John “Mac” McQuown on Inventing the Index Fund | Modern Investing Podcast

In this debut episode, we sit down with legendary innovator John “Mac” McQuown, the man credited with creating the first index fund. Mac shares the story behind one of the most revolutionary ideas in modern finance — from his time leading Wells Fargo’s Management Sciences Group to co-founding Dimensional Fund Advisors.

We explore how index funds reshaped investing by outperforming the vast majority of active strategies, why diversification remains essential, and how emerging technologies like graphene could disrupt the future of energy storage.

“A simple-minded index fund outperformed about 95% of those portfolios.”

Whether you're a finance pro, index investor, or history buff, this episode delivers deep insight into the origins of passive investing and the future of financial innovation.

About John “Mac” McQuown:

A pioneer in quantitative finance, John “Mac” McQuown helped launch the first index fund at Wells Fargo in the 1970s. He later co-founded Dimensional Fund Advisors (DFA), as well as KMV and Diversified Credit Investments. Beyond finance, he runs Stonehenge Farm Estate Vineyards, a fully off-grid, self-sustainable winery in Napa Valley. His work continues to shape how billions of dollars are invested around the world.

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About Sidepocket:

If you don't have the time to professionally trade and are tired of being at the whim of the market's ups and downs, consider using Sidepocket to automate investing.

Sidepocket monitors the markets 24/7 and automatically rebalances your holdings each month. To maximize returns while protecting against loss. 

That's why Sidepocket applies sophisticated quantitative methods, including tactical asset allocation to systematically minimize these drawdowns and consistently protect and grow your hard earned savings.

To learn more, visit www.sidepocket.com 

Transcript

Welcome to the modern investing with side pocket podcast, where we find some of the brightest minds in investing, entrepreneurship, real estate, tech, and more, and ask them, how do you stay financially ahead of the curve in the dynamic world we live in? But before we start a quick disclaimer, the content we are discussing through this channel should not be understood Or construed as financial advice, regardless of anything to the contrary, nothing available on or through this channel should be understood as a recommendation to buy or sell securities or constitute financial advice without the way let's get started.

We're here today with Mr. John Mack McQuown, who's played a significant role in shaping modern finance and investing. Mack has had a long and profound career. He is probably most well known for creating the index fund as the founder and director of the management scientists group at Wells Fargo bank. In his entrepreneurial pursuits, he co founded dimensional fund advisors with David G Booth.

KMV and diversified credit investments among accomplishing other incredible feats. Since Mac is also the owner of Stonehenge farm estate vineyards, a beautiful winery and vineyard that we had the pleasure of visiting earlier this year. That's entirely self sustainable and off grid. Really great to have you here with us today.

Super excited for this conversation, Mac, you and your team at Wells Fargo invented what's widely considered as. The greatest innovation in modern finance, the index fund without you, there would be no side pocket. We're basically built on the foundation of index funds. I think, uh, every listener on this podcast wants to know how did that come about?

Would you walk us through? Well, I have to say that I like to explain a lot of things with the, with the word serendipity. Because when you get right down to it, an awful lot of it is just that. I think, uh, to trace through the steps that led up to that. I was working on a problem related to the statistical behavior of share price changes, uh, in the market.

Uh, started out with, uh, weekly changes a minute migrated backward, uh, uh, to daily changes and then further backward to hourly changes. And that, uh, idea is was a byproduct, of course, of, uh, uh, that kind of data becoming available for the 1st time. Uh, which really that all got kind of started around 1960 and and I would credit a number of people at the University of Chicago for leading that, uh, quest, uh, headed by Jim Laurie, who professor there that I, uh, I knew, uh, and got to know much better as a byproduct of all of that evolution.

Well, we're at Wells Fargo in management sciences, which the chairman created. Uh, what in the sense that it was his budget, it wasn't part of the world budgeting. Process when we were on the chairman's personal budget. So, in order to get things done when we needed resources, or whatever, all we had to do is get an okay from the chairman and away we went.

So, that expedited an awful lot of otherwise pretty difficult kinds of things. But his objective was the following scalable.

Under and and far and a far cry from the way investment management was being conducted in those days, which was all stock picking. And subjective that wasn't analytical at all. It's just fundamental view was that, uh, commercial banks couldn't exist on loans and deposits alone. They needed. Uh, uh, a broader way of, uh, really a financial products.

