Bogleheads® on Investing Podcast 050: Craig Lazzara on active versus passive funds, host Rick Ferri

00:00:00.000 | (upbeat music)

00:00:02.580 | - Welcome everyone to the 50th edition

00:00:11.720 | of Bogleheads on Investing.

00:00:14.080 | Today, our special guest is Craig Lazera.

00:00:17.180 | Craig is the Managing Director

00:00:18.860 | in the Core Product Management Group

00:00:20.600 | at S&P Dow Jones Indices,

00:00:22.960 | where his responsibilities focus on providing

00:00:25.200 | thought leadership and educational outreach.

00:00:27.960 | Today, we're gonna be discussing the SPIVA report,

00:00:30.820 | S&P Index versus Active.

00:00:33.380 | (upbeat music)

00:00:35.960 | Hi everyone, my name is Rick Ferry

00:00:44.220 | and I'm the host of Bogleheads on Investing.

00:00:46.680 | This episode, as with all episodes,

00:00:48.840 | is brought to you by the John C. Bogle

00:00:50.880 | Center for Financial Literacy,

00:00:53.100 | a 501(c)(3) nonprofit organization

00:00:56.000 | dedicated to helping people make better financial decisions.

00:00:59.240 | Visit our newly designed website at boglecenter.net

00:01:03.020 | to find valuable information

00:01:05.040 | and to make a tax deductible contribution.

00:01:07.800 | And don't forget about our Bogleheads Conference

00:01:10.720 | coming up this October 12th through the 14th,

00:01:13.960 | featuring many speakers that I've had on this podcast

00:01:17.040 | and more.

00:01:17.880 | Today, our special guest is Craig Lazera.

00:01:20.640 | Craig is the Managing Director

00:01:22.080 | in the Core Product Management Group

00:01:23.640 | at S&P Dow Jones Indices,

00:01:25.700 | where his responsibilities focus on providing

00:01:27.840 | thought leadership and educational outreach.

00:01:30.920 | Today, Craig and I are discussing something

00:01:33.080 | near and dear to the Bogleheads' heart,

00:01:35.540 | the active versus passive debate.

00:01:38.680 | Should you only be using index funds

00:01:41.600 | or should you be using only active funds

00:01:44.500 | or should you be using a mixture

00:01:46.440 | of active funds and index funds?

00:01:49.920 | There's over a hundred years of data

00:01:52.780 | on active management versus index returns.

00:01:56.520 | And this data has been remarkably consistent

00:02:00.600 | through the entire 100 year period of time.

00:02:03.400 | Today, we're gonna be looking at the last 20 years

00:02:05.920 | when S&P has been publishing

00:02:08.160 | their detailed index versus active report

00:02:11.560 | and a second report which analyzes the persistence

00:02:15.200 | of outperforming actively managed mutual funds.

00:02:18.560 | So with no further ado,

00:02:19.960 | let me introduce Craig Lazera.

00:02:22.640 | Welcome to the Bogleheads on Investing podcast, Craig.

00:02:25.760 | - Rick, thank you.

00:02:26.600 | I'm delighted to be here.

00:02:28.200 | - Craig, we've known each other for a long time

00:02:30.220 | and you've been at S&P Dow Jones Indices for a long time.

00:02:35.220 | And I wanna get into a little bit of your background

00:02:37.560 | as to what brought you there.

00:02:39.160 | But in order to talk about S&P Dow Jones Indices,

00:02:43.960 | I think we need a little explanation

00:02:45.780 | of who is S&P Dow Jones Indices

00:02:48.520 | and why is it S&P Dow Jones rather than just S&P.

00:02:52.840 | - Well, as you suggest, Rick,

00:02:54.680 | they're originally two different entities.

00:02:57.520 | S&P, of course, stands for Standard & Poor's

00:03:00.240 | and the roots of the Standard & Poor's company

00:03:02.780 | go back to the, I think, the 1860s

00:03:05.040 | when Henry Varnum Poor published the first railroad ratings.

00:03:08.720 | And S&P obviously is well known for its ratings business.

00:03:12.960 | There was a company called Standard Statistics

00:03:16.040 | that I believe in 1923 began to publish an index

00:03:21.040 | of the U.S. stock market.

00:03:23.080 | I think it had a relatively small number of names

00:03:27.440 | and it was the first capitalization weighted index

00:03:30.960 | ever computed on a daily basis.

00:03:32.880 | And I believe that was 1923.

00:03:35.280 | Somewhere along the line,

00:03:37.420 | the Standard Statistics company

00:03:39.060 | and Henry Varnum Poor's ratings company merged.

00:03:41.480 | That's where it came to be Standard & Poor's.

00:03:44.600 | And then this initial cap weighted index product

00:03:47.800 | that I mentioned, that started in 1923,

00:03:50.840 | morphed by, that we added names as,

00:03:53.360 | or they added names as computing capabilities allowed.

00:03:57.260 | But in 1957, I believe in March, 1957,

00:04:00.840 | the Standard & Poor's company launched

00:04:03.400 | what was called then the Standard & Poor's 500,

00:04:05.760 | now the S&P 500.

00:04:07.300 | And so the history of the 500 goes back to March, 1957.

00:04:12.480 | And then obviously has come forward from there.

00:04:15.080 | On the other side of the merger,

00:04:16.760 | Mr. Dow, Mr. Jones started publishing their iconic indices.

00:04:20.920 | I think in the 1880s, the Dow Jones Industrial Average

00:04:24.000 | started I believe in 1896,

00:04:25.960 | but the transportation average

00:04:27.640 | is actually a bit older than that.

00:04:30.060 | Those were price weighted,

00:04:31.460 | meaning in the days when the only computational equipment

00:04:35.360 | you had was a piece of chalk and a blackboard,

00:04:37.440 | you could add up the names or the prices of,

00:04:39.600 | I think it was a dozen stocks to begin with,

00:04:41.800 | and divide by 12 and got your answers.

00:04:44.720 | So the Dow Company, it'll continue to evolve.

00:04:47.480 | And obviously as indices and index funds

00:04:52.400 | became more important in the investing landscape,

00:04:55.920 | both companies developed substantial businesses

00:04:58.420 | in licensing indices

00:05:00.200 | for the creation of investment products.

00:05:03.200 | In 2012, what was then called S&P Indices,

00:05:09.000 | acquired Dow Jones Indexes

00:05:11.720 | and formed S&P Dow Jones Indices,

00:05:15.240 | which is technically now a joint venture company,

00:05:18.720 | about three quarters owned by S&P Global,

00:05:21.600 | which is the parent company

00:05:22.640 | that evolved out of the old S&P,

00:05:25.160 | and one quarter by the Chicago Mercantile Exchange.

00:05:27.880 | So we've been S&P Dow Jones Indices since 2012.

00:05:32.760 | It's interesting because both S&P

00:05:34.960 | had a total stock market index

00:05:36.880 | and Dow Jones had a total stock market index.

00:05:39.240 | And I think the difference between the two indices,

00:05:41.200 | even though they both have 4,200 names,

00:05:44.600 | one of them might have 4,201 names

00:05:47.440 | and the other one might have 4,200 names.

00:05:50.360 | I mean, they're so similar.

00:05:51.600 | But when you're looking at something like

00:05:54.120 | the Fidelity Total Stock Market Index

00:05:57.080 | or iShare Total Stock Market Index,

00:05:59.200 | one of them actually tracks

00:06:00.300 | the S&P Total Stock Market Index Fund

00:06:02.440 | and the other one tracks

00:06:03.280 | the Dow Jones Total Stock Market Index Fund,

00:06:05.480 | or the Total Stock Market Index, I should say.

00:06:07.640 | And they're so very similar.

00:06:10.600 | - Yeah, no, I remember when we did the merger,

00:06:14.600 | I was product manager for our U.S. equity products,

00:06:17.400 | which basically means the S&P 500.

00:06:19.560 | And I remember looking at those very things.

00:06:21.120 | You graph the S&P Total Market Index

00:06:23.600 | and the Dow Total Market Index.

00:06:25.440 | And if you could put a spec between the graphs,

00:06:29.200 | you were lucky.

00:06:30.760 | I mean, they both include all the stocks

00:06:32.560 | you can grab hold of and they're cap-weighted.

00:06:34.880 | So it's gonna come out to the same thing

00:06:37.880 | if you do the numbers correctly.

00:06:39.440 | - Yeah, on this point, get outside of Dow Jones S&P

00:06:43.920 | and let's go to CRISP,

00:06:46.600 | which is the indices that Vanguard uses

00:06:50.360 | for their total stock market.

