April 2, 2025 • 36 mins
Adding the profitability factor to the value factor can filter out value traps and help value stocks outperform the market, according to Bloomberg Intelligence. In this episode of the Inside Active podcast, host David Cohne, BI mutual fund and active management analyst, along with co-host Christopher Cain, BI’s US quantitative strategist, spoke with John Davi, founder, CEO and chief investment officer of Astoria Portfolio Advisors about the company’s systematically active ETFs, including the Astoria US Equal Weight Quality Kings ETF (ROE), and the long-term value of quality stocks. They also discussed why equally weighting stocks can mitigate concentration risk, combining multiple factors can improve risk-adjusted returns and why adjusting factor weights based on valuation is crucial. This podcast was recorded on Feb. 24.
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Speaker 1 (00:15):
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 security selection. I'm David Cohne,
I lead mutual fund and active research at Bloomberg Intelligence.
Today my co host is Christopher kine, Us, quantitative strategist
at Bloomberg Intelligence. Chris, thank you for joining me today.
Speaker 2 (00:36):
Thank you so much for having me. David.
Speaker 1 (00:38):
So, you wrote a recent note about adding profitability to
value stocks or the profitability factor. Since our conversation today
will be focused on quality, I wonder if you could
give us a sense of how adding that profitability factor
to a standalone value strategy performs.
Speaker 2 (00:56):
Yeah. I'm a big fan of adding profitability to value.
I mean just in empirically, you know, it seems to
work better and it's very logical. So you know, just
as a as a review, you know, value was the
only factor that underperformed in twenty twenty four by our measurement.
If you look at the long short factor, it was
the only one that's down on the year, And if
(01:16):
you only look at the long only legs, it was
the only one that underperform the market. So it so
just Q one value. Just cheap stocks returned about ten
percent last year in the Russell one thousand as compared
to about thirteen or so percent return for the equal
weighted a Russell one thousand. If you added some profitability
to that, that increased the returns to about fifteen to
(01:37):
sixteen percent. So you know it, I'll perform a market
if you just had that profitability component. To me, you know,
it's very logical because you know, sometimes value stocks are
valued for a reason. You know they have, you know,
not good prospects and little chance for a turnaround. If
you add a profitability filter to your value factor, you
kind of filter out those value traps or you know,
(02:00):
cheap companies that are cheap for a good reason. And
that seems to work good both in the short term
and the long term.
Speaker 1 (02:06):
That makes sense. So I think we'd like to introduce
someone who's actually no stranger to quality, John Davey. John
is founder, CEO, and CIO of Astoria Portfolio Advisors. John,
thank you so much for joining us today.
Speaker 3 (02:20):
Great great to be here, guys, thank you for having
me on.
Speaker 1 (02:23):
So before we get into talking about your investment process,
Can you tell us more about your start in the
investment industry and what led you to start Astoria.
Speaker 3 (02:32):
Sure. So I began as a quantitative derivative analyst and
Merrill Lynch's derivative research group in the late nineties. So
it was an intern you know, basically spent my first
ten years kind of doing index level research. Quant research,
always at the index level, so that included like index
(02:54):
futures and next options ETFs. You know, got popular in
the late nineties and so ets were underneath our research group.
So then did that for ten years, and then I
became head of Morgan Stanley's ETF content for institutional clients.
And you know, I had always liked macro research, asset allocation,
(03:17):
and you know, always wanted to join the buy side
and be an entrepreneur. So I felt, you know, here
is a way to kind of do both kill two
birds one stone, kind of joined the buyside and also
to you know, be an entrepreneur.
Speaker 1 (03:29):
So let's talk a little more about Astoria. How would
you describe the Astoria investment philosophy.
Speaker 3 (03:35):
Yeah, so we I would say it's it's a combination
of there's kind of two verticals, like two business lines.
One is like quantitative research, and the expression could be
like an SMA, a quant SMA could be an ETF.
And then the other business line is kind of multi
asset ETF investing kind of asset allocation. So really I
(04:01):
would say it's it's combining active and passive. That's kind
of what we do, you know, at our firm. I
would say our ETFs are systematically active. It's very rules based.
Implementation is active like when we actually rebalance and how
we trade, and I can spend a lot of time
(04:23):
talking about that, but it's very kind of rules based,
quantitative in nature. Our asset allocation is you know, I
would say the idea there is that we want to
use the business cycle, use earning valuations, use sentiment in
order to kind of dictate whether or not we want
to be ovoid on the way in NASA class, and
(04:44):
then we use a tiny bit of liquid olts in
order to kind of hedge our left tail risk. Once
you hedge your left tail risk, which I know we're
in a bull market, but we've had many crisises the
last you know, seven eight years, so that ability to
kind of of hedge left tail risk, I think is
quite important and you know, just use like a little
(05:05):
bit of liquid alts in order to kind of hedge that.
So that's kind of like our two business lines. One
is quant sma's ETFs, the other is multi ASCID ETF
model portfolios.
Speaker 1 (05:16):
Great. So if we want to, you know, focus in
on the ETF specifically OROE the astoria US equal Weight
Quality Kings ETF, is there a process you follow to
narrow down a universe and then select the stocks for
the portfolio.
Speaker 3 (05:31):
Absolutely, so you know the idea there is that, you know,
and for background, you know, we all worried about the
concentration risk and the ND the CES. You know, ten
stocks make up forty percent of the S and P
five hundred and we launched are we back in August
first of twenty twenty three, So and you know it
takes like, you know, three six months to let's say,
(05:53):
launch an ETF. So we were worried about concentration risk
for a while, you know, and we looked at the
available options out there and we just weren't quite comfortable.
And I can spend some time talking about what makes
our equo weight approach different, but essentially it's like a funnel, right,
we start with like ten thousand stocks, we filter out,
(06:14):
set out, we picked you know, large liquid you know,
minimum free float, minimum market cap weights. Then we come
up with like eight hundred investible stocks. And the idea
with those eight hundred stocks is if we want to
pick the hundred of you know, let's say the best
in kind of that eight hundred, And what we do is,
(06:36):
you know, we look at five different factors, so things
like quality, valuation, growth, momentum, and dividend. And we want
to so we'll weight our etf So fifty percent of
the quant code is allocated to the quality factor, twenty
percent to the dividend factor, twenty percent to the valuation factor,
(06:59):
five percent to growth factor, and five percent to the
momentum factor. And essentially these winds up being like high
quality companies that are reasonably priced. You know, within each
one of those five factors, there's like three, four or
five fundamental ratios that express the factor of view. So
(07:19):
in the case of like quality, like we're looking at
companies with ro OE r O A, r O I C,
dividing could be you know, the sustainability the dividend, the
dispersion of the dividend. Valuations could be like, you know,
things like PE price, the sales. The growth factor could
be things like PEG ratios. The momentum factor could be
(07:42):
like relative strength indicators and our premise. And you asked
me before, David, like, what do we fundamentally believe in?
You know, we definitely think that the more factors you
can harvest in a portfolio, as long as you harvest
in a low cost manner and as long as you
stick with the factors like that historically has been able
(08:02):
to give you higher risk adjuster returns and kind of
put you higher up on the fishing frontier. So to summarize,
you know, start with like ten thousand investable stocks, filter
down to eight hundred of the ones that are the largest,
most liquid, you know, good free float, and then pick
a hundred of that eight hundred that have you know,
strong roe, strong row, you know, strong quality metrics, that
(08:26):
paid dividends, that are reasonably priced, that have a little
bit of growth and momentum characteristics. It's great.
