November 12, 2024 • 38 mins
Profitable companies tend to perform well among small caps, as evidenced by the Bloomberg MVP small-cap portfolio, which returned 11.9% annualized in a backtest run from 1999 to September 2024. On this episode of Inside Active, host David Cohne, mutual fund and active management analyst with Bloomberg Intelligence, along with co-host Michael Casper, sector and small-cap strategist, spoke with Mitch Zacks, CEO and principal portfolio manager of Zacks Investment Management about the significance of upwards earnings-estimate revisions and why they could be crucial for predicting stock performance. They also discussed the evolving landscape of small-cap stocks and why valuation metrics are more relevant for larger companies.
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Speaker 1 (00:13):
Welcome to Inside Active, a podcast about active managers that
goes beyond sound bites and headlines and looks deeper into
their processes, challenges and philosophies and securitance selection. I'm David Cohne.
I'd be Mutual fund and Active Research at Bloomberg Intelligence Today.
My co host is Mike Casper, sector and small cap
strategist at Bloomberg Intelligence. Mike, thank you for joining me today.
Speaker 2 (00:35):
Thank you, David.
Speaker 1 (00:36):
Well, you and Chris Kane put out a note last
week talking about applying bi's proprietary b MVP multi factor
methodology for the small cap Bloomberg two thousand. Can you
give a brief overview of the methodology and how it's performed.
Speaker 3 (00:51):
Sure, So what we do is we start with the
Bloomberg two thousand index. That's kind of roughly equivalent to
the Russell two thousand. It's just mark a cap weighted though,
so there are some slight differences on the edges. But
what we do is we take that universe and we
apply a profitability screen to it. It's awfully difficult to
kind of make conclusions on fundamentals without having such a screen,
(01:12):
given that about a third of stocks in the Russell
two thousand or in small caps in general aren't profitable
at the moment, So we take that profitability screen and
that whittles down the universe a little bit further. I
think as of September we were at about eleven hundred
constituents left over from the two thousand. When we do that,
and then we apply our momentum scores on a sector
(01:32):
neutralized basis. All these factors are on a sector neutralized basis,
but momentum the average of trailing six and twelve month
total returns excluding the last two weeks. We apply our
low volatility factor, our value factor, which four small caps,
we just look at the trailing twelfth month sales to
price ratio, and our profitability factor, which is the average
of trailing twelve month ROE and ROIC, and then the
(01:56):
top descal of those percentile ranks once we average those out,
is the final b MVP small cap portfolio. Now a
little bit of stats before I go into the back
test result. That results in a little bit higher market
cap weighting than the B two thousand. For example, so
the Bloomberg MVP, the media market caps about two point
(02:17):
eight billion. In that universe with the largest stock about
eight and a half billion, small stock about two hundred
and eighty seven million, So the market cap range of
the B two thousand as a whole as much wider
the same thing with the Russell two thousand. The media
market caps about a billion. But onto results, the cumulat
return for this thing is pretty solid. So back to
(02:39):
nineteen ninety nine through September twenty twenty four, you're looking
at percent significantly lower volatility than the Russell two.
Speaker 2 (02:46):
Thousand equal WEIGHTD.
Speaker 3 (02:47):
But what I really like to compa against is the
only profitable bin in the B two thousand. That annualized
return is about eleven point eight five percent, so we
are generating some alpha over that bin using.
Speaker 1 (02:58):
This nice I think this is a great time to
introduce our guest, Mitch Zach's. He's CEO and principal portfolio
manager of Zach's Investment Management. Mitch, thank you for joining
us today.
Speaker 4 (03:11):
David and Michael, it's a pleasure to be here.
Speaker 1 (03:13):
I'd like to start off, you know, just hearing more
about your career background.
Speaker 4 (03:17):
Sure, I you know, went to school at Yale and
I joined Lazard Freyer and Mergers and Acquisitions as an
analyst I left the analyst program to join Zach's Investment Research.
At the time, Zach's and we founded a division of
Zach's Investment Research, which was a money management subsidiary, a
(03:38):
wholly owned money management subsidiary. At the time I joined,
we had roughly fifty million dollars in assets under management,
and over the past twenty some odd years, you know,
twenty odd plus years actually at this point in time,
we've been able to grow the firm from around fifty
million dollars in asset to a little bit over nineteen
(04:01):
billion dollars in assets under management and model delivery and advisement.
So you know, that's kind of my background. I've been
working with quantitative equity portfolio strategies my entire career, and
I've designed most of the asset management strategies that we
implement at Zach's. Zach's Investment Management's a wholly owned subsidiary
(04:25):
of Zach's Investment Research. Zach's Investment Research is the second
largest provider of independent investment research in the country, and
our contribution to finance was that we created the concept
of the quarterly consensus earnings estimate before Za's came around.
What people were doing was pretty much looking at how
(04:45):
earnings evolved on a year over year basis, looking at
how that earnings growth compared to sort of a trend
line analysis based on historical earnings growth, and saying, well,
earnings have been growing at you know, ten percent, in
at twelve percent year over year earnings growth, it was
a good quarter. What we started doing is we created
this concept of the quarterly consensus earnings estimate, where we
(05:09):
took to the cell side earnings estimates by all the
analysts following these companies, and we created a consensus and
then when a company would report earnings, it would be
compared to what Wall Street expectations were. If the earnings
were greater than Wall Street expectations, it would be deemed
a earning surprise, and if they were less than Wall
(05:32):
Street expectations, it would be a negative earning surprise. And
we started to look at this data we created and
started to say, well, how can we use this data
to effectively manage money? And what we found to be
the most important use of the data is not to
look at the year over year earnings growth and instead
(05:54):
to focus on how the earnings estimates are changing over
time and what we found to be the case is
that there's some degree, it's a reasonably strong degree given finance,
you know, time series metrics of earning of serial correlation
with earnings estimate revisions, and that became the basis of
(06:15):
some of our proprietary models that we then started the
asset management firm with.
Speaker 1 (06:21):
So you mentioned, you know, earnings revision. Can you, I
guess speak to you know how that affects your investment philosophy?
And you know, I'm thinking, actually, you know your investment
process specifically for the ZACH small cap or fund.
Speaker 4 (06:33):
Sure, what we're What we found to be the case
is that companies that have received upward earnings estimate revisions
in the past are more likely to receive upward earnings
estimate revisions in the future. And if you own a
group of companies that have received upward earnings ESTEMA revisions
(06:57):
in the past, you're roughly about sixty percent more likely
to receive upward earning cestemer revisions in the immediate future.
So the first thing I started to do is to
try and say, well, what if we create a portfolio
where we simply own all the companies that are receiving
upward earning customer revisions, we hold them for a certain
period of time, and then we rebalance the portfolio. And
(07:21):
what happens is if you kind of apply that sort
of very very structured approach, you tend to have a
very high degree of turnover in the portfolio. Roughly sixty
seventy percent of the companies that are receiving upward earning
cestemer revisions at the beginning of the month are not
(07:41):
receiving upward earning customary visions over the end of the month,
So there tends to be a high degree of turnover.
You also tend to get a very very high degree
of sector concentration, so you not only see a high
degree of turnover, you see a level of sector concentration
that increases sort of the volatility and tracking error of
(08:04):
the portfolio relative to the benchmark. So what we started
to do is saying, well, how can we control the
turnover and how can we control the sector exposure to
generate a return that's more in keeping with the benchmark.
