August 13, 2025 • 15 mins
This episode of At The Money was recorded in July, before President Trump fired the Bureau of Labor Statistics Commissioner Erika McEntarfer. So please note that this conversation was had before that departure.
We live in an age of endless news and spin. We rely on data and news releases, but it's really easy to get tripped up by all of it. How should investors manage this firehose of numbers?
Michael Hiltzik covers business for the Los Angeles Times, is a two-time winner of the Gerald Loeb Award and has authored numerous books on business.
Each week, “At the Money” discusses an important topic in money management. From portfolio construction to taxes and cutting down on fees, join Barry Ritholtz to learn the best ways to put your money to work.
See omnystudio.com/listener for privacy information.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
The following episode of At the Money was recorded July
twenty twenty five. This was before President Trump fired the
Bureau of Labor Statistics Commissioner Erica mcintarfur. Please note the
conversation was recorded before that departure. Newsday Wall Street relies
(00:35):
on data economic releases, quarterly earnings, performance comparisons. But it's
really easy to get tripped up by all of this math.
How should investors manage this fire hose of numbers? To
help us navigate this, let's bring in pulled Surprise winning
reporter Michael Hiltzik. He covers business for the Los Angeles Times.
(00:59):
He's a two time winner of the Gerald Globe Award
and has authored numerous books on business. So, Michael, let's
just start with the basics. How do you manage this
analyst torrent of data that comes our way?
Speaker 2 (01:13):
Well, that's a good question. Mostly I try to ignore
most of it, and I curate what I use and
what I rely on. And basically the way I work
is I start with a topic that I want to explore,
a subject I want to explore, and then I go
search out the data that I need. In that way,
(01:34):
I'm not affected. I mean, there are some sources, you know,
come across my emails regularly that I will pay some
attention to, but most of it I don't, And I
know where to go most of the time for what
I need.
Speaker 1 (01:51):
So let's talk about that. If you're if you're going
to be writing about or researching a particular subject, what
sources of economic data do you rely on and what
sources do you find troublesome and best ignored?
Speaker 2 (02:07):
Yeah, well, I think if I'm writing about, you know,
something that touches on macroeconomics or domestic economics, I think
you can't do better than the BLS, the Bureau of
Labor Statistics or the Bureau of Economic Analysis, And thus
far they haven't been undermined maybe a little bit, but
(02:28):
not too much by by Trump, so their data are
really still reliable. And I also go to FRED that's
the service from the Saint Louis FED that can reduce
a lot of this of the data from BLS and
PA too graphical form, and I've published spread charts. You know,
(02:52):
if there's a month that passes without it, that's that's rare.
Speaker 1 (02:55):
So how do you assess the credibility enact you're cy
of any source obviously, BLS, b EA, FREAD have a
very long track record. But what factors do you consider
when you're looking at a source of economic data.
Speaker 2 (03:14):
Well, I look at at these sources the way I
look at any sources. You know, I look for consistency.
I look for you know, my father was a CPA
and he used to say, you know, check the arithmetic.
And I do that because you know, over the years
or decades that I've been writing about business and finance,
(03:37):
I look for outliers in the data. And when I
see something like that, it warrants further checking, and it
warrants skepticism. Actually, so you know, I look for trends
to be consistent. I look for the data to be
coherent and cohesive, and you know, if I can, I
(04:02):
check one source against the other and then try to
see if doing that turns up some flaw or flaws
in the in the print.
Speaker 1 (04:12):
So so you mentioned check the math. Are there any
other common data quality issues that you encounter that investors
should be aware of?
Speaker 2 (04:21):
Well, there are some consistent laws or errors or mistakes
that I find, typically in news reports that use these
data and then try to draw conclusions. I think, you know,
I both probably feel that data that's produced without an
(04:43):
inflation deflator or without an acknowledgment, particularly if it's a
trend line, is something that I try to fix if
I can. But certainly that's a context that is consistent
lacking in reports of the data. I you know, when
(05:05):
I'm reading a report of an economic release, you know,
in almost any newspaper, I will always try to go
back to the original print, not rely on somebody's interpretation.
I've just seen interpretations of data just be all over
(05:25):
the place, particularly if we're talking about government programs that
rely on financial statistics like Social Security, Medicare, Obamacare. I
just see so many problems in reporting on those programs
because reporters don't do the math, or they don't do
(05:46):
their homework, or they come at these programs through a
political perspective that basically allows them to ignore what's really happening.