Speaker 1 (08:33):
So let's focus in on quality for a second. What
is your research shown about the long term value of
quality stocks.
Speaker 3 (08:40):
Well, I liked what Chris said before. I mean, you know,
valuing in beta or valuant quality you know definitely have
very very low negative you know, very low correlations, if
not negative. Right. So if again, if you believe in
this concept of like how do I get higher? But
on the fish frontier, how do I mix factors? You know,
you're picking a factor quality that you know has pretty
(09:04):
robust long term characteristics, and especially if you pair that
with like value or you know, you know small cap
stocks that that looks very very attractive to us for
us at the end of the day, Like you know,
quality are companies that you know are robust, they pay dividends,
they have good earnings, good ROI. You know ro O,
(09:26):
I c roa Roe. And you know it's it's this
concept of like it's persistent, pervasive, robust. It works across sectors,
across countries, across economic cycles, and you know there is
historical out performance.
Speaker 2 (09:42):
Right.
Speaker 3 (09:42):
So if you do some farm of French you know analytics,
you'll see that high quality stocks have beaten low quality
stocks and being the market you know, over the last
you know, half a century, if not longer. I think
their data set goes back to like nineteen sixty three.
So that's what we're looking for. I mean, these could
be you know companies that Microsoft, Google, you know Facebook,
(10:06):
I mean a lot of them tend to be growth now,
but you know it's brand hold names essentially. So that's
that's how we think about it, you.
Speaker 4 (10:17):
Know, John, I I talked to a lot of clients
in my role, and you know, when it comes to
factor investing, you know, I feel like the standard factors,
and you know, most people have a very good definition.
Speaker 2 (10:28):
While it might vary somewhat of these factors, right, things
like value, things like momentum, things like low risk. Right,
you might have a different look back for your for
your momentum factor per se, but we all understand what
momentum is. Quality seems to be the one where people
have different differing definitions. It typically is a profitability metric.
(10:48):
And then I've seen other things and you just mentioned
some of them. But like when you think about a
quality company, obviously you use profitability metrics like r O
E r O. I see other than other factors saying
within the quality bucket, like what other thing like do
you look at like things like stability of earnings or
cruels or anything like that when you're when you're kind
of defining your quality factor.
Speaker 3 (11:11):
Yeah, we do. I mean there's you know, there's about
five that go within the quality bucket. But you know,
as I try to emphasize, like our ro O E Tiff,
not just high quality stocks, like we're picking high quality
stocks that have you know, attractive valuations, that have good
dividend pain ability and that all over and then have
(11:33):
a little bit of growth the momentum too. But yeah,
I mean in the end though, within the quality bucket
it is skew towards ro O E, r O I,
C ro O A. But I think the point that
I'm trying to articulate and maybe I am, you know,
I need to do better job. But you know, if
you have a good return on equity of a good
return on asset, you know you are doing other things
(11:56):
well in the company, Like you're issuing dividends, you have
good free cash flow, you don't use a lot of
debt to equity. Your leverage ratios are you know, well
or well allocated. Let's say. So it does at times,
Chris like feel a little bit fuzzy because I think,
(12:17):
like values just okay, you're a stock that has a
broken story and you need to have something to come corrected.
The sis factor. You know, small cap stock you're just
you know, very small. You know, momentum is just you know,
relative strength, you know, very technical factor. So quality kind
of encapsulates a few other metrics beyond just you know,
(12:39):
the return on equity, return on assets. You know, some
index providers and some metail fishers will say, you know,
debt to equity is a big component of it. Within
you know, the quality definition. For us, it's not. We
tend to use more kind of traditional metrics like r
O A ro OE and r O C.
Speaker 2 (12:59):
Yeah, I totally agree with that. I mean, just based
on my research. You know, like you said, it's quality
is sometimes a fuzzy definition. But it seems very clear
to me that profitability is the most important part of quality.
And when you when you look at back tests of
all these different things or crurals or stability of earnings
or whatever, you know, the r O E, r O
I C r O A back tests just perform much better.
(13:21):
And I and you know, I think profitability is really
the backbone equality in many respects. So another question I
give from clients all the time, kind of leading you
to water here. But you know, some some clients that
might not be a sophisticated in fact or investing. They'll
say things to me like, well, isn't quality just growth?
Why don't you just use growth? Why don't why do
(13:41):
you use quality in addition to growth or quality, you know,
instead of growth? You know, what would you say that? Like,
would you say, and you know, would you say, quality
is like growth? How would you differentiate it? What would
you say to that question?
Speaker 3 (13:59):
So they're different. We happen to like combining both of them.
We have an ETF G Triple Q that's kind of
quality growth stocks. It's like fifty percent, you know, it's
like half quality half growth. Let's say I would say,
right now, we're in this kind of unusual period, Chris,
where a lot of growth companies tend to also be
(14:21):
in the quality bucket. It wasn't O is the case,
you know, but because tech stocks have produced such strong
returns and they have good you know, earnings revisions, they
have good growth estimates, good PEG ratios, they also happen
to have like strong ROE strong ro I C strong
ro A, So they're in both buckets. But like you know,
(14:44):
we use like these zis. We have like a white
paper we wrote for our ro O E TF and
we show them in the paper, like, Okay, what's historically
the sharp ratio the standard deviation for quality momentum involve
those size growth market divine il VALU and you see
quality as the highest sharp ratio and it's got a
(15:05):
pretty low standing deviation. So the standardiation and this has
gone back you know, thirty years in MSA and the
SEAS is about fourteen and a half. The growth factor
has about seventeen and a half stand deviation, so much
like almost like three standard deviation points higher. And they
both have similar kegers. So growth stocks just happen to
(15:26):
have a lower sharp ratio. But this is like thirty
years worth of data, right, But I would say technically
the answer is like they are different. You know, one
has traditionally like strong roa ROI. You know that's equality,
and the growth have you know, more like higher peg
ratios earnings momentum. It's just the only recently in the
(15:48):
last cycle where I would say there's a lot of
like mix. You know, stocks are in both quality ETFs
and the growth ETFs.
Speaker 2 (15:58):
Yeah, I found that same thing, and you know I've
also found that you know, quality is much better, sharp
ratio is much better risk adjuster returns, and like you know,
I've seen that quality slaze profitability. It's definitely not the
same as something like low risk, but it does overlap
with like low risk, low beta lovall, you know, you
typically get similar stocks in there, and that also has
(16:19):
very strong risk adjuster returns. So the next question I
wanted to ask you was something that's very interesting and
something that I again talk to clients all the time about,
and that is weighting your stocks right like right now.
Just because of recency bias, it seems like any non
market cap weight you know, it's it's lagging. You know,
(16:40):
customers come to me and say, why would you eagually
wait the stocks? Why would you have any other waiting
scheme other than market cap waiting, because market cap waiting
has worked so well very recently, and you know, I
know that when you go back far in time, that's
not the case. So I would love to know your
thought process behind why you weight your stocks how you do,
and maybe you know, you could dub to into maybe
(17:00):
some you know, concerns you could potentially have with the
market cap weighted indices how they're currently constructed.
Speaker 3 (17:07):
I saw my career in two thousand and it was
basically the Internet bubble erupted, and that was kind of
like the the you know, the impetus for like Wisden
Try got popular, Rob Barnett got popular, you know, AQR launch,
And it was because Marke cap weight indities were flawed
because you know, they had a lot of these tech companies.
(17:29):
Some of them were profitable, some of them were not profitable,
and really smart bait investment, I think became popular because
the tech bubble and you know, unwinding in two thousand
and one, two thousand and two, I feel like it's
the same movie again, although the difference is now is
that a lot of these companies are actually pretty profitable.