00:06:51.680 | It's a University of Chicago

00:06:54.400 | Center for Research and Security prices, CRISP.

00:06:57.480 | Same thing, I mean, very, very similar tracking.

00:07:00.120 | Almost all the same stocks,

00:07:03.000 | maybe entering the index,

00:07:04.520 | coming out of the index slightly different,

00:07:06.200 | but negligible.

00:07:08.600 | And then even if you look at the Morningstar

00:07:11.320 | Broad Market Index and all of these companies

00:07:14.840 | that are creating indices for, say,

00:07:17.320 | the total stock market or the broad U.S. market.

00:07:19.960 | The broad market would be a little bit

00:07:21.360 | of a smaller sub-component than the total market,

00:07:23.520 | but they're all very similar.

00:07:25.160 | I mean, the correlation's at 99%

00:07:27.960 | and there might be a 0.1% difference in return,

00:07:31.280 | but they're all very close.

00:07:33.280 | That's the history of S&P Dow Jones indices.

00:07:37.560 | And what about your history?

00:07:39.120 | I mean, how did you end up there?

00:07:41.560 | - Yeah, well, I went to college at Princeton.

00:07:43.680 | I majored in what we called then

00:07:45.440 | public affairs and economics.

00:07:47.720 | Went from there to Harvard Business School

00:07:50.560 | and graduated in, well, more years ago

00:07:53.040 | than I care to admit to, but quite some time ago.

00:07:56.120 | And did a year with a consulting firm in Boston

00:07:59.520 | and then joined an investment management company

00:08:02.360 | and have been in the investment management business

00:08:04.400 | ever since.

00:08:05.360 | - And you received your Charter of Financial Analysts

00:08:08.440 | charter along the way?

00:08:09.560 | - Oh, yeah, yeah.

00:08:11.040 | I received the charter in 1983, yes.

00:08:13.840 | I have a four-digit charter number,

00:08:15.760 | which, as you know, Rick, is pretty low.

00:08:18.040 | - Oh, let me think.

00:08:19.360 | Do I?

00:08:20.200 | No, I don't, I don't, no.

00:08:21.200 | You're ahead of me. (laughs)

00:08:24.480 | And then you went into work in the investment industry.

00:08:27.280 | - I started my career in the investment business

00:08:29.960 | on what we'd call the buy side,

00:08:31.320 | as in investment management companies,

00:08:33.720 | and spent a number of years at a variety of firms,

00:08:37.720 | tending to specialize in what was then considered

00:08:41.880 | quantitative analysis.

00:08:43.200 | I think by today's standards, it wasn't all that advanced,

00:08:46.680 | but by 1984 standards, it was pretty good.

00:08:49.960 | And so I was an equity quant for a while.

00:08:53.160 | And in the mid '90s, I had the chance to join

00:08:56.000 | the old Salomon Brothers brokerage firm,

00:08:57.840 | kind of in a job that related to marketing

00:09:01.240 | and use of their quantitative research.

00:09:04.560 | Salomon had started, in 1989, a set of indices

00:09:09.560 | that were designed to be float market cap weighted indices

00:09:13.880 | of the entire global stock markets.

00:09:16.280 | I got to know that group.

00:09:17.480 | They were related.

00:09:18.320 | I eventually transferred into the department

00:09:20.440 | that managed and maintained those indices.

00:09:22.960 | And those indices were ultimately acquired

00:09:26.640 | by Standard & Poor's in 2002, I believe.

00:09:30.040 | Now, I had left Salomon prior to the acquisition,

00:09:33.160 | but luckily I've met a number of people at S&P,

00:09:36.600 | and when they were looking for some senior help

00:09:40.240 | in 2008, 2009, I was available

00:09:44.560 | and were able to make the match.

00:09:46.400 | So I ended up joining S&P in June of 2009.

00:09:51.160 | - So now you're at S&P Indices,

00:09:55.280 | which became S&P Dow Jones Indices.

00:09:57.840 | And you and I have been talking for many years

00:10:02.560 | about the results of the SPIVA report

00:10:07.320 | that was started 20 years ago.

00:10:09.760 | So let's talk about what SPIVA stands for

00:10:14.200 | and why this report got started and why it's important.

00:10:19.680 | Easy question for SPIVA stands for S&P Index Versus Active.

00:10:23.920 | It's an acronym.

00:10:25.440 | Sometimes in the UK pronounced SPIVA.

00:10:27.800 | So take your pick, depending on where our listeners are.

00:10:31.120 | But the point of SPIVA, then 20 years ago and now,

00:10:35.600 | is to ask the question,

00:10:36.960 | how have actively managed funds performed

00:10:41.320 | relative to benchmarks that are appropriate

00:10:44.120 | for their investment styles?

00:10:45.520 | What I mean by that is if you're trying to evaluate

00:10:48.280 | how large-cap US managers have done,

00:10:50.800 | you compare them to the S&P 500.

00:10:52.600 | You want to ask how mid-cap managers have done,

00:10:56.080 | you might compare them to the S&P mid-cap 400

00:10:58.640 | and compare growth managers to a growth index,

00:11:00.800 | value of value index, and so forth.

00:11:03.640 | But that was the notion that underlay SPIVA in 2002

00:11:07.920 | when it was first published.

00:11:09.000 | And that is the notion that we continue with today.

00:11:12.240 | - So when you talk about managers,

00:11:13.560 | you're talking specifically about mutual funds

00:11:16.120 | and exchange-traded funds, correct?

00:11:18.600 | - In the case of US SPIVA, yes.

00:11:20.960 | Really, mutual funds, actively managed mutual funds.

00:11:25.960 | The data for which we access from a database

00:11:29.040 | called the CRISP, you mentioned CRISP earlier,

00:11:32.200 | the Center for Research and Security Prices

00:11:34.240 | at the University of Chicago.

00:11:35.920 | CRISP maintains what is called

00:11:38.200 | a survivorship bias-free database of fundraisers.

00:11:43.040 | It's a mouthful to say, right?

00:11:44.200 | A CRISP survivorship bias-free database.

00:11:47.640 | In the initial generations of SPIVA,

00:11:50.320 | it was focused on mutual funds.

00:11:52.200 | We've expanded it somewhat institutionally since then.

00:11:55.120 | You think of it as a mutual fund service initially,

00:11:59.040 | and you won't go wrong.

00:12:00.440 | - Let's talk about the CRISP survivorship bias-free database

00:12:05.360 | because it was an important database.

00:12:07.720 | This didn't exist until 1997 when Mark Carhart,

00:12:13.320 | who was a University of Chicago Booth School of Business,

00:12:18.240 | a PhD student, was doing a study

00:12:21.960 | on mutual funds for his thesis,

00:12:24.040 | and there wasn't any good historic mutual fund database

00:12:29.040 | that captured all of the mutual funds going back in history

00:12:34.440 | because so many mutual funds have merged

00:12:36.640 | or gone out of business.

00:12:38.520 | So what he did with funding from Gene Fama,

00:12:43.360 | Nobel Laureate Gene Fama from the University of Chicago,

00:12:47.160 | went out as part of his PhD thesis,

00:12:50.680 | gathered information on mutual funds going back,

00:12:55.680 | as far back as mutual funds existed in the United States,

00:13:00.480 | and most of them have gone out of business over time.

00:13:04.040 | It's included in the database.

00:13:05.320 | So it is a survivorship bias-free.

00:13:08.480 | And the idea of survivorship bias,

00:13:11.480 | could you explain what that is?

00:13:12.720 | In a mutual fund database

00:13:14.000 | that doesn't have a survivorship bias-free,

00:13:16.480 | what happens with survivorship bias in these databases?

00:13:20.320 | - Let's suppose I'm working with a database

00:13:22.720 | that is not adjusted for survivorship bias,

00:13:24.840 | so it has survivorship bias built in.

00:13:26.920 | What that means is I'm only able to look at funds

00:13:30.560 | that exist today.

00:13:33.160 | If I want to say, how did these funds perform

00:13:35.840 | over the past 10 years, for example, the past 20 years,

00:13:39.080 | I'm only looking at the funds

00:13:40.800 | that did well enough to survive for 10 or 20 years.

00:13:44.960 | What you really want to do and what this database does,

00:13:47.560 | as you suggest, is I want to be able to go back 20 years

00:13:51.040 | and say of all the funds that existed then, how did they do?

00:13:54.960 | Because as you pointed out, Rick,

00:13:56.880 | a fair number of them do not survive.

00:13:59.840 | So if you examine only the returns of currently active funds,

00:14:04.760 | you're building in a bias

00:14:07.720 | because you're only looking at the funds

00:14:09.440 | that were most successful, or to be precise,

00:14:12.240 | that were successful enough to survive.