And that's what I've been doing for many years, and
(08:25):
in the ZAC small cap core process as a mutual
fund CIX, which is a very small mutual fund which
actually kind of helps us in terms of being able
to move in and out of these positions. We're trying
to control the turnover so that the turnover is around
one hundred percent per year, and we're trying to control
(08:46):
the tracking error as much as we can through optimization
to try and keep the risk in line with our
benchmark of the Russell two thousand.
Speaker 3 (08:56):
Basically, now I give my whole feel at the beginning
of this about how profitable companies tend to outperform the
egaloid to benchmark. It sounds like we're kind of arriving
at similar things you with earning s revision mean with
profitable stocks. When you look at earning servision, do you
only select profitable companies or do you care if it's
going from unprofitable to less unprofitable.
Speaker 4 (09:19):
What we're looking for are changes in the earnings estimate
revision so that they're positive. So we're looking for sort
of four factors, agreement to the extent to which multiple
analysts are revising their earning estaments in the same direction, magnitude,
the size of those estimate revisions upside, where the most
(09:39):
accurate or recent earnings estaments are coming in relative to
the consensus, and the earning surprise, the frequency and magnitude
of earning surprises that have occurred in the process that
we're implementing. We're using the Russell two thousand universe as
the initial universe of selection, and we're adding to at
(10:00):
any company that's in our current portfolio that may have
been removed from the index at some point in time.
So the union of those two sets is really our basis.
What I will say is that companies that are getting
negative earnings are a little bit less likely to get
positive earnings estimate revisions because they're sometimes in some sort
(10:22):
of growth phase where the management is spending money for
some sort of potential you know, jackpot in the future,
so to speak. So they might be a biotech company
where the management has decided, hey, we're going to continue
to spend money to engage in the drug development and
(10:43):
our hope is that it gets approved at the end
of the entire process. So generally, those companies that are
kind of in the development phase that are based on
massive capital expenditures to sort of capture a market will
likely not get upward earning cestmer visions, And in some
of those instances, it's possible they get downward esimate revisions,
but they're getting closer to their sort of goals in
(11:06):
terms of growth that the stock price will respond positively.
So they are two separate ideas. One idea is, hey,
can you look at companies that have positive earnings to
do companies with positive earnings or positive expected earnings outperformed
companies with negative earnings. And that has historically been the case,
(11:26):
but that's been widely disseminated amongst managers. So I'm not
positive that's going to be completely the case over time.
I think it might be a little bit better instead
of sort of throwing out you know, I guess you know,
roughly thirty percent of the Russell two thousand to instead say, okay,
are these companies? These companies are expected to lose money,
(11:48):
but they expected to lose less money this quarter? Are
they expected to lose less money over the last month
because analysts are revising their earnings estimates upward. And what
I've actually anecdotally found to be the case is companies
that move from a nonprofitable sort of scenario where they're
(12:08):
losing money to companies that are going to generate positive
earnings tend to maybe not all the time, but have
a potential for very very strong returns, and you don't
want to necessarily remove those from the portfolio.
Speaker 1 (12:23):
That makes sense. You know, I'm familiar with Zach's investment research,
and you know kind of you know Zach's rank and
you know strong buy stocks. Does it kind of follow
the portfolio kind of follow something similar where it only
includes like strong buy or buy stocks according to those formulas.
Speaker 4 (12:42):
Yeah, in zsci X, which has been performing relatively well
over the last you know, a couple of years. Here,
what we're doing is we're combining an estimate revision model
with a model that focuses on quality. We're using that
sort of alpha model as an input to an optimization
routine to generate a buy, a hold, and sell list
(13:03):
that we then evaluate and pretty much implement. But there
is a basis in the alpha model looking at companies
that are likely to receive upward earnings estimate revisions in
the future. But you have to remember that we again,
the pure estimate revision universe has a high degree of turnover,
(13:25):
and what we're trying to do is use earning sest
revisions as an alpha source and then control the turnover
and control the sector exposure. So the overall risk of
the portfolio is in line with the Russell two thousand benchmark.
So to answer the question, what we're doing is we're
using earnings estimate revisions along with another model in a
(13:45):
multi factor model, but we're using that as an input
to optimization that then can identify companies. So it's a
subset of that group that might be potential by candidates,
and we might even go outside of that group if
the optimization indicates that adding that position could conceivably reduce
(14:06):
tracking error.
Speaker 1 (14:08):
Okay, so you know, if you're starting at this universe,
you know, you know, I know the four factors of
zach Ring. So that is, you know, something that's just
kind of at the very beginning, is what you're looking at.
Speaker 3 (14:20):
Now, are there any sectors that you find particularly compelling
at the moment or that are being flagged for overweights
right now?
Speaker 4 (14:28):
What I've found to be the case over time is
that sector risk control is less effective than looking at
projected tracking error relative to a benchmark. So what happens
is if you focus on sort of sector exposure, what
(14:48):
tends to happen is there'll be companies that are classified correctly.
They are companies that are misclassified based upon their sector.
But there are companies that will be classified correctly based
on their sector, but they will move like a company
that is outside of that sector. So, for instance, right now,
there are companies that are construction companies that are helping
(15:12):
construct sort of cloud computing centers, helping with construct infrastructure
on semiconductor equipment, manufacturing plants, and things of that sort,
and these companies will be correctly classified as construction companies.
They build things, they issue debt, they use the debt
to buy to buy land to build things, and they
(15:34):
sell the they sell their what their access or they
sell their effective use to a company that needs to
use that type of building or that type of source.
But in terms of their contribution to risk, they behave
much more like semiconductor companies than construction companies. So I
tend not to try and think from a top down perspective.
(15:57):
I tend to want to think from a bottom up perspective.
And what we're looking for when we do do some
qualitative oversight in some of our other strategies is we're
looking for lack of crowding in the market. So we
don't want to look for a individual company where there's
a tremendous amount of information or focus from either the
(16:22):
media or from investors on what the outcome of or
how that company is evolving over time. But in terms
of like if you had to pin me down in
terms of sectors, I think industrials are looking relatively interesting
at this point in time.
Speaker 1 (16:39):
Kind of want to move to a slightly different topic, actually,
market caps something that I've kind of been looking at recently,
where if you think of the traditional dollar value of
what one considers a small cap, you know, up to
about two billion. I've been noticing a lot of mutual
funds holding much larger stocks. I know, for this particular one,
(17:00):
I think about half the portfolios in midcaps, you know,
which is, you know, much less than what I've seen.
Some small cap funds are even holding large caps. So
I'm just asking you know, for you is it Do
you think this is due to small companies growing into
midcaps or just they're being less available small cap companies.
Speaker 4 (17:18):
I think that if you look at the companies right
now that are getting upward earning cestemer visions, they tend
to be more mid cap companies. If you look at
various quality metrics that we're combining with the estimate revisions,
it also tends to bias more towards MidCap companies. And
going back to I think Michael said, if you look
at those companies that are having a negative earnings, I
(17:42):
believe that they also correlate with small with much smaller
cap companies. So we're agnostic in terms of capitalization as
long as it is within the Russell two thousand universe
or within our portfolio. But we are looking for companies
are receiving upward earning cestemer revisions. We are looking for
companies that have a reasonable score on our quality metric,
(18:06):
and we are looking for companies that, when added to
the portfolio, will conceivably provide us with the alpha score
while reducing the tracking error relative to the benchmark. So
you know, part of this is that you know, midcaps
have been performing a little bit better than small caps.