It's just that they're just gargantuan size. I would say
(17:51):
we whenever I've done quantitative like stock selection, you know
here at Astoria or my prior companies, like we always
generally equally weighted, and it's like a risk mitigation tool.
You know, the more way you give one stock, the
more you know risk you take in that one stock.
You know, obviously now that there's a big passive bubble,
(18:13):
a ETF bubble, and money keeps pouring into like spy,
vu IVV and it's like this massive self fulfilling prophecy
because you know, the cost sore low. It's you know,
two basis points, three basis points. Whatever it is, it works,
people buy it, more money comes in, they buy the
same stocks. So I would say now more than ever.
(18:36):
You know, it's important to tilt away. And we have
like these decision trees, like when we run our multi
asset ETF strategies, like what do you want to tilt
the way towards? So I'll go that real quick. So basically, okay,
like you tilted away from our cap. You can choose
eco weight. That's one you know decision tree. But you
have to be careful with equal weight because how are
(18:56):
you equally waiting it? And we could spend like five
minutes talking about that. Do you want to tell towards value? Okay?
Value is a factor. That's very tough. It's very fickle.
It works you know every ten years, maybe two three
of the ten years work. So that's tough. Small caps,
that's tough. You need a certain credit cycle, interest rate
cycle for small caps to work. Then you have international.
(19:19):
So the point is like, if you have concerns about
market weighted ind ses, people tend to lean on equal weight.
You have to be careful with equal weight. I think
eqal weight you know now has been tough, but like
over like twenty thirty years, Like you see the equal
weight tensaple form mark cap it works better out of
like a recession, so when you cut it up, you'll
(19:39):
see that. Like when when you're like in this like
economic slowdown, people buy more market cap weight, which I
think is kind of this current period that we're in now.
I sometimes hear people say, wow, small cap EQO weight's
no longer going to work because companies stay in private.
But I just think we're in this like really unusual
(20:00):
bubble of passive indexation ETF flows and mark cap is
just bid because just money keeps plowing into those products.
Speaker 2 (20:08):
Yep, yep. I found the exact same thing. So what
about like you know, and I typically stick to equal
weight as well. I feel like it gets you like
ninety five percent of the way there. But like sometimes
customers ask me, well, what about a more complicated scheme,
Like what about some kind of inverse polatility waiting like
Ray Dalio style, or like risk parity or maybe even
(20:29):
more like me invariance optimization type waiting scheme, Like do
you find any value in those overly complicated ones or
do you think that's kind of adding more degrees of
freedom to a model which is you know, generally bad.
Speaker 3 (20:44):
I mean to be honest, Like it gets such a
bad name, you know, smart beta. But this idea of
like weighting based on like quant metrics, like the higher
the quant rank in, the more way you give, Like
we have some strategies where we do that. That to
me makes sense and as like easier to explain if
you deal with institutional investors, totally get it, you know,
(21:05):
mean variance optimization inverse volatility, Like I think you can
do that and get away with it. But in the
financial advisor world that I live in, our world, like
equal weight is enough tracking error versus the benchmark that
and it's easy to explain. I think you just have
to be careful with equally weight and like when you
equally wait, you know, let's say the Russell one thousand
(21:27):
index now you're talking about each stock is like thirteen
bip weight. Let's say twelve bip weight. That's tough. If
you equal wait s and P five hundred each stock
is twenty BIPs. Wait, we just like the reason why
we launched o OEI was because we weren't comfortable with
SMP equal way to DTF because a, you know, there
was only fifteen percent technology exposure, so we wanted to
(21:50):
kind of optimize our ro oe E TF to match
the S and P sector weight. So and then we
pick only one hundred stocks. So now your marginal contribution
and to risk and return is a lot higher when
you once you equally weight one hundred stocks compared to
like five hundred or thousand. And the other real extreme
example is I put out a report called ten ETFs
(22:11):
for twenty twenty five, and I put in there this
xn tk ETF. It's an equal weighted tech ETF from
Spiders and it's actually been the cues over the last
three years. When we launched a report, and some people
were surprised, but you know, it's got thirty five stocks.
Each stock is three percent weight. And then it's not
just like they pick stocks that have like strong sales
(22:34):
and revenues, so it's a combination of like you know,
smart beta, and then the mechanism to weight is like
just a small concentrated portfolio thirty five stocks three percent weight,
so just have to be careful with equal weight. Even
though it's simplistic, it's easier to like talk to advisors
about that. It's like, how are you doing it and
how are you optimizing the sectors?
Speaker 1 (22:56):
So, actually you mentioned sectors, so I kind of want
to touch on that a little bit. You know, we've
talked about waiting stocks, but how do you determine overweighting
or underweighting different sectors? Are you looking at more macro
I guess component issues or so.
Speaker 3 (23:11):
In our quantitative stock selection SMAs and our ETFs, like
we do track the benchmark sector weight, so we optimize
against that. We want to have our stock selection provide
the alpha and this concept that when we're designing it,
you know, we're doing a multi factor you know, when
(23:32):
we when we're just doing like multi asset investment in
our ETF model portfolio business, you know, we will overweight
on the weight and that's more based on macro top
down research kind of like depending on you know, where
we are in the earning cycle, the credit cycle, the
inflation cycle. That's a much different game sort of say.
(23:53):
But when we're talking about like just quantitative investing, we'd
rather have our stock selection in our quont code, which
you know, for background, we have five CFAs on staff.
My colleague nixer Bone kind of oversees the quant side
of it, and Ponkach Patel is kind of head of
quant data science. I mean collectively these you know, the
(24:13):
five CFAs, and we've got a lot of years experience
building quant fulfillis like, we know that code is very powerful,
but we just stay diligent. We stick to our process.
We do have risk management risk management capabilities within that
which I can talk about, but I think when it
comes to like quantitative systematic investment, it's better to be
(24:35):
like rules based, diligent and stick with the process.
Speaker 2 (24:38):
You know, one of the things we do at BI
that gets a lot of traction is monitoring the valuations
of the factors. So we typically do is we'll look
at meeting valuations of a long and short side of
the factors, compare them to each other, right, and that
would say like is a long and short factor cheap
or expensive? Now I know that you said that your
value is part of your overall process, but just thinking
(25:00):
about quality in and of itself or really any other factor.
To be honest, is there every scenario where like a
certain factor gets too expensive historically, where you might decrease
the weight to it. Like one of the things we've
noticed recently is that profitability slash quality is very expensive.
You can compare it to the market, you can compare
it to low quality stocks. The ratio is very high
(25:23):
historically when this stuff is very bit up. Would that
have any influence on your the weighting of your factors?
Speaker 3 (25:30):
It would, And that's where the power of being active
and passive together and being kind of rules based, I think,
is the benefit. So what I would say to you
is like, Okay, right now, we've thought about this. In
our quality to Froe, quality fact is only fifty percent, right,
so again we've got twenty percent in like dividends, twenty
(25:53):
percent in valuation. We would say if we got even
more expensive, we would say, okay, take down the quality
factor attribution in the ranking process down until it's say
forty percent, because that you know, at the end of
the day, like we do want to buy below intrinsic value.
