate. Now, I wrote a paper several years back that said, "I'm not sure that Fed even controls the short-term interest rate, because when it puts out lots of these QE, lots of reserves, they basically better pay open-market interest on those reserves, otherwise the banks won't hold them. They'll try to get rid of them, and they'll have hyperinflation. So they've been paying reserves on them. And I think they haven't been setting that rate. That's the rate that's dictated to them by the market. So I had a paper that, basically, was

trying to kind of document that. And then it said, "Well, how far out does any influence go of the Fed?" Oh, it was very short. I mean, the term structure, at the intermediate and long end, had a mind of its own. Had, basically, nothing to do with the Fed funds rate. So what does varying the fed funds rate? Well, they can only go in one direction, as far as I can see. They can go up. They can't go down. If they go up, fine, banks will sit on the sit on the reserves. But how much

will they have to go to actually slow down economic activity? Now, let me put it differently. What firms take the short-term overnight rate as their cost to capital? I don't know any! How sensitive are they to that rate? Maybe not at all. This whole assumption about how they'll control inflation with that huge balance sheet that they have is really untested. We're gonna test it now. We'll have one observation, (laughs) a year or two from now, on this process. Sorry, that was a tangent. - No, no. I wanna keep going on this tangent, so no worries.

- Exactly, Gene, like what can the Fed do? Can they do anything? - Well, the question is if they raise the federal funds rate, how far do they have to raise it to have any effect on inflation? That's a wide open question. We don't have any data at all on that, because this QE business is a new regime. The Fed was always operated in an environment where there were no free reserves basically. And now you got, I think, about $9 trillion worth of free reserves out there. So we've never had this regime, so we don't

know what it will take to make it work. What it will take in terms of raising the short-term rate. The big discussion is it an eighth or a quarter. Well, I don't think that's anyway near what they'd have to push it up, to have any effect. Might be 10%. Now, that's extreme, but it wouldn't surprise me that they had to bring it up so that the real rate was positive. That wouldn't surprise me at all, 'cause historically, the real rate's fluctuates within plus or minus 1% of zero. - Can you elaborate a little bit on

before QE, so before the ample reserves regime, what the Fed would've done to stop inflation, and why they can't do that now? - Yeah, so what they would do is they would just cut back on reserves. They'd make it more difficult for the banks to lend, and that would, in principle, slow real activity and pull inflation down. So the idea was, "Well, it'll cause a little recession, and that'll do it." They weren't terribly good at that, because if you go back to the late '70s and early '80s, we had inflation running at 20%, near 20%,

for a couple of years, so they were never very good at this game. - So what do you say to a typical retiree who might have a 60/40 portfolio, they've done their planning properly, so given what you said about inflation, what would you say to them? - Well, (laughs) I'd say hope that the 60 part of that isn't hurt by the inflation. So far, this bout hasn't been bad for stocks. In the past, high inflation has been negatively related to stock returns, but that hasn't been true in this experience. So they might be protecting the

stock part of their portfolio. But you're stuck, you know? You gotta hold the assets that are out there. That's all you can do. So you can worry about it, but it doesn't help a lot. (chuckling) I hate to chuckle at that one, but it's true. - That's... Yeah, that's difficult advice to give. (Eugene, Ben and Cameron laughing) - You know, that's why I say, I'm glad I'm on this side of the table, and not on your side. - So you talked about QE, and how the Fed's got themselves in a bit of a pickle now.

Can the Fed cause inflation? - Oh, well in the old days, they could cause inflation, because they could put on a lot of reserves. They weren't paying interest on reserves, so the banks didn't want them, so the banks would expand their balance sheets, not to get rid of the reserves, and that could heat up the world in such a way that you got a lot of inflation and vice versa. So that was the idea in the old days, is that the small changes in the monetary base, reserves plus currency, would have a big effect on

inflation, but that's gone. That's gone. - Is that still a lending channel in that case? - Yeah, in that case, it was lending channel all right. - Okay. - You don't have a lending channel now. Well, they're hoping you do. They're hoping that by raising the fed funds rate to way above what the equilibrium would be, that that'll get banks to sit on the reserves. They won't try to lend them out, so it will reduce economic activity, but we'll see. We'll see. Plus, this is a different world now, you know? You have all these FinTech