As you go down capitalization ranges, the number of companies
(18:29):
that are not quote high quality or good companies tends
to increase. So as you go down to sub five
hundred million dollar market cap, as you go down to
sub two hundred million dollar market cap, the number of
companies that really shouldn't be public tends to increase. The
number of companies that are kind of basically completely ignored
(18:49):
by the market for usually a good reason also tend
to increase. So if you start using quantitative factors that
statistically have just gener returns, profitability, momentum, earnings, estimate revisions quality,
that will always try and sort of push you away
(19:11):
from smaller cap companies. You also have the case that
with estimate revision based strategies there tends to be a
correlation between higher market cap and the amount of analyst coverage.
So the larger the market cap, not all the time,
it really depends on the sector and how much in
vogue that company is, but generally the larger the market cap,
(19:33):
the more analyst coverage you have. So again I don't
think it's a function of a qualitative decision. It's more
a function of the result of the quantitative models we're
using to try and generate returns. And my guess is
in the process that was described at the top of
the hour, there would also be a somewhat of a
MidCap bias if you're using low volatility and price momentum
(19:57):
and valuation metric to take a look at things based.
Speaker 1 (20:01):
Okay, do you incorporate valuation metrics at all when you're
looking at, you know, quality companies, and.
Speaker 4 (20:08):
In this process we're somewhat valuation agnostic, so we're really
focused on, well, if the company's receiving upward earning estament revisions,
does adding that company to the portfolio potentially reduce the
tracking error in other strategies that we runs. As I mentioned,
we're currently managing a little bit over nineteen billion dollars.
(20:29):
We do look at valuation as a primary factor that
we're looking at. My experience has been that as you
move lower in capitalization, the returns from sort of the
smaller cap universe tend to accrue a little bit more
towards technology and more towards some biotech companies because they're
(20:54):
able to effectively compound over time, go from becoming a
small cap company to a mid cap and maybe even
potentially a small large cap company, and that compounded return
compensates you for the losses that you have with all
these other companies. So that if you look at valuation
(21:16):
amongst very very small cap companies, you can run into
a somewhat of evaluation trap where you find companies that
are attractively valued and there might be an initial push
upward as the price book ratio you know, reverts to
historical means amongst historical levels amongst sort of bank stocks.
(21:38):
But these companies don't have the ability to compound on
an annualized basis at a high enough rate to compensate
you for the smaller cap names that might not perform
well or might be value traps. So what I found
to be the case is that valuation metrics seem to
(21:58):
make more sense and the larger cap that you moved towards,
and as you move towards smaller cap names, you know,
you have the traditional Fama French research which shows this
anomaloust return amongst small cap value names, and I'm not
quite sure if that has been picked over by the
market with the amount of capital that's that's looking for
(22:22):
these opportunities. So if you if you think about value stocks,
and you're saying, well, do small cap value stocks necessarily
always out reform small cap growth stocks? You're looking at
this regression analysis that's saying, well, yes, small cap value
gives you a bias, But it's possible. We've seen sort
of a such a flood of capital into the small
(22:43):
cap space that that thirty five dollars small cap company,
or that twenty dollars small cap company is now worth
twenty two dollars, or is now worth thirty six or
thirty seven dollars, and that would eliminate the annualized return
from owning those small cap value companies, And anecdotally, that's
what I happening. So I think that as the market
becomes more efficient, valuation as a metric sometimes loses some
(23:10):
of its historical outperformance. And I'm much more comfortable owning
companies that are receiving upward earning vest revisions and controlling
the risk through tracking you a relative to the benchmark,
looking at the PE multiple, looking at cash will multiples,
just eyeing them to make sure they're in line with
the benchmark in aggregate, than saying let's try and select
(23:32):
those companies in the Russell two thousand that have the
lowest p multiple relative to the industry group that they're
trading in.
Speaker 2 (23:40):
Basically, So, we.
Speaker 3 (23:44):
Talked a little bit about profitability, and you mentioned that
it's pretty well disseminated across the market that profitability in
small caps works, right, And when we were breaking down
our MVP model across the four factors. We notice that
pretty much any naive factor that you want to look
at for the long end of a factor was working
very well over the long term. Do you think that's
(24:04):
kind of more of a function of factor crowding given
your commentary on profitability and everybody kind of be in there,
or do you think it's more, you know, small caps
are inefficient or something else.
Speaker 4 (24:16):
What I think is happening is that the nature of
the small cap market is changing over time. So if
you take a step back and you say, well, what
type of company would be going public in the small
cap space, it would be a company looking for capital
(24:39):
where the capital is not available through bank financing, so
they don't have any physical, tangible assets, they can't engage
in any bank financing. They have an idea, they're going
to create an online service, they're going to create a
new generative AI, whatever it is, and they need a
massive amount of capital to grow. Historically, in the seventies,
(25:04):
in the eighties, in the nineties, even in the early
two thousands, these companies would go public at reasonably small capitalizations.
They would use the capital and grow. Now what is
happening is that the companies that are capable of growing
with capital expenditure and growing very largely with capital expenditure,
(25:26):
are staying private. And the companies that are non profitable
are not growth companies that have gone public. They're remnant
public companies that really shouldn't be public and are losing
money for some purpose, for some reason, because they're under
competitive pressure. So what I think is happening is there's
adverse selection within the smaller cap names, within the Russell
(25:50):
two thousand that the companies that are going public are
going public because they can't remain private and they can't
attract financing to grow. So what's happening is that as
those companies that show up as nonprofitable in the smaller
cap name, they're more likely to be less likely to
(26:11):
be able to compound on a very very high basis.
So it's you have this sort of two tiered market system.
You have all these remnant companies that are public. They
shouldn't be public, they are not generating any money, they
have a low return on equity, they should not continue
to trade. But there's there and there's no methodology to
(26:33):
remove it. Meanwhile, the good private companies are staying private
and coming public at much much higher capitalization levels. So
a lot of the research showing that hey, small caps
dramatically outperform over time may be skewed until we kind
of receive these companies that are going public and can
(26:54):
grow in the small cap space. But in terms of
you know, what happens is it's it's very interesting in
the mark is that as an idea becomes readily disseminated
and acted upon it, the market changes and it ceases
to reflect that idea. The biggest example I can come
up with this sort of the accrural phenomenon, where there
was this indication that there's an anomaly and maybe the
(27:17):
anomaly was due to data mining, but everyone was talking
about accurals. There was all this research done on purls
and then if you go back and you look, you know,
five ten years after the crural phenomenon was publicized, companies
that with attractive accurals are no longer outperforming. And I
am I would be a little bit wary of going
(27:40):
into the small cap space. And because this phenomenon where
unprofitable companies have dramatically underperformed for such a period of time,
and it's just accepted wisdom that if the company is
not profitable, it has to underperform the profitable company that
the market may have adjusted valuations in such a way
that that no longer holds to be the case going forward.
(28:01):
And that's why I think it's important to focus on
the earnings estimate revisions as opposed to necessarily the profitability
or the valuations.
Speaker 3 (28:11):
Basically, Yeah, and what you're saying about privatization and all
that is definitely something we've seen in our research.
Speaker 2 (28:18):
I mean, twenty twenty three is the weakest.