Way for it to achieve intrinsic value. Our ETF has
like a seventeen p ratio versus like twenty two for SPY,
(26:17):
So I feel like, you know, it's like quality at
a reasonable price. But yeah, I think that's the flexibility
that you can provide once you buy an active ETF
or use an active manager. You know, if you just
going out there buying like the quality TF qual, you
know you're going to be stuck with high quality stocks,
let's say, especially if it's MARKAP weighted, which that is
(26:39):
you know for us, the minute you tilt away from
mar Cap, you're going to get something that looks at
a discount to the market. So you know, I would
say the other main point to make here, Chris, is
like each stock fights with one another in the quant
ranking code, right, so if a stock got too expensive,
you know eventually it's ranking would fall down. When we
pick one hundred stocks in row, each stock fights one another,
(27:02):
but we are picking top DEA SALSASL one, DEASTL two
amongst you know, ten descals amongst twenty different valuation metrics.
So if something gets very very expensive within the code,
it'll de rank by itself.
Speaker 1 (27:15):
Sort of saying I wanted to ask about selling positions.
So I understand it's a quantitative portfolio, and you know,
I know you're ranking the stocks. Is this done on
a periodic basis or is there anything that would kind
of trigger a cell, you know, in terms of rankings outside.
Speaker 3 (27:31):
Of that, So good question, I would say in anytime
you equally wait, I think it's really important that you
quarterly share change rebellance. Like we had this one stock
sm CI that you know went up you know, eight
hundred percent, So it's you know, each stock in our
basket starts at one percent weight and then does drift
(27:55):
up or down. So this then got up to like
a three percent weight, and then you know, and then
quarterly share a balance it you know, got trimmed down
to like one percent. Our code is longer term in nature,
so it's not going to pick up like accounting fraud.
Let's say, so in the case of like app love
and let's say when we launched our g triple QTF,
it was like October first last year. Like in the
(28:17):
case of like a Nazak one hundred, where like an
annual rebellance, like you know, Nazek added app lovin in
the December rebalance. I mean it was up like a
thousand percent when they added it. You know, these are
things that you can look for and be you know
a little bit more strategic when you're active, let's say,
but again, like it's not going to pick up like
(28:39):
an accountant fraud story. So like if we had an
instance where you know, a stock fell twenty percent, you know, overnight,
you know, we would then do like the human bottoms
up research and do like stock selection on that one
stock to say, okay, you know, does this stock need
to be kicked out? And I think that's a benefit
of active. The extreme example I'll give you is like
(29:02):
ten fifteen years ago, there was a stock in the
FXI ETIF that like just stopped trading because of like
a fraudulent thing, and it stayed in the FXIEYTF for
like six months right before it's kicked out. So, like,
you know, there's benefits of doing both right, being rules based,
which is more passive, and then being you know, using
(29:23):
like an human oversight and kind of be inactive. But
outside of these very episodic periods, we generally don't have
a lot of like intra quarter cell signals. Let's say,
for ro O ETF. It is like an annual reconstitution process,
but you know, every quarter we bring the shared change
you know, to share way back to one percent and
(29:45):
then we look at the quon code to see if
anything materially deranked, and if it did materially de rank,
like you know, a one to two decile became like
an eight, you know, then we would like investigate further.
So that is kind of like a risk management oversight.
Speaker 2 (30:00):
Yeah, I'm just going to piggyback off that with the
risk management. Like I know, I think about risk management
is a couple of layers, right, It's like diversification is
one layer and moving to equally wait does a lot
for risk management in and of itself. And then you
have individual position management like do you stop yourself out
of positions which you kind of just spoke about. And
then there's like I would call it like overall tactical
(30:23):
risk management, Like, so, is there anything like that, like
whether everybody a scenario where you think the whole market's
going down or went below is two day moving average
or something like that where you would like de risk
the whole portfolio. Is there any kind of tactical overall
signals like that or No?
Speaker 3 (30:41):
Not in our SMAs and our ETF. So I mean
in our other business multi asset you know, ETF investment,
Like we do have the ability to kind of be
much more flexible, which I can speak to. I would say,
I just don't believe you can kind of time the market.
So even if we were to de risk the portfolio,
it would be like US trimming like expensive US docs
(31:03):
to buy more value and or international and they're using
like more liquid olds. But like we wouldn't go more
than you know, let's say five ten percent versus like
its benchmark, so we kind of have like guardrails in place.
I think that the thing I would tell listener here
is like, if you have a high conviction view, like
(31:25):
you want to really de risk your portfolio, you know,
you really have to have that kind of second line
of thinking that like Howard Marx talks about, like what
do you know that's not already in the price. And
you know, the one instance where we really had this
huge high conviction idea was like during the inflation scare
in twenty twenty two, and that was just us you know,
(31:46):
thinking about economics one O one, the M two velocity money,
and we really did nail the inflation call, and we
had some pretty significant performance versus benchmark, you know, past
performance on it take a future result. But I think
the last two years since that inflation scare, we've been
mined in this like market cap you know, weighted bubble
(32:08):
passive seven stocks driving most of the SMP. Not really
sure what's going on with you know, the market from
a policy standpoint, so you know, we don't make any
of those bets now, we just haven't had that view.
And then the only other point to really drive home
is like, Okay, why we're active is like, and I
do have high commction in this is that my first
(32:32):
job was like derivative research like forecasts and ads and
deletes to like the S and P and next to
Russell and next And I mean I personally have a
lot of friends that work with these hedgrophones at arbitrage
in next flow. So I mean, let's say, you know,
the cash Cow's ETF has thirty billion dollars. It's index space.
(32:54):
You can basically know what stock's going to be added
or deleted, and you know, you can like pre position yourself.
I felt strongly that ANYTF that I was gonna put
my name next to would not be passive, just because
I wouldn't want the street to front run my order
flow to like you know, arbitrage net asset value points
off DTF, So what are what are I tf's like,
(33:18):
we just don't rebalance on the day of you know,
triple Witch, and let's say it's kind of like done
you know, before or afterwards, and certainly could be intro
day too.
Speaker 1 (33:28):
So we have one more question. And actually it's funny
you mentioned Howard Marks because my question was on books.
I know his most important thing. Book is one of
your favorites, might be your all time favorite. But it's
curious what other, you know, favorite financial books.
Speaker 3 (33:42):
You have, So I I definitely think, you know, Larry
Sueddrow has written a lot of great books and and
there's a very important concept that we definitely subscribe to,
which is this idea that, like you know, when you
combine in fact in a portfolio, you can get higher
by in the first fal frontier. So and he's got
(34:04):
research that goes back, you know, seventy years, fifty years.
So basically he says, okay, in this one table, if
you do a twenty five percent allocation towards Debta factor,
the size factor, the value factor, and the momentum factor,
the historical sharp ratios like point seventy four, and that's
basically double any of the individual factor bets on either
(34:27):
beta value size probably you know, Okay, So then he says, okay,
instead of doing twenty five percent, do like twenty percent
to each of those four factors. And then you add
profitability as a fifth one, and you get a point
nine to six sharp ratio. And then in the next
iteration he's substitute quality for profitability, and now you got
(34:47):
like a one point one sharp ratio. You know, when
you combine you know, five factors, you had profitability, you
sub out profitability for quality. You know, you're talking about
almost triple the sharp ratio of any individual leg beta
value size. And then you know, like if you he
(35:09):
runs someone elsis on, like what's the underperformance odds over
like a one year period, of three year period, five, ten,
twenty year and the odds of you on the perform
and are dramatically lower when you combine factors. And so
his book, you know, like everything you want to know
about fact investing. I maybe miss a title in the book,
(35:30):
but that book is kind of seminal to us. You know,
I'm a big fan of buffet. He just releases you know,
newsletter this this weekend, and you know, kind of what
he preaches, like buying quality businesses but just at a
low price. I think that's really important to how we
think about investing. So those are some of the top
(35:51):
two or three that I would say resonate with me.