companies out there, that are not even part of the system, that are doing a ton of lending. - Yeah, that's a good point. Okay, I wanna move on to theory versus practice, because obviously, you've been working... Well, theory and empirical work. Academic work versus practice, I guess. You've worked in academia for a very long time. You've also worked with Dimensional, implementing these ideas for a very long time. What's the biggest challenge in translating your academic work into live investment products? - Well, initially, it was we didn't know whether these things would carry over, whether you

could actually implement them. So for example, when a couple of students at Chicago here came up with the small firm effect, most of the academic profession said, "Yeah, that's in the data, but you'll never get it, because you're gonna get wiped out by the bid-ask spread on the small stocks, so forget about the small stock premium." And it turned out that that wasn't true. That slow trading in small stocks basically didn't pay the bid-ask spread. Dimensional basically established that through its own trading. So nobody talks that way about it anymore. And then, you know, let's

say the value premium, you worry that if too many people get into that, maybe they can kill it, you know? So if they're getting into it, because they think it's a profit opportunity, rather than a different risk factor, that could kill it. So that remains to be seen, I think. We'll never have enough data to know the answer to that, but that's one of the issues involved there. So (chuckles) another way to say it is kind of where we started. There's so much uncertainty involved in the outcomes for investing, that it's difficult to extract the

signal from the noise. It's difficult to tell what's the real stuff going on, underlying what we see every day, given what we see every day or every year or whatever, is buried in a lot of noise. - So even if value is theoretically a risk premium, if people believe it's a profit opportunity, even if it is riskier, the premium can still go away? - Sure. - Oh, wow! Hadn't thought about that. - Sure. Well, look, if I were misled, and thought that stocks were much less risky in the long run than in the short run,

I could kill the stock premium over bonds too, if enough people believe that. - But they might get- - It's not special. It's not special to the value or smaller, or any of that. If I don't really understand, the difference is that the long term does not erase uncertainty that's in the short term, unless you get a lot of negative correlation in there, then the markets can become... That's kind of an inefficiency, actually. That you kill a risk premium because of false beliefs. - But the risk would eventually show up. I guess we don't know

that. If the risk eventually showed up, they might- - So the way we thought about it originally, was that value stocks are riskier, in the sense that if I look under the hood, what I find is that those companies are not too... You know, they kind of have been badly run or whatever, or they're in industries that are sort of declining. So that's the real risk that's involved in taking on value stocks, is that it's not a healthy end of the economy. So the question, though, is should they carry a risk premium? It seems to

have been there in the past. If people are not concerned with that source of risk, 'cause everybody that works for those companies should be concerned with it. But if otherwise, if that's not enough to turn people away from those stocks, then you'd expect that premium to go away. But who knows. It'll take a long, long time of data before we know the answer to that. - Hmm. And I guess if value is a proxy for the unknown state variables in the ICAPM, then the risk could show up at times that people don't want it to?

- Right, right. - So what have you learned from working with Dimensional that you maybe wouldn't have learned through academic research? (Eugene laughing) - So when we were small, we didn't have a lot of money to invest and the markets were different. Market microstructure, which is the end of the market that can cost you a lot of money because of trading costs, that was a much less sophisticated business then, than it is now. So now, they have all kinds of trading approaches to try to minimize the trading costs of the portfolio. So seeing all of

that evolve, it's been really eye opening. And there's a whole block of literature in academia about market microstructure, and experience has shown lots of it to be, basically, hogwash. I mean, they they're on the wrong track. But there's a difference between your trading costs, if you do slow trading or if you do fast trading. So that's been something we've learned a lot about. Basically, there's always learning and implementing something that you've done with data, but you've never done in practice. So what's the slip between the data and the practice? Is there any, and how do

you go about it so that you don't create unforeseen blockages somewhere in the process? So I think Dimensional has been very good at that. The company, now, is much more technically sophisticated, in terms of how to deal with markets, how to deal with almost everything, than it was in the first, let's say, five years of existence. The first five years of existence, you could take the whole company home every night on a very small tape, you know, (laughs) a very small floppy disk. Forget it now. (laughs) - So as a follow up, Gene, what's it

like to look back and see your academic work actually implemented in practice? - Well, it's kind of satisfying. So I don't take a lot of credit from that. I think my generation came along at a time when there was nothing in academia. Finance didn't exist, basically. So my generation basically opened the field up, and it was great to be involved with all the people who did it. But we were kinda lucky, in the sense that there hadn't been anything before that, so it was like fishing in a barrel, you know? You just threw the line