Speaker 3 (28:20):
Year for small cap IPOs by our tracking, and it's
something I'm definitely worried about for the long term. But
moving on, there's a lot of managers out there, a
lot of your peers out there that are kind of
and defend their box type territory. Because the Russell two
thousand has been underperformed the S and P for so long.
What do you think since store maybe for twenty twenty five,
(28:40):
for the Russell two thousand as a whole.
Speaker 4 (28:42):
I mean, I think that the relative valuations of the
Russell are more attractive than they've been relative to large
cap companies. I think it comes down to a question
of whether the productivity gains due to sort of generative
AI accrue to the largest companies or if it helps
(29:04):
the smallest companies. What has traditionally happened is that technological
changes benefit the smaller companies because they're easier to be adopted.
The larger cap companies tend to be passed over by
the technological change. And you have this, you know what
the US economy is tremendously good at is this creative destruction.
(29:28):
But this does may not hold with software for some reason.
So if you think about a company like JP Morgan,
and you think about your favorite small cap bank, and
you think about which company is going to be better
able to increase productivity as a result of you know,
if generative AI becomes as strong as you know, sort
(29:50):
of optimist are anticipating which company is going to be
better able to increase productivity. What should be happening is
the it should help the smaller cap company a little
bit more. They can do more with fewer people. It's
easier for them to adopt their procedures, it's easier them
to change. But what we're seeing in the immediate future
is it's helping the larger cap companies because they have
(30:13):
more capital to expand, to create these structures necessary to
integrate the technological change. And I think the future of
small cap stocks is dependent upon whether the technological changes
continue to be monopolized or come benefit the larger companies
(30:34):
more than the smaller cap companies. And that has to
do with questions of monopolies from these sort of platform
type large tech companies and the question of well, why
aren't the productivity gains, why are they flowing from the
largest companies to the smallest instead of from the smallest
companies to the largest, which is how the economy always
(30:55):
functioned prior to basically two thousand and I think that's
going to be the key question. The key question is
whether a small cap company and you don't need many
of them, you need one or two or three of them,
can become the next Microsoft, can become the next AOL,
can become the next Google, or whether that growth is
(31:17):
going to be segmented to private companies and that any
technological change is going to be gobbled up at the
top of the food chain, preventing the small cap companies
from growing. And I think that's the most important fundamental
factor driving whether small caps can effectively grow. I mean,
all these companies, all these elements, they obey sort of
(31:41):
power laws where you have small numbers of companies generating
excess returns and compensating you for the majority of companies
that do not perform well. And so the question of
whether can small caps outperform, I don't think is a
valuation question, and you might get a year or two
years where valuations revert to normal levels in terms of
(32:03):
relative valuations. The key question is a growth question, and
can within the small cap universe a company arise that
can grow or are the sort of monopolistic platforms and
the integration of new technology so great amongst the large
cap names that it prevents these smaller companies from growing.
And I think that is more the fundamental question of
(32:26):
whether small caps can regain their historical annualized return levels.
That being said, small caps are very attractive from a
valuation standpoint, just from a common sense standpoint, the trees
will not grow to the sky, and it's reasonable to
have some degree of diversification around a large cap cap
weighted index.
Speaker 3 (32:46):
Basically definitely makes sense and kind of in line with
what we're seeing with a lot of our macro models
that suggest, you know, valuations could expand, but growth could
be pretty anemic given how the economy is kind of
go in right now, and an apologies for this question
in advance.
Speaker 2 (33:02):
I kind of hate answering it myself.
Speaker 3 (33:04):
But with the election only a few weeks away, are
there any policies you're seeing out there that are you're
pretty excited about or less excited about.
Speaker 4 (33:12):
Let's say I did a lot I did you know,
for one of the books I wrote, I did a
whole bunch of stuff on sort of calendar research, which
is kind of anomalies based on timing and election cycles
and things of that sort. And you know, there are
certain segments that do better under one party, there are
certain segments that do better on the other party. All
this is very hard to prove statistically because you just
(33:35):
don't have enough data points. But what what did seem
to be statistically sort of significant is the more divided
the government, the better the stock market seems to do.
So if you're taking a step back and you can
remove yourself from all the sort of raw rahing on
one side or the other, if the government can remain
(33:57):
divided so that the structure the regulatory is nothing changes,
that is probably the best outcome for an equity investor,
So a divided government is less likely to produce changes
that are going to cause you know, massive changes and
flows of capital are going to provide winners to some
(34:19):
companies and losers to other companies, and effectively, you know,
what you have priced in in the equity market is
then reflecting what the future earnings growth are is currently.
So I think as an investor, one thing to hope
for is for a divided government and for the government
to make the least amount of changes based on what's
(34:42):
occurred in the past.
Speaker 2 (34:43):
Basically, yeah, totally. Markets love certainty.
Speaker 3 (34:47):
And the other big story obviously kind of over the
next coming weeks is earning season. Is there anything you're
watching in particular? Is earning season? Gets going in earnest
or small caps?
Speaker 4 (34:58):
The key question is whether we can see some sort
of broadening of the earnings growth that has has been
extremely strong amongst the large cap companies. And there's some indication,
you know, just based on price movements and based on
what we're seeing in terms of year over year projected
earnings growth, that we are going to see some broadening
if the economy does not head into a recession, which
(35:21):
looks less and less likely it would not be unheard
of for sort of an equal weighted or smaller cap
weighted benchmark to outperform a larger cap weighted benchmark. As
sort of the earnings growth tends to sort of broaden
and move away from these extraordinarily large, extremely profitable, extremely
(35:45):
high growth, and reasonably valued companies that make up the
you know, the largest companies in the cap weighted index.
Speaker 1 (35:52):
Basically, before we let you go, I do have one
more question. You know, since you started, or you know,
since ZAX has started, have you noticed any changes in
trends regarding analyst revisions.
Speaker 4 (36:07):
Well, there was rage FD that was passed several years ago,
and that kind of eliminated sort of special flow of
information to certain select analysts. So that sort of increased
the effectiveness of looking at sort of agreement, which is
the extent to which multiple analysts are revising in the
(36:28):
same direction. But generally, what I what I've seen is
just an increase in the efficiency of the market over time.
And you know, bringing it back to sort of the
small cap space, you know, I when I look at you,
I qualitatively look at a couple of companies and I
find some very small cap company that looks interesting, and
(36:49):
it looks like it has some earnings growth, and maybe
it doesn't even have any analysts following it, and I'll
start to read the ten k's and ten ques, and
what will come up is that in the process of
doing this, there'll be two activist investors that will come
on board and start sort of looking for changes to occur.
So the level of sort of pouring over the public
(37:12):
equity markets has increased, and as a result, I think
the efficiency of the market has increased, and sort of
the mean reversion due to valuation metrics has decreased, which
I think actively helps estimate revision processes, because what you're
doing with estimate revision processes when you're implementing them quantitatively
(37:35):
is you're looking for not necessarily misvaluation on any one equity.
You're looking in aggregate that your valuation is basically in
line with your benchmark. And the more efficient the market is,
the more likely that's going to occur, and the less
likely there's going to be mean reversion in terms of
sort of valuation metrics. So over time, what I've seen
(37:56):
is an increase in efficiency, an increase of interest amongst
smaller cap companies, and a less more likely that a
company's stock price, no matter that it's capitalization, is going
to reflect current earnings outlooks, So as a result, it's
more likely conceivably to respond to upward estimate revisions that
(38:19):
materialize that have not yet been published.