Or more particularly investment products, so when when I was. Have the good fortune, the serendipity to to meet ransom. Um, and he knew I was interested in the analytics. Uh, he, uh, funded the creation of the management sciences group, and we started hiring, uh, uh, people from a variety of disciplines, uh, most almost all with advanced degrees.

And we also, uh, made arrangements with. Uh, a number of academic, uh, people who are responsible for the intellectual development of these ideas that I'm primarily at Chicago, but there were others as well from Princeton and Stanford and, uh, well, um. Uh, just to put that in perspective, we actually have 11 advisors or 8 consultants.

And 6 of those 11 have subsequently won Nobel prize. So, we were in a hot spot, and I would say that it would be very difficult in Rutgers to recreate the set of circumstances that that past constituted. But when it, when all is said and done, uh, it became clear when we looked analytically at the performance of portfolios that were being built in those days, uh, a simple minded index fund.

Outperformed about 95 percent of those portfolios. And we had, we had data probably on. About half of the institutional portfolios that were under Wells Fargo management at the time. Uh, and my recollection is that the largest 1, it was a couple 100Million. It was a pension fund. For 1 of the big oil companies, and it was a stock picking portfolio.

So, when we went to the plan sponsor. Um, and said, this is what we're, we're, we're proposing to do. Surprisingly enough, we've got 0 pushback because I think they were, were just about as disenchanted with the performance as the chairman of the bank was. So, the question is what the hell to do about it, right?

Okay. So, um, out of that, of course, came a variety of other. Related matters. By which I mean, uh, just stop and think about the, the derivative effects of that. So, actually, you know, uh, in fact, in fact, individual stock derivatives came in, came into being about the same time. And that also led to the creation of, uh, uh, uh, forwards.

And options on the indexes, the big indexes, especially the S and P

500. So, there was a lot of discussion at the time about the idea of constructing a portfolio that was just made up of, uh, derivatives on individual stocks and an underlying portfolio treasuries. But that would, you could get the same economic effect as you could by buying the shares long and then delivering them to some degree with a portfolio partially committed to fixed income and not just the equities, but that had to wait another day and, and, and, but bear in mind that options pricing theory emerged some of that picture as well because Byron Scholes and Bob Merton, uh, and most importantly, Fisher Black, Or in the hot for those ideas at the time.

So, let me just stop there and say, uh, that, uh. It just turned out to be an intersection. That was the sweet spot. On the 1 hand of the institution, willing to put up the money to do the work. Who also had access to the clientele that would benefit from the result. And we're also willing to go to Washington and deal with the.

And the controller of the currency who was responsible for. Overseeing trust development management. So, the combination, uh, well, and also the Federal Reserve, I shouldn't forget the Federal Reserve in that picture too. So, if all I can say in retrospect is, um, you, you couldn't, um, plan that kind of Intersection if your life depended on it, that's only the only possibility would be the coincidental and serendipitous intersection of interest.

And I should, I think I'd better stop there because that's kind of the, that's kind of the, uh, the essence of that, that whole picture. Yeah. It just sounds like it was a perfect mix of everything. And you just happened to be in the middle of it all. That's right. Awesome. Bear in mind that that's I had, well, remember, I had gone to business school and studied finance and been on Wall Street and fussed around with data and got to remember that computers were just coming to the floor at those in those days, that is big computers.

Uh, we had, we had, uh, punch card systems going back into the 30s. The idea of a digital computer that had some kind of scale was a pretty novel development. Uh, circa 1960. And the chairman was said to be on more than 1 occasion, you know, here we have these fancy machines and all these programmers and so forth.

And we're doing the same thing we were doing when we had green eye shades and arm bands. And now we have all this data captured. Can't we do more than just. Satisfy the requirements of accounting. So, so I want to, I don't want to under under understate the importance of the chairman's intuition about that subject.

Right? I mean, if it wasn't wasn't for his vision. And his desire and risk taking capacity. I mean, it would be hard to get that down in the middle of an organization. You have to be at the top, get that done. You couldn't allocate money very readily to such an endeavor because. You know, there's there's there's talk about risk.