Speaker 1 (35:55):
Well, this is great, John, thank you again for joining
us today.
Speaker 3 (35:59):
Next, thanks guys and Chris, thank you for.
Speaker 1 (36:02):
Being my co host today. Thank you until our next episode.
This is David Cohne 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 security selection. I'm David Cohne,
I lead mutual fund and active research at Bloomberg Intelligence.
Today my co host is Christopher kine, Us, quantitative strategist
at Bloomberg Intelligence. Chris, thank you for joining me today.
Speaker 2 (00:36):
Thank you so much for having me. David.
Speaker 1 (00:38):
So, you wrote a recent note about adding profitability to
value stocks or the profitability factor. Since our conversation today
will be focused on quality, I wonder if you could
give us a sense of how adding that profitability factor
to a standalone value strategy performs.
Speaker 2 (00:56):
Yeah. I'm a big fan of adding profitability to value.
I mean just in empirically, you know, it seems to
work better and it's very logical. So you know, just
as a as a review, you know, value was the
only factor that underperformed in twenty twenty four by our measurement.
If you look at the long short factor, it was
the only one that's down on the year, And if
(01:16):
you only look at the long only legs, it was
the only one that underperform the market. So it so
just Q one value. Just cheap stocks returned about ten
percent last year in the Russell one thousand as compared
to about thirteen or so percent return for the equal
weighted a Russell one thousand. If you added some profitability
to that, that increased the returns to about fifteen to
(01:37):
sixteen percent. So you know it, I'll perform a market
if you just had that profitability component. To me, you know,
it's very logical because you know, sometimes value stocks are
valued for a reason. You know they have, you know,
not good prospects and little chance for a turnaround. If
you add a profitability filter to your value factor, you
kind of filter out those value traps or you know,
(02:00):
cheap companies that are cheap for a good reason. And
that seems to work good both in the short term
and the long term.
Speaker 1 (02:06):
That makes sense. So I think we'd like to introduce
someone who's actually no stranger to quality, John Davey. John
is founder, CEO, and CIO of Astoria Portfolio Advisors. John,
thank you so much for joining us today.
Speaker 3 (02:20):
Great great to be here, guys, thank you for having
me on.
Speaker 1 (02:23):
So before we get into talking about your investment process,
Can you tell us more about your start in the
investment industry and what led you to start Astoria.
Speaker 3 (02:32):
Sure. So I began as a quantitative derivative analyst and
Merrill Lynch's derivative research group in the late nineties. So
it was an intern you know, basically spent my first
ten years kind of doing index level research. Quant research,
always at the index level, so that included like index
(02:54):
futures and next options ETFs. You know, got popular in
the late nineties and so ets were underneath our research group.
So then did that for ten years, and then I
became head of Morgan Stanley's ETF content for institutional clients.
And you know, I had always liked macro research, asset allocation,
(03:17):
and you know, always wanted to join the buy side
and be an entrepreneur. So I felt, you know, here
is a way to kind of do both kill two
birds one stone, kind of joined the buyside and also
to you know, be an entrepreneur.
Speaker 1 (03:29):
So let's talk a little more about Astoria. How would
you describe the Astoria investment philosophy.
Speaker 3 (03:35):
Yeah, so we I would say it's it's a combination
of there's kind of two verticals, like two business lines.
One is like quantitative research, and the expression could be
like an SMA, a quant SMA could be an ETF.
And then the other business line is kind of multi
asset ETF investing kind of asset allocation. So really I
(04:01):
would say it's it's combining active and passive. That's kind
of what we do, you know, at our firm. I
would say our ETFs are systematically active. It's very rules based.
Implementation is active like when we actually rebalance and how
we trade, and I can spend a lot of time
(04:23):
talking about that, but it's very kind of rules based,
quantitative in nature. Our asset allocation is you know, I
would say the idea there is that we want to
use the business cycle, use earning valuations, use sentiment in
order to kind of dictate whether or not we want
to be ovoid on the way in NASA class, and
(04:44):
then we use a tiny bit of liquid olts in
order to kind of hedge our left tail risk. Once
you hedge your left tail risk, which I know we're
in a bull market, but we've had many crisises the
last you know, seven eight years, so that ability to
kind of of hedge left tail risk, I think is
quite important and you know, just use like a little
(05:05):
bit of liquid alts in order to kind of hedge that.
So that's kind of like our two business lines. One
is quant sma's ETFs, the other is multi ASCID ETF
model portfolios.
Speaker 1 (05:16):
Great. So if we want to, you know, focus in
on the ETF specifically OROE the astoria US equal Weight
Quality Kings ETF, is there a process you follow to
narrow down a universe and then select the stocks for
the portfolio.
Speaker 3 (05:31):
Absolutely, so you know the idea there is that, you know,
and for background, you know, we all worried about the
concentration risk and the ND the CES. You know, ten
stocks make up forty percent of the S and P
five hundred and we launched are we back in August
first of twenty twenty three, So and you know it
takes like, you know, three six months to let's say,
(05:53):
launch an ETF. So we were worried about concentration risk
for a while, you know, and we looked at the
available options out there and we just weren't quite comfortable.
And I can spend some time talking about what makes
our equo weight approach different, but essentially it's like a funnel, right,
we start with like ten thousand stocks, we filter out,
(06:14):
set out, we picked you know, large liquid you know,
minimum free float, minimum market cap weights. Then we come
up with like eight hundred investible stocks. And the idea
with those eight hundred stocks is if we want to
pick the hundred of you know, let's say the best
in kind of that eight hundred, And what we do is,
(06:36):
you know, we look at five different factors, so things
like quality, valuation, growth, momentum, and dividend. And we want
to so we'll weight our etf So fifty percent of
the quant code is allocated to the quality factor, twenty
percent to the dividend factor, twenty percent to the valuation factor,
(06:59):
five percent to growth factor, and five percent to the
momentum factor. And essentially these winds up being like high
quality companies that are reasonably priced. You know, within each
one of those five factors, there's like three, four or
five fundamental ratios that express the factor of view. So
(07:19):
in the case of like quality, like we're looking at
companies with ro OE r O A, r O I C,
dividing could be you know, the sustainability the dividend, the
dispersion of the dividend. Valuations could be like, you know,
things like PE price, the sales. The growth factor could
be things like PEG ratios. The momentum factor could be
(07:42):
like relative strength indicators and our premise. And you asked
me before, David, like, what do we fundamentally believe in?
You know, we definitely think that the more factors you
can harvest in a portfolio, as long as you harvest
in a low cost manner and as long as you
stick with the factors like that historically has been able
(08:02):
to give you higher risk adjuster returns and kind of
put you higher up on the fishing frontier. So to summarize,
you know, start with like ten thousand investable stocks, filter
down to eight hundred of the ones that are the largest,
most liquid, you know, good free float, and then pick
a hundred of that eight hundred that have you know,
strong roe, strong row, you know, strong quality metrics, that
(08:26):
paid dividends, that are reasonably priced, that have a little
bit of growth and momentum characteristics. It's great.
Speaker 1 (08:33):
So let's focus in on quality for a second. What
is your research shown about the long term value of
quality stocks.