Speaker 1 (38:22):
Basically, it's great well Mitch. I enjoyed this and wanted
to thank you again for joining us.
Speaker 4 (38:28):
David and Michael, it's been a pleasure. Everyone have a
good rest of the week.
Speaker 1 (38:31):
And Mike, thank you for joining me today as Mike Coast.
Thank you both until our next episode. This is David
Cone with Inside Active
Welcome to Inside Active, a podcast about active managers that
goes beyond sound bites and headlines and looks deeper into
their processes, challenges and philosophies and securitance selection. I'm David Cohne.
I'd be Mutual fund and Active Research at Bloomberg Intelligence Today.
My co host is Mike Casper, sector and small cap
strategist at Bloomberg Intelligence. Mike, thank you for joining me today.
Speaker 2 (00:35):
Thank you, David.
Speaker 1 (00:36):
Well, you and Chris Kane put out a note last
week talking about applying bi's proprietary b MVP multi factor
methodology for the small cap Bloomberg two thousand. Can you
give a brief overview of the methodology and how it's performed.
Speaker 3 (00:51):
Sure, So what we do is we start with the
Bloomberg two thousand index. That's kind of roughly equivalent to
the Russell two thousand. It's just mark a cap weighted though,
so there are some slight differences on the edges. But
what we do is we take that universe and we
apply a profitability screen to it. It's awfully difficult to
kind of make conclusions on fundamentals without having such a screen,
(01:12):
given that about a third of stocks in the Russell
two thousand or in small caps in general aren't profitable
at the moment, So we take that profitability screen and
that whittles down the universe a little bit further. I
think as of September we were at about eleven hundred
constituents left over from the two thousand. When we do that,
and then we apply our momentum scores on a sector
(01:32):
neutralized basis. All these factors are on a sector neutralized basis,
but momentum the average of trailing six and twelve month
total returns excluding the last two weeks. We apply our
low volatility factor, our value factor, which four small caps,
we just look at the trailing twelfth month sales to
price ratio, and our profitability factor, which is the average
of trailing twelve month ROE and ROIC, and then the
(01:56):
top descal of those percentile ranks once we average those out,
is the final b MVP small cap portfolio. Now a
little bit of stats before I go into the back
test result. That results in a little bit higher market
cap weighting than the B two thousand. For example, so
the Bloomberg MVP, the media market caps about two point
(02:17):
eight billion. In that universe with the largest stock about
eight and a half billion, small stock about two hundred
and eighty seven million, So the market cap range of
the B two thousand as a whole as much wider
the same thing with the Russell two thousand. The media
market caps about a billion. But onto results, the cumulat
return for this thing is pretty solid. So back to
(02:39):
nineteen ninety nine through September twenty twenty four, you're looking
at percent significantly lower volatility than the Russell two.
Speaker 2 (02:46):
Thousand equal WEIGHTD.
Speaker 3 (02:47):
But what I really like to compa against is the
only profitable bin in the B two thousand. That annualized
return is about eleven point eight five percent, so we
are generating some alpha over that bin using.
Speaker 1 (02:58):
This nice I think this is a great time to
introduce our guest, Mitch Zach's. He's CEO and principal portfolio
manager of Zach's Investment Management. Mitch, thank you for joining
us today.
Speaker 4 (03:11):
David and Michael, it's a pleasure to be here.
Speaker 1 (03:13):
I'd like to start off, you know, just hearing more
about your career background.
Speaker 4 (03:17):
Sure, I you know, went to school at Yale and
I joined Lazard Freyer and Mergers and Acquisitions as an
analyst I left the analyst program to join Zach's Investment Research.
At the time, Zach's and we founded a division of
Zach's Investment Research, which was a money management subsidiary, a
(03:38):
wholly owned money management subsidiary. At the time I joined,
we had roughly fifty million dollars in assets under management,
and over the past twenty some odd years, you know,
twenty odd plus years actually at this point in time,
we've been able to grow the firm from around fifty
million dollars in asset to a little bit over nineteen
(04:01):
billion dollars in assets under management and model delivery and advisement.
So you know, that's kind of my background. I've been
working with quantitative equity portfolio strategies my entire career, and
I've designed most of the asset management strategies that we
implement at Zach's. Zach's Investment Management's a wholly owned subsidiary
(04:25):
of Zach's Investment Research. Zach's Investment Research is the second
largest provider of independent investment research in the country, and
our contribution to finance was that we created the concept
of the quarterly consensus earnings estimate before Za's came around.
What people were doing was pretty much looking at how
(04:45):
earnings evolved on a year over year basis, looking at
how that earnings growth compared to sort of a trend
line analysis based on historical earnings growth, and saying, well,
earnings have been growing at you know, ten percent, in
at twelve percent year over year earnings growth, it was
a good quarter. What we started doing is we created
this concept of the quarterly consensus earnings estimate, where we
(05:09):
took to the cell side earnings estimates by all the
analysts following these companies, and we created a consensus and
then when a company would report earnings, it would be
compared to what Wall Street expectations were. If the earnings
were greater than Wall Street expectations, it would be deemed
a earning surprise, and if they were less than Wall
(05:32):
Street expectations, it would be a negative earning surprise. And
we started to look at this data we created and
started to say, well, how can we use this data
to effectively manage money? And what we found to be
the most important use of the data is not to
look at the year over year earnings growth and instead
(05:54):
to focus on how the earnings estimates are changing over
time and what we found to be the case is
that there's some degree, it's a reasonably strong degree given finance,
you know, time series metrics of earning of serial correlation
with earnings estimate revisions, and that became the basis of
(06:15):
some of our proprietary models that we then started the
asset management firm with.
Speaker 1 (06:21):
So you mentioned, you know, earnings revision. Can you, I
guess speak to you know how that affects your investment philosophy?
And you know, I'm thinking, actually, you know your investment
process specifically for the ZACH small cap or fund.
Speaker 4 (06:33):
Sure, what we're What we found to be the case
is that companies that have received upward earnings estimate revisions
in the past are more likely to receive upward earnings
estimate revisions in the future. And if you own a
group of companies that have received upward earnings ESTEMA revisions
(06:57):
in the past, you're roughly about sixty percent more likely
to receive upward earning cestemer revisions in the immediate future.
So the first thing I started to do is to
try and say, well, what if we create a portfolio
where we simply own all the companies that are receiving
upward earning customer revisions, we hold them for a certain
period of time, and then we rebalance the portfolio. And
(07:21):
what happens is if you kind of apply that sort
of very very structured approach, you tend to have a
very high degree of turnover in the portfolio. Roughly sixty
seventy percent of the companies that are receiving upward earning
cestemer revisions at the beginning of the month are not
(07:41):
receiving upward earning customary visions over the end of the month,
So there tends to be a high degree of turnover.
You also tend to get a very very high degree
of sector concentration, so you not only see a high
degree of turnover, you see a level of sector concentration
that increases sort of the volatility and tracking error of
(08:04):
the portfolio relative to the benchmark. So what we started
to do is saying, well, how can we control the
turnover and how can we control the sector exposure to
generate a return that's more in keeping with the benchmark.
And that's what I've been doing for many years, and
(08:25):
in the ZAC small cap core process as a mutual
fund CIX, which is a very small mutual fund which
actually kind of helps us in terms of being able
to move in and out of these positions. We're trying
to control the turnover so that the turnover is around
one hundred percent per year, and we're trying to control
(08:46):
the tracking error as much as we can through optimization
to try and keep the risk in line with our
benchmark of the Russell two thousand.