00:14:15.320 | And we know very well that there are many funds

00:14:17.480 | that are not successful enough to survive.

00:14:20.040 | And since you didn't know which ones those were 20 years ago,

00:14:24.000 | the only fair way to do an evaluation

00:14:25.840 | is to use a survivorship bias adjusted database

00:14:30.080 | to go back 20 years

00:14:31.320 | and see what the fund landscape was like then.

00:14:34.120 | And in your SPIVA report, you have all this information

00:14:37.400 | for all the different categories of how much,

00:14:40.200 | over one, three, five, 15, 20 year period of time,

00:14:43.840 | how many of these funds survived or changed styles.

00:14:48.000 | That's another thing that you look at as well.

00:14:49.600 | Some funds that say that their large cap growth

00:14:53.080 | end up being something else,

00:14:54.880 | or maybe they start out as mid-cap core,

00:14:57.600 | end up becoming something else.

00:14:59.280 | So you also track a style survivorship.

00:15:02.360 | Yeah, and that's important.

00:15:03.920 | You might have a fund, let's say a successful fund,

00:15:05.880 | starts out as a mid-cap growth fund.

00:15:08.160 | It's successful.

00:15:09.080 | It begins to acquire assets.

00:15:10.680 | And the manager thinks,

00:15:11.520 | "Well, if I'm gonna continue to be a mid-cap growth fund,

00:15:15.040 | "I've gotta stop taking assets,

00:15:17.200 | "or else maybe I should migrate up the cap scale

00:15:20.080 | "and use larger stocks,

00:15:21.600 | "which then I'm able to deploy the liquidity

00:15:25.400 | "that I now have access to."

00:15:27.160 | So yeah, that's a natural sort of thing to happen,

00:15:31.000 | can happen organically as funds grow,

00:15:33.520 | or as managers change.

00:15:35.560 | You know, manager tenure is limited in most of these places.

00:15:38.960 | So things change.

00:15:40.480 | So you looked at all of this.

00:15:42.640 | I mean, you were very unbiased.

00:15:44.200 | That's how you tried to do it.

00:15:45.080 | You had to do it as fair as you possibly could

00:15:48.080 | to try to say, "Okay, let's take these funds

00:15:52.360 | "and compare them to this indice.

00:15:55.280 | "And let's see how many survived,

00:15:56.920 | "how many were style consistent,

00:15:59.320 | "and how many outperformed the index,

00:16:04.240 | "and how many underperformed."

00:16:06.280 | And here, the data gets interesting

00:16:08.960 | because it is so consistent,

00:16:13.280 | not only over the last 20 years,

00:16:14.800 | but I've done a lot of work on this.

00:16:16.600 | It goes back 100 years, how consistent this data is.

00:16:19.560 | So why don't you tell us.

00:16:21.000 | >> Yeah, the quickest way to answer your question,

00:16:23.360 | I think the adequate answer, a good answer is,

00:16:26.960 | what the data show is that most active managers

00:16:30.560 | underperform most of the time.

00:16:32.600 | So if you look for the less of the last 20 years of SPIVA

00:16:37.080 | for the large cap US manager category,

00:16:40.920 | which is the largest category, that's why I picked that one.

00:16:43.680 | If you compare those managers to the S&P 500 year by year,

00:16:48.680 | I think the average is something like 64%,

00:16:55.200 | in the average year, 64% of the large cap US managers

00:17:00.200 | underperform the S&P 500.

00:17:03.040 | Now, some years it's more, some years it's less.

00:17:05.080 | The last year in which a majority of large cap managers

00:17:10.400 | outperformed the S&P 500 was 2009.

00:17:13.880 | In the 20 years of history of SPIVA,

00:17:17.080 | I think there were only three years

00:17:19.240 | when a majority outperformed.

00:17:21.800 | So the result is, you know, suggests,

00:17:26.800 | and you, Rick, you alluded to all the research

00:17:29.960 | that goes back, you know, 100 years,

00:17:32.560 | and, you know, a lot of research was published on this topic

00:17:36.000 | before index funds started in the '60s and '70s.

00:17:38.960 | It's a very consistent finding

00:17:40.760 | that most active managers underperform a benchmark

00:17:45.360 | that is appropriate to their investment style.

00:17:47.880 | - Now, one of the things that occurs

00:17:50.400 | is that because less than 50% outperform,

00:17:55.400 | that over time, this compounds.

00:17:59.560 | So by the time you get out to, say, five years,

00:18:03.080 | it's more than 65%.

00:18:05.920 | - Exactly right.

00:18:06.760 | In fact, we just released in early September

00:18:10.760 | our mid-year SPIVA.

00:18:12.480 | So it covers the first six months of 2022.

00:18:16.160 | So for the, and it was a relatively good six-month period.

00:18:20.880 | Only, you know, 51% of the large cap managers underperformed.

00:18:25.600 | And that's really quite good,

00:18:27.600 | considering the history I mentioned to you.

00:18:29.440 | Going back a full year, so the year ended June 30th, 2022,

00:18:34.640 | 55% of large cap managers underperformed.

00:18:38.200 | Going back five years, 84% underperformed.

00:18:41.720 | Going back 20 years, 95% underperformed.

00:18:44.840 | So, and this is, again, very, very common.

00:18:47.640 | Whatever you look at SPIVA data,

00:18:49.080 | you see that the percentage of underperformers goes up

00:18:54.080 | as the time horizon extends back.

00:18:58.160 | You see the same thing if you look at mid-cap managers

00:19:00.760 | or small-cap managers or growth specialists

00:19:02.600 | or value specialists.

00:19:03.440 | Very common effect.

00:19:04.680 | - So this is important for investors

00:19:07.280 | who are long-term investors.

00:19:08.480 | I mean, if you're gonna pick an active fund,

00:19:11.360 | the longer you hold it,

00:19:12.560 | the lower the probability it'll outperform the indice.

00:19:16.640 | - Yeah.

00:19:17.800 | - So if you're gonna be a long-term investor,

00:19:19.360 | if you just bought an index fund,

00:19:21.560 | the longer you hold it,

00:19:23.800 | the higher the probability that index fund

00:19:26.840 | will outperform the active managers in that category.

00:19:30.400 | - Absolutely.

00:19:31.240 | Another way to say that is,

00:19:32.680 | if you look, say, at our large-cap US category,

00:19:34.800 | where 95% of the funds underperform the S&P 500

00:19:39.080 | over the past 20 years,

00:19:40.840 | to turn that on its head and say,

00:19:42.080 | if you were an investor choosing a large-cap fund

00:19:46.600 | 20 years ago,

00:19:48.160 | the chance that you chose one that did better

00:19:50.840 | than the index was one in 20.

00:19:53.360 | So the odds are really quite overwhelming

00:19:56.360 | in the historical data

00:19:57.920 | that the longer you're holding period,

00:20:00.080 | the more likely it is that an active manager underperformed.

00:20:05.080 | - Well, let's go to the next dimension of this,

00:20:07.840 | and that is that small minority that did outperform,

00:20:12.320 | that five or 10% that did outperform,

00:20:15.360 | they didn't outperform by very much

00:20:18.480 | relative to the 90% that underperformed.

00:20:22.480 | And the reason I say this is because

00:20:24.480 | if the payoff for picking a manager that outperformed

00:20:27.800 | was like 10% a year excess return,

00:20:31.040 | then it might be worth trying to find those 10% managers

00:20:35.320 | because the payoff is so great.

00:20:37.960 | But that's not what happens.

00:20:40.360 | The payoff, if you actually find one of those 10%,

00:20:44.400 | is significantly smaller than the 90% that underperformed,

00:20:49.200 | how much they underperformed by.

00:20:51.320 | So not only is it very difficult to find managers

00:20:55.720 | who are gonna outperform,

00:20:56.600 | and we'll get into that in a bit,

00:20:58.800 | but the payoff for doing it, you're not getting paid.

00:21:02.080 | - No, exactly.

00:21:03.200 | I mean, the odds are against you.

00:21:05.120 | I mean, I think that that's a fair thing to say

00:21:08.160 | to an investor who's contemplating,

00:21:11.040 | should I invest in an index fund

00:21:14.200 | or in an active manager who follows the same style?

00:21:18.400 | If you want to invest in an active, that's fine,

00:21:20.640 | but realize the odds are against you.

00:21:23.360 | - So there's a low probability of picking a manager,

00:21:27.200 | and then the payoff is low.