in, and when you get it up, it always came up with a fish. The current people coming into finance have a big body of stuff they have to master, before they can actually think about doing research in the area. So they're a little bit hog-tied, relative to what we were in the old days. Of course, the downside of that is we're now all old, (laughs) so... - So when you were fishing in that barrel, did you know what a big deal this was going to become? Like, did you have a sense? - Yeah, no, absolutely

not. - Really? - Yeah, absolutely not. Look, we were young people trying to do academic research that would eventually get us tenure, you know? So we didn't really know where this would go. Plus, as Mike Jensen always said, I'm amazed that people pay us to do stuff we would do anyway. (laughs) Talking about academic research. And that's basically true. I mean, the people who do it basically love to do it. It isn't really a job. - So did you see at all the evolution of indexing and Vanguard and you know, the book "Trillions" by Robin

Wigglesworth. Like, did you foresee this at all back then? - Well, in my view that all took too long. The evidence was there in the early '60s that this was the way to go, and it took a long time before that had a big impact. When Ken French did his Presidential Address at the American Finance Association, basically in that, whatever it was, 50-years period since the beginning of this research in the early '60s, late '50s, the world had gone from 0% passive to I think it was 20% passive at that point. And now, it's up

to 50%. But it's still far from 100%. So, I don't know, to me that seems slow. (laughs) - Do, you think anything useful, for a lack of a better word, has come out of behavioral finance? - Yeah, I do. See I have trouble with this, because what do you mean by behavioral finance? All of economics is behavioral. So the issue is whether the behavior is rational or irrational. So what we call behavioral finance now, is basically looking at the world as if behavior is irrational. Now that's... And the behavioral people, my good friend, Dick Thaler,

for example, they acknowledge that this is kind of a nihilistic game. That basically, they have no advice for investors, because they think whatever advice they put out there, everybody's irrational, so they kind of screw it up. So basically, they end up at the same place we do. They say, "No, just index everything, because they're too dumb to do anything other than that." (Eugene chuckling) So that's... And, way back when, I wrote a paper, It's one of the most highly cited papers in the general finance. It was about market efficiency, and the challenge for behavioral finance.

And I said, "Okay, you guys have been criticizing us, but that's all you have. Without efficient markets, you have no area, 'cause that's all you do is criticize efficient markets. It's time for you to develop an asset pricing model of your own, that we can all turn around and test and in criticize." And to this day they haven't done that, so... To this day, it's still just a criticism of efficient markets. - One of the things behavioralists talk a lot about is bubbles. What do you think about bubbles, in the context of market efficiency? -

Well, the word bubbles... I canceled my subscription to The Economist, because during the financial crisis, the word bubble appeared in almost every issue, in such a sloppy way that I couldn't stand it anymore. So I wanted to know what they considered a bubble. So my view of a bubble is something that has a predictable ending. In other words, you can make money predicting how the bubble will evolve. If it's just accumulation of random numbers that looks like a big hump, well, okay, fine. I don't call that a bubble. So I'll tell you a famous story.

So I'm forgetting his name, unfortunately, but it was a famous agricultural economist at Stanford. This is way back, before efficient markets really came up. And he thought his colleagues could see patterns and data where they were none. So what he did was he took a random numbers generator, and he accumulated the numbers. So he got lots of variation, but it was all just random. And he brought it into the faculty lounge, and he showed it to his colleagues and they... He said it took them about 15 minutes to come up with stories about what episodes

and prices those were. And the message really was they're seeing things that don't exist. This is just all randomness. And that's basically what I say about people who talk about bubbles in markets. You gotta tell me how to predict the endings of these things, otherwise I don't call 'em bubbles. I just call 'em randomness. - Hmm. Go ahead. - Go ahead, go ahead. I was just gonna ask Gene, so does the proliferation of increased computing power, artificial intelligence, machine learning, does all of that make it easier for active managers to earn alpha? (Eugene laughing) -

So I thought we were gonna get through this without that question, because I've never done this dark. I've never done one of these and that question hasn't come up. So we're consistent. And the answer's been the same for, I don't know, 40 years now. So I came online when computers were first coming around. I was one of the first ones at the University of Chicago to use the old 709 that they brought online. But anyway, my answer always is, you know, in principle, we have a lot more information, we get it a lot faster, at

least. Maybe it's the same information, but we get it a lot faster than we used to. And we have ways of distributing it that were unknown 50 years ago. But you can't see the tracks of that in the behavior of prices. You can't see that that's had any noticeable effect on whether the market is more or less efficient. So that's been the answer to that question for about 50 years. We just don't know. My view of that is that we don't know because the market has always looked pretty efficient. It doesn't look more efficient now.