Speaker 3 (08:56):
Basically, now I give my whole feel at the beginning
of this about how profitable companies tend to outperform the
egaloid to benchmark. It sounds like we're kind of arriving
at similar things you with earning s revision mean with
profitable stocks. When you look at earning servision, do you
only select profitable companies or do you care if it's
going from unprofitable to less unprofitable.
Speaker 4 (09:19):
What we're looking for are changes in the earnings estimate
revision so that they're positive. So we're looking for sort
of four factors, agreement to the extent to which multiple
analysts are revising their earning estaments in the same direction, magnitude,
the size of those estimate revisions upside, where the most
(09:39):
accurate or recent earnings estaments are coming in relative to
the consensus, and the earning surprise, the frequency and magnitude
of earning surprises that have occurred in the process that
we're implementing. We're using the Russell two thousand universe as
the initial universe of selection, and we're adding to at
(10:00):
any company that's in our current portfolio that may have
been removed from the index at some point in time.
So the union of those two sets is really our basis.
What I will say is that companies that are getting
negative earnings are a little bit less likely to get
positive earnings estimate revisions because they're sometimes in some sort
(10:22):
of growth phase where the management is spending money for
some sort of potential you know, jackpot in the future,
so to speak. So they might be a biotech company
where the management has decided, hey, we're going to continue
to spend money to engage in the drug development and
(10:43):
our hope is that it gets approved at the end
of the entire process. So generally, those companies that are
kind of in the development phase that are based on
massive capital expenditures to sort of capture a market will
likely not get upward earning cestmer visions, And in some
of those instances, it's possible they get downward esimate revisions,
but they're getting closer to their sort of goals in
(11:06):
terms of growth that the stock price will respond positively.
So they are two separate ideas. One idea is, hey,
can you look at companies that have positive earnings to
do companies with positive earnings or positive expected earnings outperformed
companies with negative earnings. And that has historically been the case,
(11:26):
but that's been widely disseminated amongst managers. So I'm not
positive that's going to be completely the case over time.
I think it might be a little bit better instead
of sort of throwing out you know, I guess you know,
roughly thirty percent of the Russell two thousand to instead say, okay,
are these companies? These companies are expected to lose money,
(11:48):
but they expected to lose less money this quarter? Are
they expected to lose less money over the last month
because analysts are revising their earnings estimates upward. And what
I've actually anecdotally found to be the case is companies
that move from a nonprofitable sort of scenario where they're
(12:08):
losing money to companies that are going to generate positive
earnings tend to maybe not all the time, but have
a potential for very very strong returns, and you don't
want to necessarily remove those from the portfolio.
Speaker 1 (12:23):
That makes sense. You know, I'm familiar with Zach's investment research,
and you know kind of you know Zach's rank and
you know strong buy stocks. Does it kind of follow
the portfolio kind of follow something similar where it only
includes like strong buy or buy stocks according to those formulas.
Speaker 4 (12:42):
Yeah, in zsci X, which has been performing relatively well
over the last you know, a couple of years. Here,
what we're doing is we're combining an estimate revision model
with a model that focuses on quality. We're using that
sort of alpha model as an input to an optimization
routine to generate a buy, a hold, and sell list
(13:03):
that we then evaluate and pretty much implement. But there
is a basis in the alpha model looking at companies
that are likely to receive upward earnings estimate revisions in
the future. But you have to remember that we again,
the pure estimate revision universe has a high degree of turnover,
(13:25):
and what we're trying to do is use earning sest
revisions as an alpha source and then control the turnover
and control the sector exposure. So the overall risk of
the portfolio is in line with the Russell two thousand benchmark.
So to answer the question, what we're doing is we're
using earnings estimate revisions along with another model in a
(13:45):
multi factor model, but we're using that as an input
to optimization that then can identify companies. So it's a
subset of that group that might be potential by candidates,
and we might even go outside of that group if
the optimization indicates that adding that position could conceivably reduce
(14:06):
tracking error.
Speaker 1 (14:08):
Okay, so you know, if you're starting at this universe,
you know, you know, I know the four factors of
zach Ring. So that is, you know, something that's just
kind of at the very beginning, is what you're looking at.
Speaker 3 (14:20):
Now, are there any sectors that you find particularly compelling
at the moment or that are being flagged for overweights
right now?
Speaker 4 (14:28):
What I've found to be the case over time is
that sector risk control is less effective than looking at
projected tracking error relative to a benchmark. So what happens
is if you focus on sort of sector exposure, what
(14:48):
tends to happen is there'll be companies that are classified correctly.
They are companies that are misclassified based upon their sector.
But there are companies that will be classified correctly based
on their sector, but they will move like a company
that is outside of that sector. So, for instance, right now,
there are companies that are construction companies that are helping
(15:12):
construct sort of cloud computing centers, helping with construct infrastructure
on semiconductor equipment, manufacturing plants, and things of that sort,
and these companies will be correctly classified as construction companies.
They build things, they issue debt, they use the debt
to buy to buy land to build things, and they
(15:34):
sell the they sell their what their access or they
sell their effective use to a company that needs to
use that type of building or that type of source.
But in terms of their contribution to risk, they behave
much more like semiconductor companies than construction companies. So I
tend not to try and think from a top down perspective.
(15:57):
I tend to want to think from a bottom up perspective.
And what we're looking for when we do do some
qualitative oversight in some of our other strategies is we're
looking for lack of crowding in the market. So we
don't want to look for a individual company where there's
a tremendous amount of information or focus from either the
(16:22):
media or from investors on what the outcome of or
how that company is evolving over time. But in terms
of like if you had to pin me down in
terms of sectors, I think industrials are looking relatively interesting
at this point in time.
Speaker 1 (16:39):
Kind of want to move to a slightly different topic, actually,
market caps something that I've kind of been looking at recently,
where if you think of the traditional dollar value of
what one considers a small cap, you know, up to
about two billion. I've been noticing a lot of mutual
funds holding much larger stocks. I know, for this particular one,
(17:00):
I think about half the portfolios in midcaps, you know,
which is, you know, much less than what I've seen.
Some small cap funds are even holding large caps. So
I'm just asking you know, for you is it Do
you think this is due to small companies growing into
midcaps or just they're being less available small cap companies.
Speaker 4 (17:18):
I think that if you look at the companies right
now that are getting upward earning cestemer visions, they tend
to be more mid cap companies. If you look at
various quality metrics that we're combining with the estimate revisions,
it also tends to bias more towards MidCap companies. And
going back to I think Michael said, if you look
at those companies that are having a negative earnings, I
(17:42):
believe that they also correlate with small with much smaller
cap companies. So we're agnostic in terms of capitalization as
long as it is within the Russell two thousand universe
or within our portfolio. But we are looking for companies
are receiving upward earning cestemer revisions. We are looking for
companies that have a reasonable score on our quality metric,
(18:06):
and we are looking for companies that, when added to
the portfolio, will conceivably provide us with the alpha score
while reducing the tracking error relative to the benchmark. So
you know, part of this is that you know, midcaps
have been performing a little bit better than small caps.