Speaker 3 (08:40):
Well, I liked what Chris said before. I mean, you know,
valuing in beta or valuant quality you know definitely have
very very low negative you know, very low correlations, if
not negative. Right. So if again, if you believe in
this concept of like how do I get higher? But
on the fish frontier, how do I mix factors? You know,
you're picking a factor quality that you know has pretty
(09:04):
robust long term characteristics, and especially if you pair that
with like value or you know, you know small cap
stocks that that looks very very attractive to us for
us at the end of the day, Like you know,
quality are companies that you know are robust, they pay dividends,
they have good earnings, good ROI. You know ro O,
(09:26):
I c roa Roe. And you know it's it's this
concept of like it's persistent, pervasive, robust. It works across sectors,
across countries, across economic cycles, and you know there is
historical out performance.
Speaker 2 (09:42):
Right.
Speaker 3 (09:42):
So if you do some farm of French you know analytics,
you'll see that high quality stocks have beaten low quality
stocks and being the market you know, over the last
you know, half a century, if not longer. I think
their data set goes back to like nineteen sixty three.
So that's what we're looking for. I mean, these could
be you know companies that Microsoft, Google, you know Facebook,
(10:06):
I mean a lot of them tend to be growth now,
but you know it's brand hold names essentially. So that's
that's how we think about it, you.
Speaker 4 (10:17):
Know, John, I I talked to a lot of clients
in my role, and you know, when it comes to
factor investing, you know, I feel like the standard factors,
and you know, most people have a very good definition.
Speaker 2 (10:28):
While it might vary somewhat of these factors, right, things
like value, things like momentum, things like low risk. Right,
you might have a different look back for your for
your momentum factor per se, but we all understand what
momentum is. Quality seems to be the one where people
have different differing definitions. It typically is a profitability metric.
(10:48):
And then I've seen other things and you just mentioned
some of them. But like when you think about a
quality company, obviously you use profitability metrics like r O
E r O. I see other than other factors saying
within the quality bucket, like what other thing like do
you look at like things like stability of earnings or
cruels or anything like that when you're when you're kind
of defining your quality factor.
Speaker 3 (11:11):
Yeah, we do. I mean there's you know, there's about
five that go within the quality bucket. But you know,
as I try to emphasize, like our ro O E Tiff,
not just high quality stocks, like we're picking high quality
stocks that have you know, attractive valuations, that have good
dividend pain ability and that all over and then have
(11:33):
a little bit of growth the momentum too. But yeah,
I mean in the end though, within the quality bucket
it is skew towards ro O E, r O I,
C ro O A. But I think the point that
I'm trying to articulate and maybe I am, you know,
I need to do better job. But you know, if
you have a good return on equity of a good
return on asset, you know you are doing other things
(11:56):
well in the company, Like you're issuing dividends, you have
good free cash flow, you don't use a lot of
debt to equity. Your leverage ratios are you know, well
or well allocated. Let's say. So it does at times,
Chris like feel a little bit fuzzy because I think,
(12:17):
like values just okay, you're a stock that has a
broken story and you need to have something to come corrected.
The sis factor. You know, small cap stock you're just
you know, very small. You know, momentum is just you know,
relative strength, you know, very technical factor. So quality kind
of encapsulates a few other metrics beyond just you know,
(12:39):
the return on equity, return on assets. You know, some
index providers and some metail fishers will say, you know,
debt to equity is a big component of it. Within
you know, the quality definition. For us, it's not. We
tend to use more kind of traditional metrics like r
O A ro OE and r O C.
Speaker 2 (12:59):
Yeah, I totally agree with that. I mean, just based
on my research. You know, like you said, it's quality
is sometimes a fuzzy definition. But it seems very clear
to me that profitability is the most important part of quality.
And when you when you look at back tests of
all these different things or crurals or stability of earnings
or whatever, you know, the r O E, r O
I C r O A back tests just perform much better.
(13:21):
And I and you know, I think profitability is really
the backbone equality in many respects. So another question I
give from clients all the time, kind of leading you
to water here. But you know, some some clients that
might not be a sophisticated in fact or investing. They'll
say things to me like, well, isn't quality just growth?
Why don't you just use growth? Why don't why do
(13:41):
you use quality in addition to growth or quality, you know,
instead of growth? You know, what would you say that? Like,
would you say, and you know, would you say, quality
is like growth? How would you differentiate it? What would
you say to that question?
Speaker 3 (13:59):
So they're different. We happen to like combining both of them.
We have an ETF G Triple Q that's kind of
quality growth stocks. It's like fifty percent, you know, it's
like half quality half growth. Let's say I would say,
right now, we're in this kind of unusual period, Chris,
where a lot of growth companies tend to also be
(14:21):
in the quality bucket. It wasn't O is the case,
you know, but because tech stocks have produced such strong
returns and they have good you know, earnings revisions, they
have good growth estimates, good PEG ratios, they also happen
to have like strong ROE strong ro I C strong
ro A, So they're in both buckets. But like you know,
(14:44):
we use like these zis. We have like a white
paper we wrote for our ro O E TF and
we show them in the paper, like, Okay, what's historically
the sharp ratio the standard deviation for quality momentum involve
those size growth market divine il VALU and you see
quality as the highest sharp ratio and it's got a
(15:05):
pretty low standing deviation. So the standardiation and this has
gone back you know, thirty years in MSA and the
SEAS is about fourteen and a half. The growth factor
has about seventeen and a half stand deviation, so much
like almost like three standard deviation points higher. And they
both have similar kegers. So growth stocks just happen to
(15:26):
have a lower sharp ratio. But this is like thirty
years worth of data, right, But I would say technically
the answer is like they are different. You know, one
has traditionally like strong roa ROI. You know that's equality,
and the growth have you know, more like higher peg
ratios earnings momentum. It's just the only recently in the
(15:48):
last cycle where I would say there's a lot of
like mix. You know, stocks are in both quality ETFs
and the growth ETFs.
Speaker 2 (15:58):
Yeah, I found that same thing, and you know I've
also found that you know, quality is much better, sharp
ratio is much better risk adjuster returns, and like you know,
I've seen that quality slaze profitability. It's definitely not the
same as something like low risk, but it does overlap
with like low risk, low beta lovall, you know, you
typically get similar stocks in there, and that also has
(16:19):
very strong risk adjuster returns. So the next question I
wanted to ask you was something that's very interesting and
something that I again talk to clients all the time about,
and that is weighting your stocks right like right now.
Just because of recency bias, it seems like any non
market cap weight you know, it's it's lagging. You know,
(16:40):
customers come to me and say, why would you eagually
wait the stocks? Why would you have any other waiting
scheme other than market cap waiting, because market cap waiting
has worked so well very recently, and you know, I
know that when you go back far in time, that's
not the case. So I would love to know your
thought process behind why you weight your stocks how you do,
and maybe you know, you could dub to into maybe
(17:00):
some you know, concerns you could potentially have with the
market cap weighted indices how they're currently constructed.
Speaker 3 (17:07):
I saw my career in two thousand and it was
basically the Internet bubble erupted, and that was kind of
like the the you know, the impetus for like Wisden
Try got popular, Rob Barnett got popular, you know, AQR launch,
And it was because Marke cap weight indities were flawed
because you know, they had a lot of these tech companies.
(17:29):
Some of them were profitable, some of them were not profitable,
and really smart bait investment, I think became popular because
the tech bubble and you know, unwinding in two thousand
and one, two thousand and two, I feel like it's
the same movie again, although the difference is now is
that a lot of these companies are actually pretty profitable.
It's just that they're just gargantuan size. I would say
(17:51):
we whenever I've done quantitative like stock selection, you know
here at Astoria or my prior companies, like we always
generally equally weighted, and it's like a risk mitigation tool.
You know, the more way you give one stock, the
more you know risk you take in that one stock.