It doesn't look less efficient now. It's always looked pretty efficient. So it's nice to have all this information, and to get it quickly and cheaply, but it doesn't seem to have improved markets that much. - I think I've heard- - It's pretty good. - I think I've heard your former student, Cliff Asness, talk about machine learning, kind of like as a supercharged version of the anecdote that you told about the agricultural economist finding patterns in randomness. - (laughs) Right, right. There was a story that I now remember, because there was a period of time when,

with computers coming around, the people in physics were having difficulty finding jobs, so they thought finance was gonna be an easy field, and they'd come in and develop models to predict markets and prices. And that was a huge failure. It didn't work. - Yeah. - But they'll try again. - Always, I'm sure. - Sure, right. Artificial intelligence is just that. - Yeah. - Artificial. is the key word there. (laughs) - And it would be competitive too, right? That's the way I've always thought about it is if there's an AI that's good enough to earn alpha,

then someone else is gonna build a competitive AI that- - That it'll kill itself, right? - Right. I wanna move on to crypto for a little bit. You were on a Bitcoin podcast that I listened to. It was from back in 2015, and I'll quickly summarize your position from that interview. You kinda said that Bitcoin's a accounting system for exchange that may be useful to drug dealers, because it's somewhat anonymous, but it's otherwise no different from a volatile checking account. Has your thinking changed at all since then? - Well, here's what I say. I like

this area anyways, 'cause it's so much to talked about this, it's garbage. So that you gotta distinguish between the medium of exchange, like the cryptocurrency itself, and the mechanism that does the exchanging, so the blockchain. People often don't distinguish between the blockchain, and let's say, the cryptocurrency, but they are different. So I could put the cryptocurrency into the exchange mechanism that the Fed runs among banks, and I could put reserves into the blockchain, if I wanted to. So you gotta distinguish between those two. Now, the problem with, let's say with Bitcoin, if you go back

to the old monetary theory, what it said was if something's highly variable in real terms, it's not gonna survive as a medium of exchange. Simple way to think about it is firms don't wanna do business in a medium of exchange that itself can put them out of business. You know, just its own variability can put them out of business. So if you look now at the people who, quote, take Bitcoin for transactions, what you'll find is they take it, but they don't hold it. They get rid of it almost instantly. They just sell it. Now,

that's one thing. The second part of it is, okay, what gives Bitcoin it's value? If it's not really being used as a medium of exchange, well, then it should really have no value. And it's volatility would kill it as a medium of exchange. Now, these stablecoins that people are talking about, they're better, because they're, basically, linked to the dollar. I think they recognize this problem. But at that point, they're kind of like bank reserves. You know, they're just linked to the dollar, I don't know, is the Fed gonna allow that kind of competition in there.

And is it really credible that a private issuer of reserves will always be ready and able to exchange it for currency on demand? I don't know. That's a tough one. That's a really tough one. How much currency do you keep in the background to make that credible? So we'll see. Now, that's separate. So that's the medium of exchange. The method of exchange is something else. You could certainly improve on the method of exchange that the Fed uses to clear transactions. There's no reason it should take two days, or even a day, to clear transactions through

that system. I mean, it's all just a computer. (laughs) I wrote this paper like 25 years ago, that that exchanges in the computer age, exchanges should be instant. They should be able to trade reserves instantly across the system. So there are efficiency improvements that could take place in the mechanism. Now, blockchains... Blockchains don't look to me like that kind of system. They're incredible hogs, in terms of the electricity they use, because to not have somebody overseeing the system, like the Fed oversees the system among banks, becomes very expensive and it uses incredible energy. So it

doesn't look to me like that system has much of a future to it, but we'll see. We'll see. But I think there's a lot of junk that gets talked about in terms of cryptocurrency. I don't think people who write about it really understand what it is and what it needs to be in order to survive. - What do you mean by junk? What's the kind of junk? - Well, the kind of junk is they don't sit around and say, "Well, why does this thing have any value at all?" You know, the answer to that, from