As you go down capitalization ranges, the number of companies
(18:29):
that are not quote high quality or good companies tends
to increase. So as you go down to sub five
hundred million dollar market cap, as you go down to
sub two hundred million dollar market cap, the number of
companies that really shouldn't be public tends to increase. The
number of companies that are kind of basically completely ignored
(18:49):
by the market for usually a good reason also tend
to increase. So if you start using quantitative factors that
statistically have just gener returns, profitability, momentum, earnings, estimate revisions quality,
that will always try and sort of push you away
(19:11):
from smaller cap companies. You also have the case that
with estimate revision based strategies there tends to be a
correlation between higher market cap and the amount of analyst coverage.
So the larger the market cap, not all the time,
it really depends on the sector and how much in
vogue that company is, but generally the larger the market cap,
(19:33):
the more analyst coverage you have. So again I don't
think it's a function of a qualitative decision. It's more
a function of the result of the quantitative models we're
using to try and generate returns. And my guess is
in the process that was described at the top of
the hour, there would also be a somewhat of a
MidCap bias if you're using low volatility and price momentum
(19:57):
and valuation metric to take a look at things based.
Speaker 1 (20:01):
Okay, do you incorporate valuation metrics at all when you're
looking at, you know, quality companies, and.
Speaker 4 (20:08):
In this process we're somewhat valuation agnostic, so we're really
focused on, well, if the company's receiving upward earning estament revisions,
does adding that company to the portfolio potentially reduce the
tracking error in other strategies that we runs. As I mentioned,
we're currently managing a little bit over nineteen billion dollars.
(20:29):
We do look at valuation as a primary factor that
we're looking at. My experience has been that as you
move lower in capitalization, the returns from sort of the
smaller cap universe tend to accrue a little bit more
towards technology and more towards some biotech companies because they're
(20:54):
able to effectively compound over time, go from becoming a
small cap company to a mid cap and maybe even
potentially a small large cap company, and that compounded return
compensates you for the losses that you have with all
these other companies. So that if you look at valuation
(21:16):
amongst very very small cap companies, you can run into
a somewhat of evaluation trap where you find companies that
are attractively valued and there might be an initial push
upward as the price book ratio you know, reverts to
historical means amongst historical levels amongst sort of bank stocks.
(21:38):
But these companies don't have the ability to compound on
an annualized basis at a high enough rate to compensate
you for the smaller cap names that might not perform
well or might be value traps. So what I found
to be the case is that valuation metrics seem to
(21:58):
make more sense and the larger cap that you moved towards,
and as you move towards smaller cap names, you know,
you have the traditional Fama French research which shows this
anomaloust return amongst small cap value names, and I'm not
quite sure if that has been picked over by the
market with the amount of capital that's that's looking for
(22:22):
these opportunities. So if you if you think about value stocks,
and you're saying, well, do small cap value stocks necessarily
always out reform small cap growth stocks? You're looking at
this regression analysis that's saying, well, yes, small cap value
gives you a bias, But it's possible. We've seen sort
of a such a flood of capital into the small
(22:43):
cap space that that thirty five dollars small cap company,
or that twenty dollars small cap company is now worth
twenty two dollars, or is now worth thirty six or
thirty seven dollars, and that would eliminate the annualized return
from owning those small cap value companies, And anecdotally, that's
what I happening. So I think that as the market
becomes more efficient, valuation as a metric sometimes loses some
(23:10):
of its historical outperformance. And I'm much more comfortable owning
companies that are receiving upward earning vest revisions and controlling
the risk through tracking you a relative to the benchmark,
looking at the PE multiple, looking at cash will multiples,
just eyeing them to make sure they're in line with
the benchmark in aggregate, than saying let's try and select
(23:32):
those companies in the Russell two thousand that have the
lowest p multiple relative to the industry group that they're
trading in.
Speaker 2 (23:40):
Basically, So, we.
Speaker 3 (23:44):
Talked a little bit about profitability, and you mentioned that
it's pretty well disseminated across the market that profitability in
small caps works, right, And when we were breaking down
our MVP model across the four factors. We notice that
pretty much any naive factor that you want to look
at for the long end of a factor was working
very well over the long term. Do you think that's
(24:04):
kind of more of a function of factor crowding given
your commentary on profitability and everybody kind of be in there,
or do you think it's more, you know, small caps
are inefficient or something else.
Speaker 4 (24:16):
What I think is happening is that the nature of
the small cap market is changing over time. So if
you take a step back and you say, well, what
type of company would be going public in the small
cap space, it would be a company looking for capital
(24:39):
where the capital is not available through bank financing, so
they don't have any physical, tangible assets, they can't engage
in any bank financing. They have an idea, they're going
to create an online service, they're going to create a
new generative AI, whatever it is, and they need a
massive amount of capital to grow. Historically, in the seventies,
(25:04):
in the eighties, in the nineties, even in the early
two thousands, these companies would go public at reasonably small capitalizations.
They would use the capital and grow. Now what is
happening is that the companies that are capable of growing
with capital expenditure and growing very largely with capital expenditure,
(25:26):
are staying private. And the companies that are non profitable
are not growth companies that have gone public. They're remnant
public companies that really shouldn't be public and are losing
money for some purpose, for some reason, because they're under
competitive pressure. So what I think is happening is there's
adverse selection within the smaller cap names, within the Russell
(25:50):
two thousand that the companies that are going public are
going public because they can't remain private and they can't
attract financing to grow. So what's happening is that as
those companies that show up as nonprofitable in the smaller
cap name, they're more likely to be less likely to
(26:11):
be able to compound on a very very high basis.
So it's you have this sort of two tiered market system.
You have all these remnant companies that are public. They
shouldn't be public, they are not generating any money, they
have a low return on equity, they should not continue
to trade. But there's there and there's no methodology to
(26:33):
remove it. Meanwhile, the good private companies are staying private
and coming public at much much higher capitalization levels. So
a lot of the research showing that hey, small caps
dramatically outperform over time may be skewed until we kind
of receive these companies that are going public and can
(26:54):
grow in the small cap space. But in terms of
you know, what happens is it's it's very interesting in
the mark is that as an idea becomes readily disseminated
and acted upon it, the market changes and it ceases
to reflect that idea. The biggest example I can come
up with this sort of the accrural phenomenon, where there
was this indication that there's an anomaly and maybe the
(27:17):
anomaly was due to data mining, but everyone was talking
about accurals. There was all this research done on purls
and then if you go back and you look, you know,
five ten years after the crural phenomenon was publicized, companies
that with attractive accurals are no longer outperforming. And I
am I would be a little bit wary of going
(27:40):
into the small cap space. And because this phenomenon where
unprofitable companies have dramatically underperformed for such a period of time,
and it's just accepted wisdom that if the company is
not profitable, it has to underperform the profitable company that
the market may have adjusted valuations in such a way
that that no longer holds to be the case going forward.
(28:01):
And that's why I think it's important to focus on
the earnings estimate revisions as opposed to necessarily the profitability
or the valuations.
Speaker 3 (28:11):
Basically, Yeah, and what you're saying about privatization and all
that is definitely something we've seen in our research.
Speaker 2 (28:18):
I mean, twenty twenty three is the weakest.
Speaker 3 (28:20):
Year for small cap IPOs by our tracking, and it's
something I'm definitely worried about for the long term. But
moving on, there's a lot of managers out there, a
lot of your peers out there that are kind of
and defend their box type territory. Because the Russell two
thousand has been underperformed the S and P for so long.
What do you think since store maybe for twenty twenty five,
(28:40):
for the Russell two thousand as a whole.