00:21:30.000 | You also, though, look at this and say,

00:21:32.160 | well, some of those funds that are not performing well

00:21:35.400 | don't have much money in them,

00:21:36.720 | so why are we even counting them in this study?

00:21:39.720 | So you actually do the study two different ways.

00:21:42.600 | You do it based on equal weighting all of the funds,

00:21:46.320 | as though all these active funds

00:21:48.040 | have the same amount of money in them.

00:21:50.000 | And then you do cap weighting,

00:21:51.800 | which is, okay, let's look at the funds

00:21:53.520 | and see how much money there is actually in there

00:21:55.760 | so we can really find out what the investor experience is.

00:21:58.640 | So could you explain the difference between the two?

00:22:01.080 | - If I'm trying to answer the question,

00:22:02.760 | how did the average large cap manager do?

00:22:05.680 | I've got, you know, let's say 1,000,

00:22:07.840 | I'm making the number up here, 1,000 funds to evaluate.

00:22:12.160 | One way to do it, as you say,

00:22:13.520 | is take the returns of all 1,000,

00:22:15.480 | add them up, divide by 1,000.

00:22:16.840 | That's equal weighted.

00:22:18.280 | The problem with doing that is that you may have,

00:22:21.160 | you know, some funds that control $50 billion

00:22:24.280 | and another fund that controls $1 billion

00:22:26.760 | and you're treating them as if they're equally important.

00:22:29.480 | So the other way to do it is what we call asset weighting,

00:22:32.740 | where you weight each fund's return

00:22:34.880 | by the amount of money

00:22:36.440 | or the amount of assets that the fund has,

00:22:38.560 | and then divide by the total value

00:22:40.960 | of all the assets across all the funds.

00:22:43.240 | So that gives you an asset-weighted rate of return.

00:22:46.420 | And the difference between those numbers

00:22:49.160 | will tell you whether the larger funds are doing better

00:22:52.240 | or doing worse than the smaller funds.

00:22:54.200 | If the asset-weighted number is 12%

00:22:57.420 | and the equal weighted number is 10,

00:22:59.320 | that tells you the larger funds, or at least some of them,

00:23:01.640 | the larger funds did better

00:23:03.200 | and the reverse if the numbers are reversed.

00:23:05.740 | And over the long enough time horizon,

00:23:08.260 | what you quite often see is that both the simple average

00:23:13.120 | and the asset-weighted average return of these funds

00:23:17.520 | is less than that of the index to which is being compared.

00:23:22.200 | - But how much of a difference is it?

00:23:23.480 | I mean, is it better to get in bigger active funds

00:23:26.280 | than smaller active funds?

00:23:28.200 | - Depends on the period.

00:23:29.360 | I don't think I could make a blanket judgment about it.

00:23:32.240 | This last six months, for example,

00:23:34.120 | I mentioned we just published a SPIVA

00:23:35.760 | for the first six months of 2022.

00:23:37.700 | The last six months,

00:23:39.120 | the asset-weighted average did less well

00:23:42.640 | than the equal weighted average,

00:23:44.140 | meaning the smaller funds did better.

00:23:47.520 | But there have been years when the reverse has been true.

00:23:50.680 | - So it's probably pretty equal then over time.

00:23:53.120 | - I would think so, yeah.

00:23:54.480 | I mean, I would think so.

00:23:56.360 | - And you've also done,

00:23:57.800 | you've added this over the last few years.

00:23:59.800 | You tried to do some risk-adjusted numbers.

00:24:02.960 | So you're looking at sharp ratios,

00:24:05.960 | which is a function of adjusting for,

00:24:09.440 | call it the volatility of the funds.

00:24:11.320 | - Yes.

00:24:12.580 | - And what has that shown?

00:24:13.800 | Has that, in other words, are the active funds,

00:24:16.640 | even though they're underperforming, they're less volatile?

00:24:19.120 | Is that a factor?

00:24:20.280 | - No, that is, in fact, just the opposite.

00:24:22.480 | The average actively managed fund in the SPIVA database

00:24:26.920 | is more volatile than the index

00:24:30.040 | to which it's being compared.

00:24:31.180 | Tim Edwards and I did a paper about this

00:24:33.000 | some years ago now.

00:24:34.360 | It's called "The Volatility of Active Management."

00:24:36.640 | It's very clear that most active managers

00:24:40.240 | do not produce less risk than the benchmarks

00:24:44.040 | that you're comparing them to.

00:24:45.440 | The one thing that is particularly interesting

00:24:48.100 | about that paper, as I'm recalling it,

00:24:50.280 | is that unlike returns,

00:24:53.120 | where a manager might have good returns one year

00:24:56.400 | and bad returns the next, and that fluctuates,

00:24:59.880 | the volatility profile of funds is fairly stable.

00:25:03.320 | So if you have a fund that is relatively more volatile,

00:25:06.200 | say a large-cap fund, relatively more volatile

00:25:08.640 | than the S&P 500 this year,

00:25:10.680 | it's probably gonna be more volatile

00:25:12.440 | next year and the year after that,

00:25:13.680 | and probably the same for the years in the past.

00:25:15.600 | So there's some stability in volatility of active funds,

00:25:19.040 | but there's no, I mean, some funds, yes, are less volatile,

00:25:23.920 | but the majority are not.

00:25:26.120 | - So you also do this for international equities

00:25:32.080 | and for U.S. bonds.

00:25:34.960 | So let's start with the international equities first,

00:25:37.880 | which is a completely different database.

00:25:39.680 | I mean, you're in a completely different market.

00:25:41.320 | It has nothing to do with the U.S.,

00:25:43.120 | nothing to do with the original U.S. SPIVA.

00:25:46.720 | Is the data the same?

00:25:48.040 | - The conclusion is the same, yes.

00:25:50.920 | I mean, I can answer the question really in two ways.

00:25:53.840 | One is that as part of U.S. SPIVA,

00:25:56.660 | we look at international funds or global funds

00:25:59.680 | that trade in the U.S., so same database,

00:26:02.400 | and that gets you the same answer.

00:26:04.760 | But then our business has expanded internationally.

00:26:06.840 | Other clients in other regions have said,

00:26:08.880 | well, what about Canada?

00:26:10.440 | What about Europe?

00:26:11.280 | What about Australia?

00:26:13.160 | And so we've begun to publish,

00:26:15.440 | we have now published SPIVA, I think,

00:26:17.800 | in 10 or 12 different regions,

00:26:19.960 | Europe, Australia, Latin America, Canada.

00:26:24.680 | I'm sure I'm leaving things out, India.

00:26:28.560 | And the conclusion is remarkably consistent.

00:26:33.560 | I mean, there are exceptional years, yes,

00:26:35.840 | in all these places.

00:26:37.520 | But if you look at long periods of time,

00:26:39.880 | even an interval as short as five years,

00:26:42.360 | the majority of active managers underperform

00:26:46.240 | a benchmark that is appropriate to their investment style.

00:26:49.680 | It's very, very consistent.

00:26:52.560 | - So let's go back, circle back to,

00:26:54.840 | investors here in the U.S. are investing

00:26:56.800 | in international or foreign stocks

00:27:00.360 | through foreign mutual funds

00:27:02.080 | that are managed here in the U.S.

00:27:05.200 | - The conclusion is the same.

00:27:06.520 | Yeah, there's no evidence that the managers

00:27:10.120 | of international funds do any better

00:27:11.760 | than the managers of domestically focused funds.

00:27:13.800 | - Okay, I have to ask you this,

00:27:14.760 | because a lot of people say,

00:27:16.200 | well, yes, but not in emerging markets.

00:27:18.080 | I mean, emerging markets, you can go out

00:27:19.400 | and you can find active managers

00:27:20.560 | that are gonna outperform.

00:27:21.640 | I mean, do you find that to be true?

00:27:23.000 | - Well, some, sure.

00:27:24.400 | But there's no consistency there either.

00:27:26.680 | I mean, I think the thing that makes SPIVA results

00:27:32.160 | what they are is that in most markets,

00:27:37.080 | including emerging markets,

00:27:39.100 | including all of the international markets I mentioned,

00:27:42.040 | in most markets, the investment business

00:27:45.160 | is very largely institutionalized.

00:27:48.200 | Most of the money is controlled and managed

00:27:51.960 | by institutional, by which I mean mutual fund,

00:27:55.960 | pension fund, endowments, professional asset managers,

00:28:00.400 | which means that the managers of emerging market funds

00:28:05.100 | or the managers of Canadian funds,

00:28:06.280 | the managers of U.S. funds

00:28:07.600 | are competing against other professionals

00:28:10.960 | who have the same skillset, information access,

00:28:15.960 | computational ability, knowledge of the markets.