I mean, that's about as risky as you could you could imagine. So anyway, uh, it was, it was a perfect storm. And, uh, of course, out of that came all kinds of interesting things. Yeah, like, I put myself in the shoes of someone, you know, maybe a leadership role in that space, trying to pitch to the board to get funding for this type of exercise.

I can imagine how much of an uphill battle that would have been. Well, yeah, it's kind of unfathomable. You wouldn't even nobody in their right mind would even try it. It has to come as a byproduct of other things. Right? And that's exactly what happened. I don't want to underscore in the, in the final analysis, the.

Insignificance of the attitude of the existing financial analysis group. I mean, they, they, uh, at the very early suggestion of these ideas. Or, uh, I would say up in arms to put it mildly. But the head of that group at well, after a lot of conversation with professors and with the data, in essence, he came around.

So, all of a sudden, there wasn't any more resistance from the established. Uh, investment management order at the institution. Uh, that, I mean, without that, I would, that would have, you know, that I mean, that that entailed the entire investment management side of the organization, which was. In Wells Fargo's case, very substantial, it was by far the biggest trust company in the West at the time.

And I think only JP Morgan and 1st, uh, Chicago were. Had larger trust activity. Oh, no, maybe melon, but, you know, in the whole, the whole world, there were only 2 or 3 bank with, uh, that scale of trust investment activity. And, I mean, and, you know, the, the 4th, national bank of would never think about taking on such a project, right?

It just didn't have the reach it didn't have the market position. So, anyway, I think, as I say, I'm right back to the serendipitous word because. You just can't imagine how many stars had to get lined up in order to get that to happen. I'm kind of curious. Uh, how many people comprise that team initially?

And then as the data started coming, coming in, that expand, was it a very kind of focused team or, or did it become fairly prominent? Well, it, it, it started out Danny, but, um, About four or five, uh, advanced degree people, we hired maybe maybe half a dozen total. People were involved until kind of got going and a fair amount of that effort was organized around creating databases.

Uh, it took some, they took some time before we could get to the analysis because we needed the data. You can't imagine a world without data. I mean, not today you can, but there was no data on those days. Right now. That is to say nothing that was analytically susceptible to being, uh, uh, transformed into a reasonable way of looking at the world.

That wasn't any such thing at the time. Here's another really interesting piece of data that comes to mind. How many IBM 7090s. Do you think banks had in 1960, Wells Fargo was the only one in America, one, it would be my guess there was one. And that was because the chairman was willing to put the money up to do that.

Right? Another thing about him was, he says, there's no reason to keep any of this under wraps. That's not the right thing to do with this. So there were a lot of efforts, uh, among the sort of the equivalent of management science guys, especially at JP Morgan, uh, bank of Boston, uh, first Chicago, where I already mentioned Mellon.

So they might have been maybe a maximum of half a dozen, uh, banks that have analytically minded people. With varying degrees of sophistication in their back pocket. Um, so you don't want to forget that took another kind of serendipity that not just the problem size and and the possibility, uh, existing from a database that the Chicago guys built.

But you also had to have the computers, and I mean, you couldn't do that. Remember timesharing was a predecessor of what we now call the Internet. And we were very early in the evolution of the Internet. We have the ARPANET. We were the 1st commercial subscriber to ARPANET, which was the, which was, uh, you know, the, uh, Department of Defense, uh, advanced research group.

Doing doing work that gave rise to the Internet, and we were. In the middle of that picture because of very, I was introduced to a fantastic guy at Berkeley who was. The head of that group and coincidentally at Berkeley, we're just across the streets of escape. So, that was another thing of considerable importance of being able to sit down at your desk and what used to be a common.

Looking computer, uh, known as a typewriter. And after they have it hooked up to the world over a telephone lines or power lines, both actually, all of a sudden we were doing something that was completely unintended by the architects of those technologies in their base and form. So, I can see this is this topic has got wrinkles and tentacles heading in a lot of directions.

Well, what I found really interesting was just how you didn't really have to sell diversification and indexing to the group you're working with at Wells Fargo, everyone already wanted to go that direction. That's right, because you can't believe how undiversified these portfolios were. I'm going back to this big oil company who I remember very well, but I'll skip over the name.

Thank you. It's entire pension fund, which was about a half a billion dollars, which in those days was a very big number was invested in 25 stocks handpicked 25 stocks. Just think about what half a billion would be today, you know, 10 billion. Yeah, can you imagine a 10 billion dollar portfolio invested in 25 names?