You know, obviously now that there's a big passive bubble,
(18:13):
a ETF bubble, and money keeps pouring into like spy,
vu IVV and it's like this massive self fulfilling prophecy
because you know, the cost sore low. It's you know,
two basis points, three basis points. Whatever it is, it works,
people buy it, more money comes in, they buy the
same stocks. So I would say now more than ever.
(18:36):
You know, it's important to tilt away. And we have
like these decision trees, like when we run our multi
asset ETF strategies, like what do you want to tilt
the way towards? So I'll go that real quick. So basically, okay,
like you tilted away from our cap. You can choose
eco weight. That's one you know decision tree. But you
have to be careful with equal weight because how are
(18:56):
you equally waiting it? And we could spend like five
minutes talking about that. Do you want to tell towards value? Okay?
Value is a factor. That's very tough. It's very fickle.
It works you know every ten years, maybe two three
of the ten years work. So that's tough. Small caps,
that's tough. You need a certain credit cycle, interest rate
cycle for small caps to work. Then you have international.
(19:19):
So the point is like, if you have concerns about
market weighted ind ses, people tend to lean on equal weight.
You have to be careful with equal weight. I think
eqal weight you know now has been tough, but like
over like twenty thirty years, Like you see the equal
weight tensaple form mark cap it works better out of
like a recession, so when you cut it up, you'll
(19:39):
see that. Like when when you're like in this like
economic slowdown, people buy more market cap weight, which I
think is kind of this current period that we're in now.
I sometimes hear people say, wow, small cap EQO weight's
no longer going to work because companies stay in private.
But I just think we're in this like really unusual
(20:00):
bubble of passive indexation ETF flows and mark cap is
just bid because just money keeps plowing into those products.
Speaker 2 (20:08):
Yep, yep. I found the exact same thing. So what
about like you know, and I typically stick to equal
weight as well. I feel like it gets you like
ninety five percent of the way there. But like sometimes
customers ask me, well, what about a more complicated scheme,
Like what about some kind of inverse polatility waiting like
Ray Dalio style, or like risk parity or maybe even
(20:29):
more like me invariance optimization type waiting scheme, Like do
you find any value in those overly complicated ones or
do you think that's kind of adding more degrees of
freedom to a model which is you know, generally bad.
Speaker 3 (20:44):
I mean to be honest, Like it gets such a
bad name, you know, smart beta. But this idea of
like weighting based on like quant metrics, like the higher
the quant rank in, the more way you give, Like
we have some strategies where we do that. That to
me makes sense and as like easier to explain if
you deal with institutional investors, totally get it, you know,
(21:05):
mean variance optimization inverse volatility, Like I think you can
do that and get away with it. But in the
financial advisor world that I live in, our world, like
equal weight is enough tracking error versus the benchmark that
and it's easy to explain. I think you just have
to be careful with equally weight and like when you
equally wait, you know, let's say the Russell one thousand
(21:27):
index now you're talking about each stock is like thirteen
bip weight. Let's say twelve bip weight. That's tough. If
you equal wait s and P five hundred each stock
is twenty BIPs. Wait, we just like the reason why
we launched o OEI was because we weren't comfortable with
SMP equal way to DTF because a, you know, there
was only fifteen percent technology exposure, so we wanted to
(21:50):
kind of optimize our ro oe E TF to match
the S and P sector weight. So and then we
pick only one hundred stocks. So now your marginal contribution
and to risk and return is a lot higher when
you once you equally weight one hundred stocks compared to
like five hundred or thousand. And the other real extreme
example is I put out a report called ten ETFs
(22:11):
for twenty twenty five, and I put in there this
xn tk ETF. It's an equal weighted tech ETF from
Spiders and it's actually been the cues over the last
three years. When we launched a report, and some people
were surprised, but you know, it's got thirty five stocks.
Each stock is three percent weight. And then it's not
just like they pick stocks that have like strong sales
(22:34):
and revenues, so it's a combination of like you know,
smart beta, and then the mechanism to weight is like
just a small concentrated portfolio thirty five stocks three percent weight,
so just have to be careful with equal weight. Even
though it's simplistic, it's easier to like talk to advisors
about that. It's like, how are you doing it and
how are you optimizing the sectors?
Speaker 1 (22:56):
So, actually you mentioned sectors, so I kind of want
to touch on that a little bit. You know, we've
talked about waiting stocks, but how do you determine overweighting
or underweighting different sectors? Are you looking at more macro
I guess component issues or so.
Speaker 3 (23:11):
In our quantitative stock selection SMAs and our ETFs, like
we do track the benchmark sector weight, so we optimize
against that. We want to have our stock selection provide
the alpha and this concept that when we're designing it,
you know, we're doing a multi factor you know, when
(23:32):
we when we're just doing like multi asset investment in
our ETF model portfolio business, you know, we will overweight
on the weight and that's more based on macro top
down research kind of like depending on you know, where
we are in the earning cycle, the credit cycle, the
inflation cycle. That's a much different game sort of say.
(23:53):
But when we're talking about like just quantitative investing, we'd
rather have our stock selection in our quont code, which
you know, for background, we have five CFAs on staff.
My colleague nixer Bone kind of oversees the quant side
of it, and Ponkach Patel is kind of head of
quant data science. I mean collectively these you know, the
(24:13):
five CFAs, and we've got a lot of years experience
building quant fulfillis like, we know that code is very powerful,
but we just stay diligent. We stick to our process.
We do have risk management risk management capabilities within that
which I can talk about, but I think when it
comes to like quantitative systematic investment, it's better to be
(24:35):
like rules based, diligent and stick with the process.
Speaker 2 (24:38):
You know, one of the things we do at BI
that gets a lot of traction is monitoring the valuations
of the factors. So we typically do is we'll look
at meeting valuations of a long and short side of
the factors, compare them to each other, right, and that
would say like is a long and short factor cheap
or expensive? Now I know that you said that your
value is part of your overall process, but just thinking
(25:00):
about quality in and of itself or really any other factor.
To be honest, is there every scenario where like a
certain factor gets too expensive historically, where you might decrease
the weight to it. Like one of the things we've
noticed recently is that profitability slash quality is very expensive.
You can compare it to the market, you can compare
it to low quality stocks. The ratio is very high
(25:23):
historically when this stuff is very bit up. Would that
have any influence on your the weighting of your factors?
Speaker 3 (25:30):
It would, And that's where the power of being active
and passive together and being kind of rules based, I think,
is the benefit. So what I would say to you
is like, Okay, right now, we've thought about this. In
our quality to Froe, quality fact is only fifty percent, right,
so again we've got twenty percent in like dividends, twenty
(25:53):
percent in valuation. We would say if we got even
more expensive, we would say, okay, take down the quality
factor attribution in the ranking process down until it's say
forty percent, because that you know, at the end of
the day, like we do want to buy below intrinsic value.
Way for it to achieve intrinsic value. Our ETF has
like a seventeen p ratio versus like twenty two for SPY,
(26:17):
So I feel like, you know, it's like quality at
a reasonable price. But yeah, I think that's the flexibility
that you can provide once you buy an active ETF
or use an active manager. You know, if you just
going out there buying like the quality TF qual, you
know you're going to be stuck with high quality stocks,
let's say, especially if it's MARKAP weighted, which that is
(26:39):
you know for us, the minute you tilt away from
mar Cap, you're going to get something that looks at
a discount to the market. So you know, I would
say the other main point to make here, Chris, is
like each stock fights with one another in the quant
ranking code, right, so if a stock got too expensive,
you know eventually it's ranking would fall down. When we
pick one hundred stocks in row, each stock fights one another,
(27:02):
but we are picking top DEA SALSASL one, DEASTL two
amongst you know, ten descals amongst twenty different valuation metrics.