00:28:19.160 | It's a fair game.

00:28:20.720 | It's not like, as it might have been in the '50s,

00:28:23.460 | for example, it's not like one set of investors,

00:28:26.640 | the professionals, have access to information

00:28:28.960 | and trading data that is superior to that of others.

00:28:32.960 | And so the ones with superior knowledge

00:28:35.040 | can take advantage of the ones with less knowledge.

00:28:37.920 | Here, it's professionalized pretty much across the board.

00:28:41.920 | - Well, here in the U.S. anyway.

00:28:43.780 | - Certainly in the U.S., and increasingly globally too, yeah.

00:28:47.200 | - But let's get into fixed income.

00:28:48.500 | So you also do this with fixed income.

00:28:50.980 | You look at treasury indices versus managed treasury funds

00:28:56.440 | and corporate bond funds versus corporate bond indices.

00:29:00.480 | And does the data hold there?

00:29:02.960 | - Yes, although I would say it's more volatile there

00:29:07.820 | because in the following sense,

00:29:10.460 | if you're in an environment where interest rates

00:29:13.400 | are increasing, as we have been now,

00:29:16.880 | if most fixed income managers

00:29:19.800 | have a duration in their portfolio

00:29:22.360 | that is less than that of the index

00:29:24.200 | to which they're being compared,

00:29:25.960 | then a majority can outperform

00:29:28.800 | in periods when rates are increasing,

00:29:30.480 | and they will then underperform

00:29:32.880 | in periods when rates are decreasing.

00:29:34.880 | So the importance of the maturity/duration decision,

00:29:39.560 | what's the average maturity of the bonds in the portfolio,

00:29:42.080 | is what I mean by duration.

00:29:43.960 | The importance of duration in fixed income analysis

00:29:47.720 | is just overwhelming.

00:29:49.560 | If you get that decision right, you can get a lot wrong

00:29:52.280 | and still be a really good bond manager.

00:29:54.360 | And because it's so important,

00:29:56.360 | I think you see more fluctuation.

00:29:58.560 | You'll see, in some categories, a large majority

00:30:01.640 | outperform one measurement period

00:30:03.720 | and then underperform in the next measurement period

00:30:06.140 | simply because the direction of interest rates is changing.

00:30:11.040 | The other thing to keep in mind about fixed income markets,

00:30:14.720 | certainly government, the treasury markets in particular,

00:30:18.280 | is unlike the equity markets

00:30:21.200 | where basically all of the players are,

00:30:25.080 | you know what an economist might call,

00:30:26.840 | rational profit maximizer.

00:30:28.600 | My fund, I'm trying to make a lot of money.

00:30:31.440 | Your fund, you're trying to make a lot of money.

00:30:33.280 | In the fixed income market,

00:30:34.640 | there is one very large player

00:30:36.480 | who is not a profit maximizer,

00:30:37.920 | that being the Federal Reserve.

00:30:39.040 | So you have another presence in fixed income

00:30:44.040 | that sometimes, depending on his interest rate decision,

00:30:47.040 | sometimes helps the managers,

00:30:49.020 | sometimes hurts the managers.

00:30:50.840 | But there is this other factor

00:30:53.000 | which you don't see in the equity world.

00:30:55.040 | - I guess it would be also a little different

00:30:57.480 | because the dispersion of returns among bond managers

00:31:02.480 | is going to be much narrower.

00:31:04.800 | - Much lower, much lower.

00:31:06.360 | - Than the dispersion of returns of equity managers.

00:31:08.840 | - Yeah, oh, very much so, very much so.

00:31:11.000 | In fact, David Swenson, the late head of the Yale endowment,

00:31:15.440 | I think at one point was quoted as saying,

00:31:18.000 | the difference in performance

00:31:20.240 | between a top decile bond manager

00:31:22.040 | and a bottom decile bond manager

00:31:24.040 | was so small that it wasn't worth your time

00:31:26.080 | to try to figure out who was who.

00:31:27.200 | - (laughs) Very good.

00:31:29.920 | Okay, so Spiva, congratulations on 20 years of data.

00:31:34.640 | I will say that I've been following this market

00:31:37.360 | for 30 years, 35 years.

00:31:39.360 | And it's remarkable that what S&P Dow Jones has done,

00:31:45.520 | your data has correlated so highly with others

00:31:49.840 | like Morningstar does the same study and the same results.

00:31:54.840 | Now they might use different indices,

00:31:56.680 | but it's the same result.

00:31:58.280 | And Vanguard does a annual study too.

00:32:02.560 | And it's the same result.

00:32:03.960 | So they're using different indices,

00:32:07.200 | but again, it doesn't really matter that much.

00:32:09.560 | The results all come out to about the same

00:32:13.560 | and the results are this,

00:32:16.200 | can active funds beat the benchmark?

00:32:19.080 | The answer is yes, but not many,

00:32:24.080 | not by much, not for long,

00:32:28.400 | and the winners are not predictable.

00:32:31.640 | And with that, let's get into the second study that you do,

00:32:36.000 | which is called a persistence study.

00:32:38.320 | So tell us about persistence.

00:32:41.120 | - Okay, the persistence scorecard.

00:32:43.400 | So-called uses the same database as SPIVA.

00:32:47.200 | So it's the same CRISP survivorship bias-free database.

00:32:52.200 | And the question that we asked in the persistence scorecard

00:32:57.280 | is really very simple.

00:32:58.600 | It says, for example, let me identify all of the funds

00:33:03.600 | who were above average two years ago

00:33:08.080 | and say of those that were above average two years ago,

00:33:10.440 | how many were above average last year?

00:33:12.880 | We go back five years and say,

00:33:14.520 | of those who were above average five years ago,

00:33:16.240 | how many were above average four years ago,

00:33:18.160 | three years, two years, one, and so forth?

00:33:20.360 | - So what you're trying to measure here then

00:33:22.240 | is it does the outperformance carry forward?

00:33:24.800 | - Yeah, exactly, exactly.

00:33:26.840 | Recognizing that only a minority of active managers

00:33:31.840 | outperform in a given year,

00:33:34.000 | if I focus on the more successful active managers

00:33:37.480 | in the historical data,

00:33:39.800 | will I be more successful going forward?

00:33:42.240 | The simple way to say,

00:33:43.760 | question that the persistence scorecard tries to answer is,

00:33:46.600 | do winners continue?

00:33:48.480 | Do losers continue?

00:33:49.600 | Is there persistence in skill?

00:33:51.820 | - Or does it ask, is there actually skill

00:33:55.560 | or is it randomness?

00:33:56.920 | - The way to think of it is this.

00:33:58.960 | When you identify a manager who has outperformed,

00:34:03.640 | how can you tell whether his outperformance

00:34:08.140 | is a result of genuine skill or simply of good luck?

00:34:13.060 | And the answer to that question is,

00:34:15.740 | genuine skill should persist.

00:34:19.260 | Good luck is ephemeral, comes and goes.

00:34:21.700 | You're lucky this year, not last year, or not next year.

00:34:24.500 | And so what the persistence scorecard does is to,

00:34:27.820 | at various time horizons and various breakpoints,

00:34:32.620 | look at funds which have outperformed historically

00:34:37.240 | and ask, did their outperformance continue?

00:34:40.580 | For example, there are many, many cuts

00:34:42.700 | in the persistence scorecard.

00:34:44.320 | One thing we do is to say,

00:34:45.980 | let's go back to 10 years of data,

00:34:48.100 | take all the managers who were above average

00:34:51.260 | in the first five years and say,

00:34:52.900 | how did they do in the second five years?

00:34:55.260 | And obviously, if you were in the top half of the universe

00:35:00.260 | five years ago, in the first five years,

00:35:05.060 | and skill persists, the likelihood is

00:35:07.520 | you should have a lot higher probability

00:35:09.560 | of being in the top half of the distribution

00:35:11.240 | in the second five years than the managers

00:35:14.240 | who were in the bottom of the distribution

00:35:16.480 | in the first five years.

00:35:17.880 | And what the persistence scorecard tells us is

00:35:21.040 | that there's relatively little persistence.

00:35:24.720 | In other words, the example I just posed

00:35:27.520 | the last time we ran persistence,

00:35:29.320 | if you take, again, all large cap managers,

00:35:31.760 | go back 10 years, take the first five years,

00:35:35.440 | and say, of the managers who were above average

00:35:40.000 | in the first five years, how many of them

00:35:43.320 | were above average in the second five years?

00:35:45.840 | The answer was 42%.

00:35:48.320 | - So less than half.

00:35:49.400 | So this is like a random event.