Speaker 1 (05:57):
So you mentioned making sure that data is inflation adjusted.
You and I have spoken about seasonality and how often
that seems to trip up consumers of data. What other
problems tend to arise when you see a commonly used
data source or data series, Well, those.
Speaker 2 (06:20):
Are the big those are the big ones, you know,
if I'm looking at a chart, if it if it's
a trend line chart, and it doesn't go to zero,
so that you don't really know, you can't really tell,
you know, if a change is significant or if it's
an artifact of big numbers are small numbers. I want
(06:41):
to be suspicious about that. So and we see the
you know, we see these flaws in reporting all over
the place, the major newspapers, the wire services, cable news.
They are basically winging it, and they're using data. Uh,
they're using numbers that they get, they're misinterpreting them, sometimes wildly.
Speaker 1 (07:04):
So you you mentioned fred, which I really think of
as an online software tool to depict data series in
a graph or an image. Any other software or tools
that you find useful.
Speaker 2 (07:17):
Well from time to time, we you know, we at
the only times we've used fact set, Uh, we've used
wide charts. I'm not sure. I'm pretty sure that we're
not even subscribers to them anymore, but we use you know,
for raw data and graphical displays. I find, yeahoo, finance
(07:40):
is as good as as anything else. But you know,
when I'm using these these sources, I do want to
go back and double check the numbers, just to make
sure that what what I'm using are the figures that
were produced originally.
Speaker 1 (07:56):
What about trade organizations I recall frequently, especially during the
financial crisis, being annoyed by a lot of the spin
from the National Association of Realtors, who are the original
source of a lot of housing sales data.
Speaker 2 (08:14):
Yeah, I think I think you're absolutely right about that.
I mean, if I need to turn to an industry
source or a lobbying organization or what have you, like
the national, like the NAM, the franchisees have something, and
they all produce figures. If I'm looking for a figure
(08:37):
that they produce, if I want to say, you know,
the National Association of Manufacturers says this, then I'll use it.
But with the caveat that that's who they are. You
can't always trust them. They are almost always talking their book,
so to speak. And have to keep that in mind,
(08:59):
and it's got to be re elected in what I
write as well, and often look at you know, some
of these outfits are sources that I rely on to debunk.
And it's always a good column if I can say, look,
here's what these guys said, and here's how they got
the numbers. Wrong, and here's why they probably deliberately got
the numbers wrong.
Speaker 1 (09:20):
What about thing tanks? They publish analytical data frequently, but
I would hardly consider them objective or disinterested parties.
Speaker 2 (09:29):
Yeah, I agree, some are better than others. Some I
will use or quote without too much fear. The Peterson
Institute of International Economics I find consistently pretty good, definitely
useful for trade issues, trade figures, trade commentary. There's another
(09:53):
Peterson funded think tank, the Commission for the Responsible Federal
But I mean sometimes I find them useful. Sometimes their
analysis is so infected by ideology or partisanship that you know,
I have to walk back when I when I see
(10:13):
I have to sort of, you know, recalculate with what
they've used.
Speaker 1 (10:18):
So you were a column recently on Tesla. What about
public companies? How do we evaluate things like not just earnings,
but forward guidance and all sorts of Sometimes it's a
little bit of happy talk about what's coming in the future.
Speaker 2 (10:35):
Yeah, well, I think, you know, if we're uh, you know,
to the extent they're putting out disclosed financials, you know,
subject to sec oversight, they are, that is what it is. Uh,
you know, you know I can say, this is what
they've disclosed, this is what they've said. Forward guidance, I think,
(10:56):
you know, forward guidance to me is basically, you know,
trying to shoehorn a long term perspective into a snapshot. Uh.
It's it's very rare, rare that it's uh useful at all.
And of course that also depends on who's doing the
forward guidance. When we add elon musk uh you know,
(11:18):
deliver a you know, financial Q and a. Just last night,
I'm not sure. I'm not sure that any of that
uh is useful any more than anything he says it's useful,
and it was. It was very musky, and you know,
it was, uh, you know, we're going to have uh,
you know, we're you know, we're gonna have robots, you know,
(11:39):
cleaning our house and you know, take care of our
children by the end of next year, you know, I
mean is his timelines are always suspect and others. But
a company that's in trouble you want to be uh,
you know, very cautious about uh, you know what they're saying.