So if something gets very very expensive within the code,
it'll de rank by itself.
Speaker 1 (27:15):
Sort of saying I wanted to ask about selling positions.
So I understand it's a quantitative portfolio, and you know,
I know you're ranking the stocks. Is this done on
a periodic basis or is there anything that would kind
of trigger a cell, you know, in terms of rankings outside.
Speaker 3 (27:31):
Of that, So good question, I would say in anytime
you equally wait, I think it's really important that you
quarterly share change rebellance. Like we had this one stock
sm CI that you know went up you know, eight
hundred percent, So it's you know, each stock in our
basket starts at one percent weight and then does drift
(27:55):
up or down. So this then got up to like
a three percent weight, and then you know, and then
quarterly share a balance it you know, got trimmed down
to like one percent. Our code is longer term in nature,
so it's not going to pick up like accounting fraud.
Let's say, so in the case of like app love
and let's say when we launched our g triple QTF,
it was like October first last year. Like in the
(28:17):
case of like a Nazak one hundred, where like an
annual rebellance, like you know, Nazek added app lovin in
the December rebalance. I mean it was up like a
thousand percent when they added it. You know, these are
things that you can look for and be you know
a little bit more strategic when you're active, let's say,
but again, like it's not going to pick up like
(28:39):
an accountant fraud story. So like if we had an
instance where you know, a stock fell twenty percent, you know, overnight,
you know, we would then do like the human bottoms
up research and do like stock selection on that one
stock to say, okay, you know, does this stock need
to be kicked out? And I think that's a benefit
of active. The extreme example I'll give you is like
(29:02):
ten fifteen years ago, there was a stock in the
FXI ETIF that like just stopped trading because of like
a fraudulent thing, and it stayed in the FXIEYTF for
like six months right before it's kicked out. So, like,
you know, there's benefits of doing both right, being rules based,
which is more passive, and then being you know, using
(29:23):
like an human oversight and kind of be inactive. But
outside of these very episodic periods, we generally don't have
a lot of like intra quarter cell signals. Let's say,
for ro O ETF. It is like an annual reconstitution process,
but you know, every quarter we bring the shared change
you know, to share way back to one percent and
(29:45):
then we look at the quon code to see if
anything materially deranked, and if it did materially de rank,
like you know, a one to two decile became like
an eight, you know, then we would like investigate further.
So that is kind of like a risk management oversight.
Speaker 2 (30:00):
Yeah, I'm just going to piggyback off that with the
risk management. Like I know, I think about risk management
is a couple of layers, right, It's like diversification is
one layer and moving to equally wait does a lot
for risk management in and of itself. And then you
have individual position management like do you stop yourself out
of positions which you kind of just spoke about. And
then there's like I would call it like overall tactical
(30:23):
risk management, Like, so, is there anything like that, like
whether everybody a scenario where you think the whole market's
going down or went below is two day moving average
or something like that where you would like de risk
the whole portfolio. Is there any kind of tactical overall
signals like that or No?
Speaker 3 (30:41):
Not in our SMAs and our ETF. So I mean
in our other business multi asset you know, ETF investment,
Like we do have the ability to kind of be
much more flexible, which I can speak to. I would say,
I just don't believe you can kind of time the market.
So even if we were to de risk the portfolio,
it would be like US trimming like expensive US docs
(31:03):
to buy more value and or international and they're using
like more liquid olds. But like we wouldn't go more
than you know, let's say five ten percent versus like
its benchmark, so we kind of have like guardrails in place.
I think that the thing I would tell listener here
is like, if you have a high conviction view, like
(31:25):
you want to really de risk your portfolio, you know,
you really have to have that kind of second line
of thinking that like Howard Marx talks about, like what
do you know that's not already in the price. And
you know, the one instance where we really had this
huge high conviction idea was like during the inflation scare
in twenty twenty two, and that was just us you know,
(31:46):
thinking about economics one O one, the M two velocity money,
and we really did nail the inflation call, and we
had some pretty significant performance versus benchmark, you know, past
performance on it take a future result. But I think
the last two years since that inflation scare, we've been
mined in this like market cap you know, weighted bubble
(32:08):
passive seven stocks driving most of the SMP. Not really
sure what's going on with you know, the market from
a policy standpoint, so you know, we don't make any
of those bets now, we just haven't had that view.
And then the only other point to really drive home
is like, Okay, why we're active is like, and I
do have high commction in this is that my first
(32:32):
job was like derivative research like forecasts and ads and
deletes to like the S and P and next to
Russell and next And I mean I personally have a
lot of friends that work with these hedgrophones at arbitrage
in next flow. So I mean, let's say, you know,
the cash Cow's ETF has thirty billion dollars. It's index space.
(32:54):
You can basically know what stock's going to be added
or deleted, and you know, you can like pre position yourself.
I felt strongly that ANYTF that I was gonna put
my name next to would not be passive, just because
I wouldn't want the street to front run my order
flow to like you know, arbitrage net asset value points
off DTF, So what are what are I tf's like,
(33:18):
we just don't rebalance on the day of you know,
triple Witch, and let's say it's kind of like done
you know, before or afterwards, and certainly could be intro
day too.
Speaker 1 (33:28):
So we have one more question. And actually it's funny
you mentioned Howard Marks because my question was on books.
I know his most important thing. Book is one of
your favorites, might be your all time favorite. But it's
curious what other, you know, favorite financial books.
Speaker 3 (33:42):
You have, So I I definitely think, you know, Larry
Sueddrow has written a lot of great books and and
there's a very important concept that we definitely subscribe to,
which is this idea that, like you know, when you
combine in fact in a portfolio, you can get higher
by in the first fal frontier. So and he's got
(34:04):
research that goes back, you know, seventy years, fifty years.
So basically he says, okay, in this one table, if
you do a twenty five percent allocation towards Debta factor,
the size factor, the value factor, and the momentum factor,
the historical sharp ratios like point seventy four, and that's
basically double any of the individual factor bets on either
(34:27):
beta value size probably you know, Okay, So then he says, okay,
instead of doing twenty five percent, do like twenty percent
to each of those four factors. And then you add
profitability as a fifth one, and you get a point
nine to six sharp ratio. And then in the next
iteration he's substitute quality for profitability, and now you got
(34:47):
like a one point one sharp ratio. You know, when
you combine you know, five factors, you had profitability, you
sub out profitability for quality. You know, you're talking about
almost triple the sharp ratio of any individual leg beta
value size. And then you know, like if you he
(35:09):
runs someone elsis on, like what's the underperformance odds over
like a one year period, of three year period, five, ten,
twenty year and the odds of you on the perform
and are dramatically lower when you combine factors. And so
his book, you know, like everything you want to know
about fact investing. I maybe miss a title in the book,
(35:30):
but that book is kind of seminal to us. You know,
I'm a big fan of buffet. He just releases you know,
newsletter this this weekend, and you know, kind of what
he preaches, like buying quality businesses but just at a
low price. I think that's really important to how we
think about investing. So those are some of the top
(35:51):
two or three that I would say resonate with me.
Speaker 1 (35:55):
Well, this is great, John, thank you again for joining
us today.
Speaker 3 (35:59):
Next, thanks guys and Chris, thank you for.
Speaker 1 (36:02):
Being my co host today. Thank you until our next episode.
This is David Cohne with Inside Active.
Host
Gina Martin Adams
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