00:35:50.600 | I mean, if you think about it,

00:35:51.480 | it's like, well, it seems like half should be,

00:35:53.480 | at least half.

00:35:54.920 | - Yeah, no, exactly right.

00:35:56.520 | That's the default is half.

00:35:58.000 | In other words, if the results are completely random,

00:36:00.400 | half of the managers are gonna be in the top half.

00:36:02.920 | And it turns out that somewhat less than half

00:36:05.840 | of the top half managers from 10 years ago

00:36:10.240 | are still in the top half in the second five-year period.

00:36:13.760 | - Now, what about the bottom half?

00:36:15.480 | Did any of the managers in the bottom half

00:36:16.920 | end up in the top half?

00:36:17.880 | - Oh, sure, sure.

00:36:19.500 | - Well, I'm actually looking at the data right here,

00:36:21.440 | and it does look like the bottom half,

00:36:24.360 | a little less than 20% of the bottom half

00:36:26.700 | ended up in the top half.

00:36:28.340 | But about 15% of the bottom half

00:36:31.280 | ended up going out of business.

00:36:33.560 | Whereas the top half, only about 6% went out of business.

00:36:37.960 | So I guess what you could say about the top half

00:36:40.200 | is there's a lower probability

00:36:41.720 | they will merge or go out of business.

00:36:43.960 | So they have that momentum,

00:36:45.080 | probably because they have a lot of assets in the fund

00:36:47.160 | if they're in the top half.

00:36:48.500 | - Yeah, and there's some persistence, I think,

00:36:50.940 | not of performance, but of stickiness of funds.

00:36:55.940 | If you're in a fund, you may not want to get out

00:36:58.680 | for a variety of reasons.

00:37:00.200 | So if your historical success,

00:37:02.280 | if you look, let's say, at the large-cap funds

00:37:04.960 | do this five-year exercise,

00:37:07.160 | if you're in the top half in the first five-year period,

00:37:10.400 | you're less likely to go out of business or to liquidate.

00:37:15.120 | You're not particularly likely, necessarily,

00:37:17.560 | to repeat in the top half,

00:37:18.840 | but you're likely to live, to persist,

00:37:22.400 | in terms of still being around.

00:37:25.880 | Now, you also divided these down into quartiles.

00:37:28.100 | So you can look at the top quarter,

00:37:30.020 | and then the second quarter,

00:37:31.420 | and then the bottom three quarters,

00:37:33.660 | and then the bottom quarter.

00:37:35.100 | And it doesn't appear, overall,

00:37:39.540 | that it's much different than random,

00:37:41.900 | what happens to a fund.

00:37:43.580 | - That's a very fair summary.

00:37:45.340 | Again, coming back to the way the exercise works,

00:37:48.460 | if skill is randomly distributed,

00:37:50.460 | or results are randomly distributed,

00:37:53.540 | in any given period, 25% of the managers

00:37:56.100 | are gonna be in the top quartile.

00:37:58.740 | So if you look at large-cap U.S. managers, again,

00:38:02.940 | and say, look, in the first five years,

00:38:04.940 | what percentage of large-cap U.S. managers

00:38:07.780 | who were in the first quartile in the first five years

00:38:10.340 | repeated in the first quartile in the second five years?

00:38:12.900 | The answer, again, the most recent persistence scorecard

00:38:15.740 | was about 27%, not really much better than random.

00:38:20.460 | - Random.

00:38:21.300 | - Yeah, and if you go to look at mid-cap and small-cap,

00:38:23.340 | it's even worse.

00:38:24.820 | - A manager who has skill, like a bowling team,

00:38:28.100 | or a top tennis player, should continue.

00:38:31.140 | I mean, they should continue to win.

00:38:33.660 | - Exactly, exactly.

00:38:35.260 | The fact that the persistence scorecard says what it says,

00:38:40.260 | in other words, that there is no predictive value

00:38:43.740 | in historical performance,

00:38:46.120 | I mean, I think it says two things, Rick.

00:38:47.860 | One is, it reminds us that what active managers

00:38:52.420 | are trying to do is very difficult.

00:38:54.540 | It's so difficult that most of them

00:38:57.220 | don't do it particularly well.

00:38:59.500 | And secondly, it reminds us, as investors

00:39:03.820 | who are potentially identifying funds to buy,

00:39:07.780 | that historical performance is not a good gauge

00:39:12.340 | of what will happen in the future.

00:39:14.480 | - Some have said that fees are a good indication.

00:39:17.780 | In other words, if you have low fees,

00:39:19.300 | you have a higher probability of outperforming.

00:39:22.080 | Do you work any of that into your studies?

00:39:24.140 | - It, yes.

00:39:26.020 | There's a, I guess I'll give you an answer at two levels.

00:39:29.500 | One, we haven't done the study directly.

00:39:31.940 | I know Morningstar has, and the summary

00:39:34.460 | is exactly what you say.

00:39:36.420 | If you were, instead of picking a fund

00:39:40.900 | based on past performance, if you picked a fund

00:39:43.220 | based on, I want something in the lowest quartile of fees,

00:39:46.180 | that's a more sensible strategy than picking a fund

00:39:49.780 | that has outperformed by a lot recently.

00:39:53.400 | What we have done in SPIVA is,

00:39:56.200 | and we don't do it every six months,

00:39:58.600 | but we'll every year do what we call

00:40:01.960 | an institutional SPIVA.

00:40:03.840 | And what we do in institutional SPIVA,

00:40:05.920 | among other things, is to take all of the funds

00:40:08.320 | that were in SPIVA, classic SPIVA that we were talking about

00:40:11.640 | and add back their fee.

00:40:14.480 | - Ah, you do a gross, you do a gross.

00:40:16.320 | - We do a gross of fees.

00:40:18.200 | And not surprisingly, somewhat fewer managers

00:40:22.760 | underperform when you don't count their fees,

00:40:26.000 | but it's still a majority.

00:40:27.080 | I mean, I remember when the very first

00:40:29.020 | institutional SPIVA came out,

00:40:31.560 | I remember doing a meeting with a client

00:40:33.280 | and so I'll make it as simple as I can.

00:40:36.840 | SPIVA tells us, classic SPIVA tells us

00:40:39.800 | that most mutual fund managers, net of fees,

00:40:44.200 | underperform most of the time.

00:40:45.920 | Institutional SPIVA tells us

00:40:48.160 | that most institutional managers

00:40:49.980 | and most mutual fund managers, gross of fees,

00:40:53.680 | underperform most of the time.

00:40:55.400 | The conclusion doesn't change.

00:40:56.960 | I mean, the numbers change a little bit,

00:40:58.920 | but the conclusion doesn't change really at all.

00:41:02.360 | - That's interesting.

00:41:03.700 | I wonder how much of that is related

00:41:05.400 | to just the amount of cash

00:41:07.040 | that they have to have in a portfolio.

00:41:09.880 | And bull markets occurring at times

00:41:12.720 | when there's cash in a portfolio,

00:41:14.160 | which is hurting performance and, you know,

00:41:16.160 | kind of an asset allocation decision

00:41:17.800 | as opposed to a stock selection decision.

00:41:20.160 | - Part of it could be that,

00:41:21.960 | but we've done some work on this topic

00:41:24.680 | 'cause we hear this objection all the time.

00:41:26.120 | Well, you know, that index funds will outperform

00:41:29.440 | because they're fully invested in a rising market,

00:41:31.560 | but not in a falling market.

00:41:33.200 | And if you look back at the falling markets

00:41:35.400 | in our historical database,

00:41:37.480 | look, for example, at, you know, 2001, 2002,

00:41:42.640 | the majority of managers underperformed.

00:41:44.920 | 2008, the majority underperformed.

00:41:48.080 | So it's, the data don't really support.

00:41:51.640 | - Can't make that argument about cash then, huh?

00:41:54.320 | - Yeah, it's, I mean, I think there's a slight advantage.

00:41:57.680 | Sure, if the other thing to keep in mind, of course,

00:42:00.440 | is that, you know, we're at a point

00:42:02.800 | in the investment business now

00:42:04.720 | where sophisticated mutual fund managers,

00:42:08.440 | which I would think would include most of,

00:42:10.800 | most if not all of them, there are ways

00:42:13.240 | to equitize your cash.

00:42:14.600 | If you have, you have to have a lot of cash on hand,

00:42:16.600 | you know, because you might get redemptions.

00:42:18.200 | Well, you can buy index futures

00:42:21.360 | to so-called equitize the cash,

00:42:22.960 | give you the return of the equity market.

00:42:24.760 | So I think that that wasn't possible,

00:42:27.160 | you know, 50 years ago, certainly,

00:42:28.560 | but certainly it is today.