Speaker 4 (28:42):
I mean, I think that the relative valuations of the
Russell are more attractive than they've been relative to large
cap companies. I think it comes down to a question
of whether the productivity gains due to sort of generative
AI accrue to the largest companies or if it helps
(29:04):
the smallest companies. What has traditionally happened is that technological
changes benefit the smaller companies because they're easier to be adopted.
The larger cap companies tend to be passed over by
the technological change. And you have this, you know what
the US economy is tremendously good at is this creative destruction.
(29:28):
But this does may not hold with software for some reason.
So if you think about a company like JP Morgan,
and you think about your favorite small cap bank, and
you think about which company is going to be better
able to increase productivity as a result of you know,
if generative AI becomes as strong as you know, sort
(29:50):
of optimist are anticipating which company is going to be
better able to increase productivity. What should be happening is
the it should help the smaller cap company a little
bit more. They can do more with fewer people. It's
easier for them to adopt their procedures, it's easier them
to change. But what we're seeing in the immediate future
is it's helping the larger cap companies because they have
(30:13):
more capital to expand, to create these structures necessary to
integrate the technological change. And I think the future of
small cap stocks is dependent upon whether the technological changes
continue to be monopolized or come benefit the larger companies
(30:34):
more than the smaller cap companies. And that has to
do with questions of monopolies from these sort of platform
type large tech companies and the question of well, why
aren't the productivity gains, why are they flowing from the
largest companies to the smallest instead of from the smallest
companies to the largest, which is how the economy always
(30:55):
functioned prior to basically two thousand and I think that's
going to be the key question. The key question is
whether a small cap company and you don't need many
of them, you need one or two or three of them,
can become the next Microsoft, can become the next AOL,
can become the next Google, or whether that growth is
(31:17):
going to be segmented to private companies and that any
technological change is going to be gobbled up at the
top of the food chain, preventing the small cap companies
from growing. And I think that's the most important fundamental
factor driving whether small caps can effectively grow. I mean,
all these companies, all these elements, they obey sort of
(31:41):
power laws where you have small numbers of companies generating
excess returns and compensating you for the majority of companies
that do not perform well. And so the question of
whether can small caps outperform, I don't think is a
valuation question, and you might get a year or two
years where valuations revert to normal levels in terms of
(32:03):
relative valuations. The key question is a growth question, and
can within the small cap universe a company arise that
can grow or are the sort of monopolistic platforms and
the integration of new technology so great amongst the large
cap names that it prevents these smaller companies from growing.
And I think that is more the fundamental question of
(32:26):
whether small caps can regain their historical annualized return levels.
That being said, small caps are very attractive from a
valuation standpoint, just from a common sense standpoint, the trees
will not grow to the sky, and it's reasonable to
have some degree of diversification around a large cap cap
weighted index.
Speaker 3 (32:46):
Basically definitely makes sense and kind of in line with
what we're seeing with a lot of our macro models
that suggest, you know, valuations could expand, but growth could
be pretty anemic given how the economy is kind of
go in right now, and an apologies for this question
in advance.
Speaker 2 (33:02):
I kind of hate answering it myself.
Speaker 3 (33:04):
But with the election only a few weeks away, are
there any policies you're seeing out there that are you're
pretty excited about or less excited about.
Speaker 4 (33:12):
Let's say I did a lot I did you know,
for one of the books I wrote, I did a
whole bunch of stuff on sort of calendar research, which
is kind of anomalies based on timing and election cycles
and things of that sort. And you know, there are
certain segments that do better under one party, there are
certain segments that do better on the other party. All
this is very hard to prove statistically because you just
(33:35):
don't have enough data points. But what what did seem
to be statistically sort of significant is the more divided
the government, the better the stock market seems to do.
So if you're taking a step back and you can
remove yourself from all the sort of raw rahing on
one side or the other, if the government can remain
(33:57):
divided so that the structure the regulatory is nothing changes,
that is probably the best outcome for an equity investor,
So a divided government is less likely to produce changes
that are going to cause you know, massive changes and
flows of capital are going to provide winners to some
(34:19):
companies and losers to other companies, and effectively, you know,
what you have priced in in the equity market is
then reflecting what the future earnings growth are is currently.
So I think as an investor, one thing to hope
for is for a divided government and for the government
to make the least amount of changes based on what's
(34:42):
occurred in the past.
Speaker 2 (34:43):
Basically, yeah, totally. Markets love certainty.
Speaker 3 (34:47):
And the other big story obviously kind of over the
next coming weeks is earning season. Is there anything you're
watching in particular? Is earning season? Gets going in earnest
or small caps?
Speaker 4 (34:58):
The key question is whether we can see some sort
of broadening of the earnings growth that has has been
extremely strong amongst the large cap companies. And there's some indication,
you know, just based on price movements and based on
what we're seeing in terms of year over year projected
earnings growth, that we are going to see some broadening
if the economy does not head into a recession, which
(35:21):
looks less and less likely it would not be unheard
of for sort of an equal weighted or smaller cap
weighted benchmark to outperform a larger cap weighted benchmark. As
sort of the earnings growth tends to sort of broaden
and move away from these extraordinarily large, extremely profitable, extremely
(35:45):
high growth, and reasonably valued companies that make up the
you know, the largest companies in the cap weighted index.
Speaker 1 (35:52):
Basically, before we let you go, I do have one
more question. You know, since you started, or you know,
since ZAX has started, have you noticed any changes in
trends regarding analyst revisions.
Speaker 4 (36:07):
Well, there was rage FD that was passed several years ago,
and that kind of eliminated sort of special flow of
information to certain select analysts. So that sort of increased
the effectiveness of looking at sort of agreement, which is
the extent to which multiple analysts are revising in the
(36:28):
same direction. But generally, what I what I've seen is
just an increase in the efficiency of the market over time.
And you know, bringing it back to sort of the
small cap space, you know, I when I look at you,
I qualitatively look at a couple of companies and I
find some very small cap company that looks interesting, and
(36:49):
it looks like it has some earnings growth, and maybe
it doesn't even have any analysts following it, and I'll
start to read the ten k's and ten ques, and
what will come up is that in the process of
doing this, there'll be two activist investors that will come
on board and start sort of looking for changes to occur.
So the level of sort of pouring over the public
(37:12):
equity markets has increased, and as a result, I think
the efficiency of the market has increased, and sort of
the mean reversion due to valuation metrics has decreased, which
I think actively helps estimate revision processes, because what you're
doing with estimate revision processes when you're implementing them quantitatively
(37:35):
is you're looking for not necessarily misvaluation on any one equity.
You're looking in aggregate that your valuation is basically in
line with your benchmark. And the more efficient the market is,
the more likely that's going to occur, and the less
likely there's going to be mean reversion in terms of
sort of valuation metrics. So over time, what I've seen
(37:56):
is an increase in efficiency, an increase of interest amongst
smaller cap companies, and a less more likely that a
company's stock price, no matter that it's capitalization, is going
to reflect current earnings outlooks, So as a result, it's
more likely conceivably to respond to upward estimate revisions that
(38:19):
materialize that have not yet been published.
Speaker 1 (38:22):
Basically, it's great well Mitch. I enjoyed this and wanted
to thank you again for joining us.
Speaker 4 (38:28):
David and Michael, it's been a pleasure. Everyone have a
good rest of the week.
Speaker 1 (38:31):
And Mike, thank you for joining me today as Mike Coast.
Thank you both until our next episode. This is David
Cone with Inside Active
Host
Gina Martin Adams
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