Right. That that would be crazy. Yeah, but that's everybody's intuition today because they know what the consequences are. So, so what is that pointing out diversification? Right? I mean, they didn't have a model of what diversification was in those days. I mean, if somebody said, well, diversification was 1 minus r squared, how far do you think that would go?

That would mean nothing. Yeah, right. And to add to that, there was probably very little, uh, literature on diversification from a mathematical and statistical standpoint as well, right? Well, I can tell you right now, there was some early work in that, in that was extremely seminal, right? Like Harry Markowitz wrote a dissertation in 19...

1952, uh, called portfolio selection or more fundamentally portfolio theory, right? But that was entirely theoretical. There's no data in that at all. It was an exercise in mathematical, uh, statistics. It wasn't, it didn't have anything to do with data, right? Yeah. Data was the problem. That's right. Well, computers.

And yeah, that's right with access to data, comparing it to that, that dissertation, um, how, how on point was it?

Let's see if I can remember that some of the details here, professor Jim Laurie at Chicago and his right hand man, Larry Fisher, and Larry was the one who did the hard work, but Jim was the overall architect of the idea and the one responsible for going to Merrill Lynch and saying to Merrill Lynch, we really ought to have some data on share prices.

And we ought to figure out what that implies about what's going on with portfolios. And Merrill Lynch put up whatever it was for 500, 000 dollars. Which was a big number in those days to collect. I think it was 200 or 400 names. From the N. Y. S. C. that only had 500 names. Listed at the time, and they went back led by Larry Fisher.

They went back and collected that 1st, Chris data file, which was started in 1926. And ended in 1960, and I think there were, I think there were, I don't know how many and the final analysis. I think there were 400 names. Because that's how many names to work on the New York at the time. Well, I mean, uh, Merrill Lynch put up.

They didn't I mean, you think they could possibly imagine what that was going to lead to? Hell, no, no, nobody could. Yeah. So you don't want to underestimate how important certain individuals like, like Kim Laurie were. And Larry Fisher for that matter. Uh, you know, to construct the return on a given stock, you need its dividends, right?

What if there were dividends in kind? Or what about shared dividends? Or what, what about mergers? I mean, how the hell do you deal with those? Well, you have to do it analytically, right? And you have to, in a time series, you've got, uh, things wandering through time in one form, and all of a sudden they take on a new form.

Or they spin off something. Uh, and he, of course, quickly realized that you had to go back and collect the dividend flow. So, it wasn't just the share price behavior per se, it had to include the dividends flow. So now that's another problem of considerable importance. Because the failure to get the dividends low misses about 40 percent of the data, 40 percent of the economics.

So, anyway, you just can't back to the point. I'm back to serendipity, right? You just can't imagine that kind of. Circumstances arising, but there they were, and there's lots of data available today, as you know, and various mathematical and statistical approaches, including, um, factor investing and momentum.

What, what experience do you have with like applying active approaches to asset allocation and like, like tactical, for example. Well, the whole idea around factor investing, it's also relatively new compared to what we were just talking about. And, of course, that's because when you write down to it, individual share prices are not mutually independent.

They jointly, they jointly depend upon common factors, but I mean, there wasn't that that kind of thinking wasn't even present then. When Gene Fama, uh, first started, uh, working on factor analysis. Uh, there were a lot of people around him, he didn't know what he was talking about to say nothing of the of the practicing world.

In fact, you know, factor analysis is still not that widely understood, but all all, of course, they are is the statistical. Uh, correlation partial correlation of implicit factors in in the overall. Otherwise, random behavior, but when you remove the factors, the residual is, in fact, random, but the fraction that is random is, is relatively small.

Once you get an understanding of the complete factor structure. Yeah, that gets you to probably 85 to 90 percent of the variance. I mean, you know, this is all fancy stuff relative to what came before. I'm glad you mentioned Gene Fama. Defianta knows something we want to discuss because, um, you know, Gene Fama called momentum as the ultimate anomaly.

Do you believe that there's opportunity to improve portfolio management using the momentum factor? Yes, I do. Sure. I'm especially curious because this is, you know, a lot of what SidePocket is built on as well. You know, because we apply momentum to portfolio management among other sophisticated mathematical techniques.