00:42:30.640 | And I think that that also, you know,

00:42:33.120 | rebuts that argument.

00:42:36.520 | - Past performance is not an indication of future results,

00:42:41.120 | is really what the persistence studies show.

00:42:43.560 | - That is a very good summary, yes.

00:42:46.520 | - A lot of it is random.

00:42:48.520 | Skill is very difficult to discern,

00:42:51.160 | very hard to go out and pick a manager

00:42:53.280 | that actually has skill.

00:42:54.960 | And I get, one of the problems

00:42:56.440 | with trying to pick a manager that has skill

00:42:58.000 | is everyone is looking for these managers.

00:43:00.720 | And if you actually identify somebody that has skill,

00:43:03.400 | the money is gonna just pile in,

00:43:06.120 | and that in itself could harm the performance of the fund.

00:43:11.120 | We see that quite frequently.

00:43:13.400 | - One thing I think we know for sure about fund flows

00:43:17.640 | is that the vast majority of fund flows

00:43:21.640 | come into funds that have recently done very well.

00:43:25.360 | So especially if you think back to what you just said, Rick,

00:43:29.640 | that past performance is not a good indicator

00:43:32.760 | of future performance to allocate money

00:43:35.640 | based on past performance.

00:43:37.200 | It's just saying you're almost asking to underperform.

00:43:40.520 | Of course, that's what happens.

00:43:42.160 | And it's understandable.

00:43:43.160 | People want to buy something that has done well,

00:43:46.680 | except that the fact that it did well last year

00:43:50.160 | does not tell you much about how it's gonna do this year.

00:43:52.920 | - So let's talk about a report that I did that you've read.

00:43:57.360 | - Yes.

00:43:58.200 | - So as a advisor for 35 years,

00:44:02.240 | if I just took client money

00:44:06.040 | and allocated it between stocks and fixed income,

00:44:10.320 | and then within the stock side,

00:44:12.720 | the U.S. stocks, international stocks,

00:44:15.000 | and on the fixed income side,

00:44:17.640 | treasury bonds, corporate bonds,

00:44:19.640 | or total bond market fund,

00:44:21.480 | and all I did was buy the cheapest index fund I could get

00:44:29.080 | on the U.S. stock side, total stock market index fund,

00:44:32.800 | or even an S&P 500 fund.

00:44:34.680 | On the international side, a total international fund.

00:44:37.840 | On the bond side, a total bond market,

00:44:40.160 | or if I wanted to have a municipal bond fund,

00:44:43.120 | a municipal bond index fund, or something similar to it,

00:44:46.320 | because Vanguard actually has actively managed

00:44:48.440 | municipal bond funds that are basically index funds

00:44:51.120 | because there's so many bonds in there.

00:44:52.280 | But if that's all I did, what the study that I did,

00:44:56.520 | which is called the Case for Index Fund Portfolios,

00:45:01.120 | which actually came out 10 years ago

00:45:03.520 | with Alex Bankey as the co-author,

00:45:06.160 | if all you did was buy a portfolio of index funds

00:45:09.040 | and nothing else, forget about trying to pick active managers,

00:45:12.880 | forget about trying to pick managers

00:45:16.160 | who are gonna outperform the international market

00:45:18.400 | or the small cap market or whatever.

00:45:20.120 | Just forget it, just buy all index funds,

00:45:22.360 | cheapest you can, and maintain your asset allocation.

00:45:25.960 | The probability of the portfolio

00:45:30.160 | outperforming a portfolio that has either all active funds

00:45:34.240 | or some active funds in it is well over 90%.

00:45:38.320 | And it's even higher in a portfolio sense

00:45:43.120 | than it is in each one of these silos,

00:45:46.200 | like large cap, mid cap, small cap.

00:45:48.080 | When you put it all together in a portfolio,

00:45:49.520 | the probability of the portfolio

00:45:52.360 | outperforming a portfolio with active funds in it

00:45:56.280 | is actually higher than the individual silos

00:45:59.680 | because there might be a couple of active funds

00:46:02.120 | that you own that outperform,

00:46:03.360 | but the underperforming funds drag everything down.

00:46:06.240 | So I've been pounding the table on this,

00:46:09.320 | and of course, Jack Bogle did for years,

00:46:11.360 | and the Bogleheads pound the table on this.

00:46:14.200 | Just put together a few good index funds in a portfolio

00:46:18.120 | and hold it for the longterm and you'll be far better off.

00:46:21.360 | Do you agree with that?

00:46:23.560 | - Absolutely, absolutely.

00:46:25.240 | I mean, I think the mistake people make,

00:46:28.640 | and what you addressed in your study, Rick,

00:46:30.960 | but the mistake people make is to say

00:46:34.120 | that diversification will help me.

00:46:37.560 | So I'll pick an active US fund, for example,

00:46:40.720 | or maybe two active US funds,

00:46:42.880 | and then I'll diversify by picking an active

00:46:45.400 | international fund, an active bond fund, and so forth.

00:46:49.600 | And the difficulty is that that works

00:46:53.920 | if the expected return or the expected benefit

00:46:57.560 | of buying those active funds is positive.

00:47:01.040 | But if the expected benefit

00:47:02.800 | of buying the active funds is negative,

00:47:05.040 | which Spiva and other research demonstrate

00:47:07.920 | very clearly that it is,

00:47:10.200 | you're basically compounding the mistake.

00:47:13.760 | I mean, another way to say that is,

00:47:16.680 | let's suppose I go into a casino,

00:47:18.240 | I go up to the roulette wheel and I put $10 on red,

00:47:22.920 | then I spend it, lose my money.

00:47:24.960 | And the next roll, I go, I'm gonna put $10 on black now

00:47:28.600 | 'cause I'm gonna diversify.

00:47:29.960 | I mean, you're not diversifying anything except randomness.

00:47:34.280 | What you identified in that paper,

00:47:36.320 | which is really important,

00:47:37.840 | is to my mind is conceptually similar argument

00:47:42.640 | to the reason why Spiva results are so much worse

00:47:47.840 | over a 20-year horizon than over a one-year horizon.

00:47:51.760 | And that is that the probability of success

00:47:54.520 | is less than 50%.

00:47:56.920 | I am a lousy basketball player.

00:47:58.880 | So let's suppose that I was to get

00:48:01.680 | into a free throw shooting contest with Michael Jordan.

00:48:05.720 | It's possible he might miss his first shot.

00:48:09.400 | It's possible I might make my first shot.

00:48:13.000 | So as the unskilled player,

00:48:15.800 | I don't want many, if we have to shoot 100 free throws,

00:48:19.360 | he's gonna beat me easily, easily.

00:48:22.320 | Even 10, he's gonna beat me easily,

00:48:24.120 | but I might get lucky the first time or the second time.

00:48:26.960 | So if you're a low-skill player, you don't want many trials.

00:48:31.960 | You want relatively few.

00:48:35.120 | - You wanna rely on luck.

00:48:36.280 | - You rely on luck.

00:48:37.440 | If you're a high-skill player, you want lots of trials.

00:48:41.760 | And so in Spiva's case, lots of trials means,

00:48:45.600 | let's look not at one year, but at 20 years.

00:48:48.240 | In the case of your paper, lots of trials means

00:48:50.880 | let's look at multiple asset classes,

00:48:52.840 | not just one asset class.

00:48:54.600 | But they both point to the same conclusion,

00:48:57.200 | which is that the probability of success

00:49:00.680 | in picking an active manager is less than even.

00:49:04.480 | That's why the results get worse over time.

00:49:06.560 | That's why the results get worse

00:49:07.840 | when you use more asset classes.

00:49:09.600 | - I think we made the conclusion in the paper

00:49:11.440 | that if you were going to go with active management,

00:49:14.440 | you put all your money on red and spin the wheel,

00:49:17.240 | like you were saying.

00:49:18.080 | The more active managers you put in your portfolio,

00:49:21.600 | the lower the probability is that that portfolio

00:49:24.680 | will outperform a portfolio of index.

00:49:26.400 | It's already low to begin with.

00:49:28.120 | But as you add more active funds,

00:49:30.160 | the probability actually decreases.

00:49:31.960 | And I'll just tell people that you can find this paper

00:49:34.360 | at my website at rickferry.com.

00:49:37.240 | It's a case for index fund portfolios.

00:49:40.920 | Again, the paper's 10 years old now.

00:49:42.240 | It was published in 2012.

00:49:43.640 | But the results are the same.