(12:00):
You know, a company that's revised me it's forward guidance.
We're dropping its forward guidance. I think we all know
these are red flans.
Speaker 1 (12:07):
Yeah, I've been waiting for fully self driving cars now
for ten years and it's always two years away. So
let me ask a slightly offbeat question. Early in my career,
there was this entire group of conspiracy theorists who believed
(12:29):
that the BLS was cooking the data, that you couldn't
trust beea, that all of the government sources of information
were partisan and biased and completely unreliable. That hasn't been
my experience, But what's your experience, like.
Speaker 2 (12:48):
Well, no, it hasn't been my experience. And look, you
know the data, the statistics that come out of those agencies. Basically,
you know, these are time trend prints essentially, and they're
the benchmarks. So I think we have to rely on
(13:10):
them as benchmarks. And we know that b l S
and b A I think periodically revise their methodology, but
they're transparent about it. And you know, as long as
we recognize that there's a break in in the in
the trend line, then I think we can deal with
it safely. But but but you know, politicians are always
(13:33):
sort of attacking these sources when they the numbers that
they produce you know, are inimical to their uh, their
partisan goals, and you know, we have to get used
to it. We're certainly seeing that now. I think we'll
see it more. You know, we're going to see an
attack on FED data. Uh, dis intensifying.
Speaker 1 (13:56):
So what are the other pitfalls that investors should aware
of when it comes to economic data.
Speaker 2 (14:02):
Well, I think investors always have to be sensitive to
the source of the data they're relying on. They have
to be cautious about sort of second order or third
order interpretations. The data, certainly on the macroeconomic or agency level,
is always accessible. But you know, I sympathize with investors
(14:26):
who just don't have the time to go back and look.
I think projections of market activity these are you know,
never of great value. You know, projections are always good
and accurate right up to the point that they're not. So,
(14:47):
you know, at sort of larger, larger issues. You know,
when I when I hear somebody talking about, well, liquidity
is going to drive the market or something like that,
I don't really put much trust really as in that.
Speaker 1 (15:01):
So to wrap up, investors who are looking to learn
more from economic data need to go to the original
sources prioritize, make sure you are aware of things like
inflation adjusting and seasonal adjustments. Be wary about trade organizations
and think tanks, not all of them are objective. The
(15:23):
same is true about forward guidance from companies public companies
about what they see in the future, and just generally
use common sense when it comes to analyzing the endless
fire hose of economic data. I'm Barry Ridholts. You're listening
to act the money on bloomber
The following episode of At the Money was recorded July
twenty twenty five. This was before President Trump fired the
Bureau of Labor Statistics Commissioner Erica mcintarfur. Please note the
conversation was recorded before that departure. Newsday Wall Street relies
(00:35):
on data economic releases, quarterly earnings, performance comparisons. But it's
really easy to get tripped up by all of this math.
How should investors manage this fire hose of numbers? To
help us navigate this, let's bring in pulled Surprise winning
reporter Michael Hiltzik. He covers business for the Los Angeles Times.
(00:59):
He's a two time winner of the Gerald Globe Award
and has authored numerous books on business. So, Michael, let's
just start with the basics. How do you manage this
analyst torrent of data that comes our way?
Speaker 2 (01:13):
Well, that's a good question. Mostly I try to ignore
most of it, and I curate what I use and
what I rely on. And basically the way I work
is I start with a topic that I want to explore,
a subject I want to explore, and then I go
search out the data that I need. In that way,
(01:34):
I'm not affected. I mean, there are some sources, you know,
come across my emails regularly that I will pay some
attention to, but most of it I don't, And I
know where to go most of the time for what
I need.
Speaker 1 (01:51):
So let's talk about that. If you're if you're going
to be writing about or researching a particular subject, what
sources of economic data do you rely on and what
sources do you find troublesome and best ignored?
Speaker 2 (02:07):
Yeah, well, I think if I'm writing about, you know,
something that touches on macroeconomics or domestic economics, I think
you can't do better than the BLS, the Bureau of
Labor Statistics or the Bureau of Economic Analysis, And thus
far they haven't been undermined maybe a little bit, but
(02:28):
not too much by by Trump, so their data are
really still reliable. And I also go to FRED that's
the service from the Saint Louis FED that can reduce
a lot of this of the data from BLS and
PA too graphical form, and I've published spread charts. You know,
(02:52):
if there's a month that passes without it, that's that's rare.