monetary theory would be, if I don't use Bitcoin to execute transactions, it has no real value. I mean, it's just numbers, you know? There's no real use for it. So bank reserves have value, because they are an electronic means of exchange. A very efficient means of exchange. Well, maybe the blockchain is that, but if that's it, then you get the problem that this thing has a highly variable real value. So it shouldn't survive as a means of exchange on that basis alone. So these are the things I don't think people... I don't hear anybody talking

about that. I don't think people coming up now actually learn monetary theory. (laughs) - So what do you think is- - Go ahead, Ben. - I think that the argument from the Bitcoin community would be that it has value because it is censorship resistant. So even though it is highly inefficient, because it has trustless, or not centralized trust, consensus, which is expensive, and it's volatile, people who can't operate within the existing financial system, because they're criminals or they're in a country that doesn't have infrastructure. - Yeah, that produces demand for it, basically. You gotta have

a demand for this as a medium of exchange. So illegal transactions produces demand for it. But, (laughs) then my question is, how much of that do you need to give it substantial value? How many illegal transactions? How much of the illegal transaction trade do you have to get to make that work? I don't claim to know the answer to that. But that's the question implied by that line of defense is now you gotta tell me something more about what it would take to make that survive. - Yeah, and we can't know that, but a paper

came out recently that found that, I think, 90% of Bitcoin transactions are not economically meaningful. - They're just people trading? - Yeah, exactly. - That's not telling. so you could have zillions of those transactions, right? Just people trading, but there still could be a large amount of absolute transaction goods being exchanged in the background, and that, still, would have to be very big, so that doesn't really answer the question. - So what do you think is driving the incredible rise in price of Bitcoin? Like, the past seven years is up like 11000% or something. -

(laughs) Right, right. That's fine. I mean, I'm not buying it, but I think it's volatility. Its high price is very impressive. The volatility is equally impressive. It goes up and down 30% in short periods of time. That's really impressive. - You mentioned for reserves, or for, say the dollar, that it's an efficient means of electronic transactions, and that makes it valuable. Is that what gives the dollar value? - Nah. You gotta limit the supply, and then you gotta have people willing to trade in it, willing to execute transactions in it, and then you have value.

So the dollar is a fiduciary currency. There's nothing there, except that the fact that the supply is limited. (laughs) So this is taking us back to the QE period again. So this is another thing that really bothers me is that in the old days, we had a supply of the monetary base, which is basically currency plus reserves, and neither of them paid interest. So they looked identical. Now you've got currency plus reserves, and then reserves basically pay market interest. And you can go back and forth. The banks can go back and forth between them on

demand. That's written into the Federal Reserve Act. So you don't have the supply of a medium of exchange, which the Fed has control over, because the reserves are, now, just another interest-bearing asset, another asset out there that's bearing market interest. So the fact that it's exchangeable for currency, currency, in principle, could be used to control the price level. But when it's exchangeable freely for reserves, that goes out the window. 'cause the supply is no longer fixed by anybody. - Hmm. So do you need to have a fixed supply for the dollar- - Need to have

somebody controlling that supply, if you wanna control inflation with it. - Interesting. 'Cause banks control or the Fed controls, or tries to control interest rates, but not necessarily- - Yeah. - Monetary base? - It gave up on controlling the monetary base when it went to QE. The price of QE was giving up control of the monetary base. - Hmm. So one of the things that, again, Bitcoin people would say, is that Bitcoin is better than a fiduciary currency like the dollar, because the supply is mechanically fixed. Is that a good property of a currency or

of a money? - Well, Milton Friedman always said that that was a good property of a currency. He was always in favor of the Fed limiting the supply of currency plus reserves to a fixed rate of increase every year, basically the expected long-term expansion of the economy. And that's it, no more. So that's basically the same statement that knowing how much is gonna be out there and how much will be out there in the future is very important, if you want this thing to be the medium of exchange. Now, that's a big problem with a

few fiduciary currency. They often blow up because governments can't resist throwing more of it out there and spending it. So that is an advantage. Having a fixed supply is an advantage. (chuckles) The huge disadvantage is the variability, in real terms, of the value of that supply. That's the cost of using that as a medium of exchange. - Hmm. So to have stable, real value. Right, but in the current system, to try and have a stable, real value. Oh, we don't currently have stable, real value? - Not on Bitcoin. - Oh yeah, sorry. Back to the