00:49:44.920 | In fact, if we were to redo the study,

00:49:46.760 | or someone was to redo the study right now,

00:49:48.960 | going back 10 years,

00:49:49.920 | I think you'd find that the case

00:49:51.640 | for index fund portfolios was even higher.

00:49:54.240 | - Absolutely.

00:49:55.080 | - Than it was that we found in our paper.

00:49:57.040 | Now, let me ask you about taxes.

00:50:00.880 | Now, I know that you don't include taxes

00:50:03.400 | in your SPIVA report,

00:50:05.840 | but you probably have done some work on taxes,

00:50:08.240 | because a lot of individual investors

00:50:11.080 | have taxable accounts,

00:50:12.600 | and they have to pay taxes

00:50:14.960 | on capital gain distributions from mutual funds.

00:50:17.200 | So has S&P Dow Jones done any work on after-tax returns?

00:50:22.200 | - We have not done it ourselves.

00:50:26.640 | I've certainly read some of the literature,

00:50:29.520 | and your question is the right one.

00:50:32.280 | The thing to remember about index vehicles

00:50:35.520 | is that they typically are much more tax-friendly

00:50:38.720 | than actively managed vehicles,

00:50:42.480 | especially if you access them via an ETF,

00:50:47.120 | which has considerable tax advantages

00:50:49.440 | relative to a traditional mutual fund.

00:50:52.360 | And since it's almost any index fund

00:50:55.560 | that you want to have access to

00:50:58.480 | can be got via an exchange-traded fund.

00:51:01.760 | I'm not a tax authority or a tax lawyer,

00:51:04.440 | but it's what I do personally,

00:51:06.480 | and I can certainly recommend it

00:51:09.840 | as something for clients to think about.

00:51:11.520 | - Yeah, it's interesting you think that.

00:51:12.720 | We're off going on a little different topic here,

00:51:14.720 | but in my own personal portfolio,

00:51:16.640 | I only have exchange-traded funds.

00:51:18.680 | Now, Vanguard's a little different

00:51:20.400 | because the ETF and the mutual fund,

00:51:22.400 | they're all the same.

00:51:23.720 | It's all the same.

00:51:24.560 | They're all treated the same for taxes,

00:51:25.640 | but if you're gonna buy an iShare or a Spyder

00:51:29.040 | or a Schwab fund,

00:51:30.640 | you're better off in a taxable account with the ETF

00:51:35.080 | because it doesn't spin off capital gains

00:51:37.280 | at the end of the year.

00:51:38.680 | You only have to pay capital gains

00:51:40.080 | when you actually sell your shares.

00:51:41.960 | You do have to pay taxes on the dividends,

00:51:44.240 | but not the capital gain distributions

00:51:47.240 | that you would see a lot in actively-managed funds.

00:51:49.960 | So again, actively-managed funds in a taxable account

00:51:54.960 | creates another cost

00:51:57.080 | because of the capital gain distributions

00:51:59.040 | at the end of the year,

00:52:00.040 | and so all index funds all the time,

00:52:02.720 | whether it's your retirement account,

00:52:04.320 | whether it's taxable account,

00:52:06.560 | if you're gonna use it in your taxable account,

00:52:08.120 | using ETF certainly makes sense.

00:52:11.000 | Any parting words for our Bogleheads listeners?

00:52:14.120 | I think the thing to keep in mind,

00:52:16.680 | and we've written about this a number of times at S&P,

00:52:20.720 | is two things.

00:52:22.080 | One is the conclusion we've been talking about

00:52:24.680 | for the past hour or so,

00:52:26.080 | which is the majority of active managers

00:52:29.080 | underperform most of the time.

00:52:31.320 | The second thing to keep in mind

00:52:32.440 | is this is not a coincidence.

00:52:34.720 | It's not random that this happened.

00:52:36.600 | There are good reasons why this happens.

00:52:39.800 | We've talked a little bit about cost.

00:52:41.840 | I mean, index funds are cheaper

00:52:44.240 | than actively-managed funds.

00:52:46.440 | The Investment Company Institute estimates every year

00:52:49.680 | the weighted average cost of U.S. actively-managed funds

00:52:54.000 | versus index funds is about a 60-basis point difference

00:52:56.760 | as of the most recent estimate.

00:52:59.280 | That means, on average, an active manager

00:53:01.840 | starts 60 basis points in the hole.

00:53:03.840 | It's a lot to make up.

00:53:05.320 | The second reason, again, we mentioned this earlier,

00:53:07.680 | is the notion that in most of the world,

00:53:10.840 | certainly in the United States,

00:53:12.480 | the investment management business

00:53:14.240 | is very largely professionalized.

00:53:16.640 | If you hire an active manager, let's say from Fidelity,

00:53:20.920 | and he's making a trade against an active manager,

00:53:23.640 | let's say from J.P. Morgan,

00:53:26.520 | the guy from Fidelity and the guy from Morgan

00:53:28.800 | have access to the same information.

00:53:30.800 | They read the same research.

00:53:32.040 | They have the same Bloombergs on their desk.

00:53:33.800 | They probably went to the same MBA program.

00:53:35.840 | They have the same CFA certificate.

00:53:37.720 | There is a level playing field.

00:53:40.680 | There's no reason to assume that one of these guys

00:53:43.600 | has an advantage over the other.

00:53:45.800 | And that phenomenon of the professionalization

00:53:49.520 | was identified in kind of a famous article.

00:53:52.920 | It was famous in the index world by Charles Ellis in 1975

00:53:56.680 | called "The Loser's Game."

00:53:58.240 | What Charlie did in that article,

00:53:59.640 | he surveyed the then post-war history

00:54:03.120 | of U.S. financial markets,

00:54:04.320 | so that was 30 years in 1975,

00:54:06.520 | and this is exactly what he said.

00:54:08.640 | In the '50s, when the investment business

00:54:11.840 | was largely dominated by retail investors

00:54:15.000 | and relatively few professionals,

00:54:16.800 | it was possible for the majority of professionals

00:54:19.560 | to outperform because they had advantages

00:54:21.880 | that the retail investor didn't have.

00:54:23.920 | As the business became increasingly professionalized

00:54:27.040 | in the '60s and '70s, that advantage went away

00:54:30.160 | because all of the managers got better,

00:54:32.440 | the professionalization took over,

00:54:34.680 | and we got to the point, even in 1975, let alone today,

00:54:38.680 | when it was impossible for any particular active manager

00:54:43.680 | consistently to have an advantage over the others.

00:54:47.320 | That's why, by the way, index funds started in the 1970s,

00:54:51.280 | because professionalization was well along by then.

00:54:54.640 | Some of the other arguments were just as good

00:54:56.200 | 20 years later, but 20 years sooner,

00:54:58.440 | but that's the thing that happened.

00:55:00.840 | So the fact that the majority of managers underperform

00:55:05.280 | most of the time is not random,

00:55:08.800 | it's not just a quirk of fate.

00:55:11.520 | It happens for very good reasons.

00:55:13.600 | Those reasons still exist, which means that the phenomenon

00:55:17.000 | is likely to continue to exist going forward.

00:55:20.440 | So even though past performance

00:55:21.800 | is not a predictor of future returns,

00:55:23.680 | I feel safe saying that if you're in index funds,

00:55:26.760 | the future performance of index funds

00:55:28.800 | is going to be higher than actively managed funds.

00:55:31.200 | In this case, past performance does predict future returns.

00:55:34.000 | - I think that's a very fair statement, yes.

00:55:36.480 | - Thank you, Craig, so much for being

00:55:37.680 | on "Bogleheads on Investing."

00:55:38.960 | I appreciate it. - Thank you, Rick.

00:55:40.640 | - This concludes this episode of "Bogleheads on Investing."

00:55:44.720 | Join us each month as we interview a new guest

00:55:47.480 | on a new topic.

00:55:48.520 | In the meantime, visit boglecenter.net, bogleheads.org,

00:55:52.800 | the Bogleheads Wiki, Bogleheads Twitter.

00:55:55.240 | Listen live each week to "Bogleheads Live"

00:55:57.520 | on Twitter Spaces.

00:55:58.720 | The Bogleheads YouTube channel.

00:56:00.240 | Bogleheads Facebook.

00:56:01.560 | Bogleheads Reddit.

00:56:03.200 | Join one of your local Bogleheads chapters

00:56:06.080 | and get others to join.

00:56:07.440 | Thanks for listening.

00:56:08.440 | (upbeat music)

00:56:11.040 | (upbeat music fades)

00:56:14.120 | (upbeat music)

00:56:16.700 | you

Bogleheads® on Investing Podcast 050: Craig Lazzara on active versus passive funds, host Rick Ferri

Chapters