Speaker 1 (02:55):
So how do you assess the credibility enact you're cy
of any source obviously, BLS, b EA, FREAD have a
very long track record. But what factors do you consider
when you're looking at a source of economic data.
Speaker 2 (03:14):
Well, I look at at these sources the way I
look at any sources. You know, I look for consistency.
I look for you know, my father was a CPA
and he used to say, you know, check the arithmetic.
And I do that because you know, over the years
or decades that I've been writing about business and finance,
(03:37):
I look for outliers in the data. And when I
see something like that, it warrants further checking, and it
warrants skepticism. Actually, so you know, I look for trends
to be consistent. I look for the data to be
coherent and cohesive, and you know, if I can, I
(04:02):
check one source against the other and then try to
see if doing that turns up some flaw or flaws
in the in the print.
Speaker 1 (04:12):
So so you mentioned check the math. Are there any
other common data quality issues that you encounter that investors
should be aware of?
Speaker 2 (04:21):
Well, there are some consistent laws or errors or mistakes
that I find, typically in news reports that use these
data and then try to draw conclusions. I think, you know,
I both probably feel that data that's produced without an
(04:43):
inflation deflator or without an acknowledgment, particularly if it's a
trend line, is something that I try to fix if
I can. But certainly that's a context that is consistent
lacking in reports of the data. I you know, when
(05:05):
I'm reading a report of an economic release, you know,
in almost any newspaper, I will always try to go
back to the original print, not rely on somebody's interpretation.
I've just seen interpretations of data just be all over
(05:25):
the place, particularly if we're talking about government programs that
rely on financial statistics like Social Security, Medicare, Obamacare. I
just see so many problems in reporting on those programs
because reporters don't do the math, or they don't do
(05:46):
their homework, or they come at these programs through a
political perspective that basically allows them to ignore what's really happening.
Speaker 1 (05:57):
So you mentioned making sure that data is inflation adjusted.
You and I have spoken about seasonality and how often
that seems to trip up consumers of data. What other
problems tend to arise when you see a commonly used
data source or data series, Well, those.
Speaker 2 (06:20):
Are the big those are the big ones, you know,
if I'm looking at a chart, if it if it's
a trend line chart, and it doesn't go to zero,
so that you don't really know, you can't really tell,
you know, if a change is significant or if it's
an artifact of big numbers are small numbers. I want
(06:41):
to be suspicious about that. So and we see the
you know, we see these flaws in reporting all over
the place, the major newspapers, the wire services, cable news.
They are basically winging it, and they're using data. Uh,
they're using numbers that they get, they're misinterpreting them, sometimes wildly.
Speaker 1 (07:04):
So you you mentioned fred, which I really think of
as an online software tool to depict data series in
a graph or an image. Any other software or tools
that you find useful.
Speaker 2 (07:17):
Well from time to time, we you know, we at
the only times we've used fact set, Uh, we've used
wide charts. I'm not sure. I'm pretty sure that we're
not even subscribers to them anymore, but we use you know,
for raw data and graphical displays. I find, yeahoo, finance
(07:40):
is as good as as anything else. But you know,
when I'm using these these sources, I do want to
go back and double check the numbers, just to make
sure that what what I'm using are the figures that
were produced originally.
Speaker 1 (07:56):
What about trade organizations I recall frequently, especially during the
financial crisis, being annoyed by a lot of the spin
from the National Association of Realtors, who are the original
source of a lot of housing sales data.
Speaker 2 (08:14):
Yeah, I think I think you're absolutely right about that.
I mean, if I need to turn to an industry
source or a lobbying organization or what have you, like
the national, like the NAM, the franchisees have something, and
they all produce figures. If I'm looking for a figure
(08:37):
that they produce, if I want to say, you know,
the National Association of Manufacturers says this, then I'll use it.
But with the caveat that that's who they are. You
can't always trust them. They are almost always talking their book,
so to speak. And have to keep that in mind,
(08:59):
and it's got to be re elected in what I
write as well, and often look at you know, some
of these outfits are sources that I rely on to debunk.
And it's always a good column if I can say, look,
here's what these guys said, and here's how they got
the numbers. Wrong, and here's why they probably deliberately got
the numbers wrong.