dollar. - Can give a- - Oh, well, worked so far, right? (laughs) But we've had periods where, if you go back to the '70s, periods where the value of the dollar wasn't that unstable, but it was just going down all the time fast. That can kill a currency too. - What do you gain by fixing the supply? That like maintaining real value long-term, like the gold standard kind of argument? - No, So if you maintain the total supply of what people transact in, what monetary theory would say is the real value, that should you go

up through time, if the economy expands, because you have less of it to use in transactions. So you basically create more by increasing the value of it. the value increase in its own. So you expect the price level to go down in that case. - So it was deflationary. Is that bad though? - No, not necessarily. Not necessarily. - Huh? Very interesting. - Mm-hmm. - Just wanna shift gears to your career, Gene, and we're curious, who were the most influential figures in your early academic career? - Oh, gee... Merton Miller and Harry Roberts, easy. Merton

Miller, probably everybody knows, he was one of the founders of finance, especially capital structure in finance. Harry Roberts, very few people know, but he was the one that gave me my upbringing in statistics, and how you go about doing meaningful statistical work and how you look at it. So those two were the most important. - How many hours per day... I'm asking this to you, because you've been incredibly productive throughout your career. How many hours per day do you think the brain can handle thinking work? - Okay, (laughs) that's a very good question. It took

me a long time to figure that out. So I would say you have about four hours a day. So I say over my academic life, I do my work in the morning, and I do other people's work in the afternoon, because in the afternoon, I'm burnt out. I can't do the original stuff that... My productivity per unit time goes way down. So then, what I found out later in life, is you can take, this is when I took up golf, (laughs) what I found out was you can take those four hours anytime. It's not important

that you get there in the morning. You can go play golf in the morning, and get the four hours in the afternoon, but then nobody else gets your time, so... - So you tackle something hard for four hours every day? - Yeah, every day, seven days a week. - Right, like your work ethic is legendary. - Well, I don't know. - I mean, we've heard Ken French talk about you calling him on Christmas day before. - Well, remember now, I would always... If people ever ask me, why did I go into academics, I'd say, because

I would have control over my time, and it wouldn't interfere with my athletic interests. So initially, it was tennis. I played tennis every day for a couple of hours. And now, I'm an old guy, so at age 63, I switched from tennis to golf. (laughs) But that takes a lot of time. It's not... Basically, academia is a way that you can squeeze your working around your other activities. - What do you think explains the, I mean, unbelievably productive relationship that you've had with Ken French? - Oh, well it's we have some very similar work habits.

So I know that I can call him or email him at any time, and he'll be working at about the same time that I'm working, right? He does more hours than I do. He's able to survive sleeping five hours a night. If I sleep five hours a night, I'd kill people the next day. - Yeah, me too. So we were way different in that respect, but we have similar interests too. That's good and bad. So really we're similar, but we're also different. So you gotta be different along a lot of dimensions, otherwise, there's nothing more

than the sum of the products that comes out of the relation. But I worked with other people in the past that didn't work the same long hours, the same consistent hours, and figured out I couldn't work with them, because our work times didn't intersect enough. - Hmm. So our final question, Gene, how do you define success in your life? - Oh, success? Okay, that's a good question. Well, I mean, I guess the primary thing is having a family that turns out to be something you're really proud of. I've been really successful on that, but my

wife gets most of the credit for that. Not me, because I spent too much time working, and she's the one that did all the work on the family side. So that that's very important. The second thing, very important, I would... Whenever young people/person comes to me, I say, "Look, it's important to find some... You're gonna spend at least a third of your life working. It's important to find something that you really like to do, otherwise those hours are gonna be basically torture." That's the first part of that advice. The second part is find something that

not too many other people wanna do, so that you can make pretty good money out of it. So you don't wanna be a ballet dancer, for example, because very few people succeed in that area. Even to do... There are lots of areas where that can be a problem. So that's the advice I kinda give to people. - Terrific. Well, Gene, this has been a real pleasure (upbeat rock music) to see you and for you to participate in our 200th episode, so thanks much your time. - Sure, my pleasure. - Thanks a lot, Gene. - Uh-huh.