Well, it's a, that's a complicated... Subject, um, the reason I say that is, um, it's kind of, um, what do I want to call it? Uh, it's, um, we're imputing, um, uh, we're imputing behavior to correlation and there is a difference between correlation and behavior. And trying to sort out the distinction between those things.

So, momentum is a statistically demonstrable fact, but you, you might very well. I want to understand how that how that could arise. What has been overlooked in order for momentum to exist. Measure of market inefficiency and, uh.

You know, if enough people were fine tuned tuned enough in their analysis, it's an interesting question. Whether whether momentum would exist.

I have, I mean, I'm, I'm kind of agnostic about that point. I mean, otherwise, I've got to be predicting human behavior and that's the last thing I want to do.

Well, and with that, uh, you know, we're definitely in some interesting times to say, at least, you know, bear markets, volatility, some say we're even in a recession. Could you share with us your perspective on what's really going on and how that all makes sense to you?

That's outside of my scope of, uh, willingness to expect away. Sure, that's, you know, that's a, that's a problem that only data analysis will shed light on. And I think it's. I wouldn't want to go, you know, it's, it's not, um, it's not out of the realm of possible. It's not out of the realm of likely. The only question is how likely

well, like economically speaking, right? Like, um, the markets have seen previous decades where there was zero appreciation in the markets, high inflation stack inflationary environments. Do you think that investors today are prepared for this new high interest rate, high inflation environment where we might not see substantial appreciation equities?

Markets are never prepared for anything markets unfold. What kind of investment strategies do you see as being more appropriate in today's environment that you hold fiduciaries are exploring and learning more about? I don't have I don't have a view on that point. Dimensional fund advisors, which I'm still very involved with has something like 100 people doing research.

Well, if there wasn't something out there that was was conceivably. Of interest, why in the hell would you have 100 people doing research? So there's, there's, there's more in the wind, as it were, or in the tide or in the, whatever you want to call it, the relationship between. The 2, uh, that still suggests that we haven't uncovered the totality of what's going on.

And I'm, I dare say that's right. Max, so you've been through a lot of different decades and economic regimes. You've seen it firsthand, which. Uh, is more than many people could could say. And the markets have, um, there's, there's been decades where they call them the last decades. Uh, I guess what I'd be personally curious is how did you navigate as an investor those decades?

How do you navigate those kinds of environments? Well, you only know what you know, Danny. So, I guess I think Jack Bogle was right. When in doubt on the S and P 500, just start there. So some big fraction of your capital investment should be in the, just in in ground 0. And that's really ground 0, you wouldn't want to have even maybe 20 percent of your wealth.

Invested in a strategy that's a factor dependent. That would be that would be too much. I mean, at the margin. When you're trying to pull young people into the puzzle, I think what you guys are doing with side pocket is in many ways, bringing the efficacy of diversification and risk down to a level where a few hundred dollars can get you started.

You know, 25 or 50 years ago, I mean, nobody would have would have even dreamed of such a thing. That wouldn't have been in with a conceptually relevant or conceivable to say nothing of relevant scope of where we were heading or going at the time. I can remember when the minimum investment was 10 grand.

Yeah, well, it's it's a lot of things to change and, uh, what do you think my father would say if I showed him a cell phone? He died in 1974, he would, he would, he wouldn't even know what to say.

Yeah. How are you right now? Like, uh, investing today, the truth be known so much of the McClown family wealth is invested in dimensional fund advisors and Stonehenge farm. Uh, and if you take, if you add to that a few things that I've been interested in, where I've actually put up a modest amount of money, that's turned into a lot more.

In individual companies, I mean, I have 3 or 4 of those. That, uh, that have survived, uh, and, you know, I, I can, I can give you an example of 1. If you think I had any idea that this was going to be the outcome, I would never I could never have guessed that. But, um, let me, let me just. Let me just, um, tell you this story and this goes back about 20 plus years, I get an email one day from a very good friend of mine.

I've been around a long time. And, and Brad said, in my ear, I've just encountered a company that I think you will, would interest you in your, in your technically sophisticated brain, he accused me of having, and, uh, so I went and talked to the company. So what, what, what was the company? Okay. Well, what it had developed was a technology for dissociating carbon from hydrogen and methane, and that separation gave rise to a form of carbon that was artificial.