Speaker 1 (09:20):
What about thing tanks? They publish analytical data frequently, but
I would hardly consider them objective or disinterested parties.
Speaker 2 (09:29):
Yeah, I agree, some are better than others. Some I
will use or quote without too much fear. The Peterson
Institute of International Economics I find consistently pretty good, definitely
useful for trade issues, trade figures, trade commentary. There's another
(09:53):
Peterson funded think tank, the Commission for the Responsible Federal
But I mean sometimes I find them useful. Sometimes their
analysis is so infected by ideology or partisanship that you know,
I have to walk back when I when I see
(10:13):
I have to sort of, you know, recalculate with what
they've used.
Speaker 1 (10:18):
So you were a column recently on Tesla. What about
public companies? How do we evaluate things like not just earnings,
but forward guidance and all sorts of Sometimes it's a
little bit of happy talk about what's coming in the future.
Speaker 2 (10:35):
Yeah, well, I think, you know, if we're uh, you know,
to the extent they're putting out disclosed financials, you know,
subject to sec oversight, they are, that is what it is. Uh,
you know, you know I can say, this is what
they've disclosed, this is what they've said. Forward guidance, I think,
(10:56):
you know, forward guidance to me is basically, you know,
trying to shoehorn a long term perspective into a snapshot. Uh.
It's it's very rare, rare that it's uh useful at all.
And of course that also depends on who's doing the
forward guidance. When we add elon musk uh you know,
(11:18):
deliver a you know, financial Q and a. Just last night,
I'm not sure. I'm not sure that any of that
uh is useful any more than anything he says it's useful,
and it was. It was very musky, and you know,
it was, uh, you know, we're going to have uh,
you know, we're you know, we're gonna have robots, you know,
(11:39):
cleaning our house and you know, take care of our
children by the end of next year, you know, I
mean is his timelines are always suspect and others. But
a company that's in trouble you want to be uh,
you know, very cautious about uh, you know what they're saying.
(12:00):
You know, a company that's revised me it's forward guidance.
We're dropping its forward guidance. I think we all know
these are red flans.
Speaker 1 (12:07):
Yeah, I've been waiting for fully self driving cars now
for ten years and it's always two years away. So
let me ask a slightly offbeat question. Early in my career,
there was this entire group of conspiracy theorists who believed
(12:29):
that the BLS was cooking the data, that you couldn't
trust beea, that all of the government sources of information
were partisan and biased and completely unreliable. That hasn't been
my experience, But what's your experience, like.
Speaker 2 (12:48):
Well, no, it hasn't been my experience. And look, you
know the data, the statistics that come out of those agencies. Basically,
you know, these are time trend prints essentially, and they're
the benchmarks. So I think we have to rely on
(13:10):
them as benchmarks. And we know that b l S
and b A I think periodically revise their methodology, but
they're transparent about it. And you know, as long as
we recognize that there's a break in in the in
the trend line, then I think we can deal with
it safely. But but but you know, politicians are always
(13:33):
sort of attacking these sources when they the numbers that
they produce you know, are inimical to their uh, their
partisan goals, and you know, we have to get used
to it. We're certainly seeing that now. I think we'll
see it more. You know, we're going to see an
attack on FED data. Uh, dis intensifying.
Speaker 1 (13:56):
So what are the other pitfalls that investors should aware
of when it comes to economic data.
Speaker 2 (14:02):
Well, I think investors always have to be sensitive to
the source of the data they're relying on. They have
to be cautious about sort of second order or third
order interpretations. The data, certainly on the macroeconomic or agency level,
is always accessible. But you know, I sympathize with investors
(14:26):
who just don't have the time to go back and look.
I think projections of market activity these are you know,
never of great value. You know, projections are always good
and accurate right up to the point that they're not. So,
(14:47):
you know, at sort of larger, larger issues. You know,
when I when I hear somebody talking about, well, liquidity
is going to drive the market or something like that,
I don't really put much trust really as in that.
Speaker 1 (15:01):
So to wrap up, investors who are looking to learn
more from economic data need to go to the original
sources prioritize, make sure you are aware of things like
inflation adjusting and seasonal adjustments. Be wary about trade organizations
and think tanks, not all of them are objective. The
(15:23):
same is true about forward guidance from companies public companies
about what they see in the future, and just generally
use common sense when it comes to analyzing the endless
fire hose of economic data. I'm Barry Ridholts. You're listening
to act the money on bloomber
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