It doesn't occur in nature. It's called graphene. Well, graphing, you know, you can read about graphing these days and you can look it up on the web and you can get a picture of it, but. Just think about this for a second. Graphene has an affinity for electrons, that is to say, suppose you created an electronic circuit where the components were made out of graphene.

So, resistors, transistors, capacitors, wires were made from graphene instead of silicone. So you had all of the components of an electronic circuit, except they're now made out of graphene. What do you think the power consumption relationship is between a silicone circuit and a graphene circuit? 1, 000 to 1, the power consumption is 1, 1, 000.

That is silicone. Whoa, wait a minute. Suppose you create a battery.

I'm, I'm, I'm very fond of a little exercise that I've had here at the farm. Here's a battery, conventional battery, conventional technology. 1 cubic foot, suppose I contrast that with 1 cubic foot of hydrogen. Compressed to 3, 500 PSI.

How much energy density is, is in the hydrogen versus the battery? It's between 2 or 3 orders of magnitude more in the hydrogen.

Well, suppose I did the same thing with graphene. How much energy density do I have relative to a conventional battery? Not far from the same. It's between 2 and 3 orders of magnitude.

Well, while you're talking about a whole new ball game, right? Like the head of that company said to me, one of the days I drove up in my 1st Tesla. Well, if you're your car and our. Had batteries made from, uh, from graphene, instead of going 200 miles, that's how far my first Tesla would go. It would go 2000 miles, same size battery.

Coast to coast. Yeah.

So, I mean, you gotta, you gotta remember that we're subject to a lot of technological development in the material science part of it. Of the spectrum as well. It's not this is not just data and it's not just the scale of computers independent of their technology.

So anyway, I think it's, it's kind of, it's kind of not in the realm of easy to think about that, to grasp what, what would happen if you made batteries out of graphene now, by the way, what's happened to the company, of course. And I could see already the interest on the government military side, like so many applications.

That's exactly right. I mean, their, their first big contract was with the D O D D O E.

It was a research contract, right? Beyond investing also, um, is there anything else right now that's capturing your attention or curiosity that you'd like to share? Uh, we ever hear of Rhonda Fleming. No, you wouldn't. She was a very well known movie star a generation ago. Made, I don't know how many, a hundred films or something like that.

Well, would you believe that in

1960, about the same time I began fussing with computers, uh, my family took Me and my 2 sisters on a trip to the West Coast from the Chicago area. And 1 of my father's partners in the distribution business, uh, distribution side of their manufacturing business, uh, had a friend, uh, who was, uh, the head of a hotel chain.

On the West Coast, and we went. The Hollywood park to see a horse. In the 7th race, the feature race of the day that this crazy friend of my father's that won in a poker game. You think this is a crazy story, right? This is not made up. Well, who, um, who was 1 of, uh, his name was Jim noble who was who was 1 of Jim's friends.

The guy who was dating Rhonda Fleming, she was, she was in, uh, she was in the, uh, uh, the owner circle when that horse won the race. Who else was in the winner's circle besides Jim? And the jockey you amazing, that's awesome years and years and years later, I, I, I had a way to contact Rhonda and reminded her of that moment.

And, uh, she remembered it vividly and sent me that picture. Can't tell me that's not a pretty funny story. Yeah, it was that she sent me the picture. That was probably. About 15 years ago, it was in the days of KMV. Well, it, uh, good to see you guys and, uh, because you want to talk further about some of this as we can do that.

Okay, definitely. Thanks so much for your time. Oh, my pleasure, Danny. Take care, guys. Thanks, Mac. Take care. This podcast is sponsored by SidePocket, the only automated robo advisor on the market that combines multiple tactical asset allocation investment strategies to generate returns. If you don't have the time to professionally trade and you're tired of being at the whim of the market's ups and downs, consider using SidePocket to automate your investing.

SidePocket monitors the markets and automatically rebalances your holdings each month for you to maximize returns while protecting against loss. Losses are not a one to one relationship. When you lose 50 percent of your portfolio in a bad quarter, it requires 100 percent return the next just to break even.

That's why SidePocket applies sophisticated quantitative methods, including tactical asset allocation, to systematically minimize these drawdowns and consistently protect and grow your hard earned savings. To learn more, visit Side pocket dot com.