At The Money: How to Manage Your News & Data Flow

Summary

Podcast Summary: Masters in Business – At The Money: How to Manage Your News & Data Flow

Episode Details:

• Title: At The Money: How to Manage Your News & Data Flow
• Host: Bloomberg’s Barry Ritholtz
• Guest: Michael Hiltzik, Pulitzer Prize-winning Business Reporter for the Los Angeles Times
• Release Date: August 13, 2025

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Introduction

In this episode of Masters in Business, Barry Ritholtz delves into the overwhelming influx of economic data that inundates investors daily. To navigate this "fire hose of numbers," Ritholtz welcomes Michael Hiltzik, a renowned business reporter with extensive experience in analyzing and interpreting complex financial data.

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Managing the Data Deluge

Michael Hiltzik's Approach to Data Management ([01:57]): Hiltzik begins by emphasizing the importance of selective attention amidst the vast sea of data.

“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 and that way I'm not affected.” ([01:57])

This method allows him to focus on relevant information without getting bogged down by unnecessary details.

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Reliable Data Sources

Preferred Sources for Economic Data ([02:51] & [03:40]): Hiltzik highlights key sources he trusts for macroeconomic and domestic economic data:

“I think you can't do better than the BLS, the Bureau of Labor Statistics or the Bureau of Economic Analysis.” ([02:51])

He also relies on Fred, the Federal Reserve Economic Data service from the St. Louis Fed, for visual representations of data:

“Fred charts... if there's a month that passes without it, that's rare.” ([03:40])

These sources provide consistent and reliable data essential for accurate analysis.

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Assessing Credibility and Accuracy

Evaluating Data Sources ([03:58] & [04:55]): Hiltzik outlines his criteria for assessing data credibility:

“I look for consistency. I look for, you know, my father was a CPA and he used to say, you know, check the arithmetic.” ([03:58])

He emphasizes verifying data through multiple sources to identify outliers and discrepancies, ensuring the information's integrity.

Common Data Quality Issues ([05:05] – [07:03]): Hiltzik points out frequent pitfalls in data reporting, such as the lack of inflation adjustment and improper trend analysis:

“Data that's produced without an inflation deflator... is something that I try to fix if I can.” ([05:05])

He also criticizes trend line charts that don’t start at zero, advocating for clearer representations to avoid misinterpretation.

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Tools and Software for Data Analysis

Useful Analytical Tools ([07:48] & [08:00]): While acknowledging the utility of Fred, Hiltzik also mentions other tools like FactSet and Y Charts, although he notes their subscription cost may be prohibitive:

“I've seen interpretations of data just be all over the place... heavily dependent on tools.” ([07:48])

For accessible alternatives, he recommends Yahoo Finance, stressing the importance of cross-verifying figures with original sources.

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Evaluating Trade Organizations and Think Tanks

Skepticism Towards Industry Sources ([08:58] – [10:19]): Hiltzik warns about the biases inherent in data from trade organizations and industry sources:

“If I'm looking for a figure that they produce me... with the caveat that that's who they are. You can't always trust them.” ([08:58])

Regarding think tanks, he differentiates based on ideological leanings:

“The Peterson Institute of International Economics, I find consistently pretty good... Sometimes their analysis is so infected by ideology...” ([10:19])

This discernment is crucial for maintaining objectivity in data interpretation.

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Assessing Public Companies and Forward Guidance

Analyzing Corporate Data ([11:25] – [12:58]): Hiltzik discusses the reliability of forward guidance from public companies, using Tesla and Elon Musk as examples:

“Forward guidance to me is basically, you know, trying to shoehorn a long term perspective into a snapshot.” ([11:25])

He expresses skepticism towards overly optimistic forecasts, noting that they are often unreliable predictors of actual performance.

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Handling Skepticism Towards Government Data

Trust in Government-Produced Data ([13:39] – [14:48]): Addressing concerns about the integrity of government data, Hiltzik affirms his trust in agencies like the BLS and BEA:

“The data... are the benchmarks. And we know that BLS and BEA... they're transparent about it.” ([13:39])

He acknowledges political attacks on these institutions but maintains that their data remains foundational for economic analysis.

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Additional Pitfalls for Investors

Navigating Economic Data Challenges ([14:59] – [16:04]): Hiltzik advises investors to remain vigilant about the sources and interpretations of economic data, cautioning against:

• Second or Third Order Interpretations: Relying on layered analyses that may distort original data.
• Projections and Market Activity: Recognizing the inherent uncertainty in forecasts.
• Overreliance on Trends like Liquidity: Understanding that macroeconomic theories may not always translate into actionable insights.

“Projections are never of great value... I think we all know these are red flags.” ([16:04])

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Conclusion

Barry Ritholtz summarizes the key strategies for managing economic data:

• Use Original Sources: Prioritize data directly from trusted institutions like BLS, BEA, and Fred.
• Adjust for Inflation and Seasonality: Ensure data is contextually accurate.
• Critically Evaluate Trade Organizations and Think Tanks: Recognize potential biases.
• Be Skeptical of Corporate Forward Guidance: Understand the limitations of company projections.
• Apply Common Sense: Use discernment to navigate the vast influx of economic information.

Hiltzik’s insights provide a robust framework for investors to effectively manage and interpret economic data, ensuring informed and strategic decision-making.

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Notable Quotes:

• “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 and that way I'm not affected.” – Michael Hiltzik ([01:57])

• “Data that's produced without an inflation deflator... is something that I try to fix if I can.” – Michael Hiltzik ([05:05])

• “Forward guidance to me is basically, you know, trying to shoehorn a long term perspective into a snapshot.” – Michael Hiltzik ([11:25])

• “Projections are never of great value... I think we all know these are red flags.” – Michael Hiltzik ([16:04])

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This episode serves as a comprehensive guide for investors seeking to streamline their data consumption, emphasizing the importance of source credibility, data accuracy, and critical analysis in the ever-evolving landscape of financial information.

Transcript

[Karen Moscow]

Bloomberg Daybreak is your best way to get informed first thing in the morning, right in your podcast feed. Hi, I'm Karen Moscow.

[Nathan Hager]

And I'm Nathan Hager. Each morning we're up early putting together the latest episode of Bloomberg Daybreak US Edition. It's your daily 15 minute podcast on the latest in global news, politics and international relations.

[Karen Moscow]

What's special about Bloomberg Daybreak is the immediacy of the news we bring you each day in your podcast feed by 6am Eastern Time.

[Nathan Hager]

This isn't a deep dive on yesterday's news. Instead, you get the latest stories with.

[Karen Moscow]

Context and that's something you don't get from other news podcasts. So join us for the best from Bloomberg's 3,000 journalists and analysts around the world with reporting backed by data and journalists at the center of the stories we cover.

[Nathan Hager]

Listen to the Bloomberg Daybreak US Edition podcast each morning for the stories that matter with the context you need.

[Karen Moscow]

Find us on Apple, Spotify or anywhere you listen to.

[Barry Ritholtz]

Wall street relies 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 Pulitzer Prize winning reporter Michael Hiltzik. He covers business for the Los Angeles Times. He's a two time winner of the Gerald Loeb 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?

[Michael Hiltzik]

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 and that way I'm not affected. I mean, there are some sources, you know, come across my, my emails regularly that I will pay some attention to, but most of it I, I don't, and I know where to go most of the time for, for what I need.

[Barry Ritholtz]

So, 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, and what sources do you, do you find troublesome and best ignored?

[Michael Hiltzik]

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 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 St. Louis Fed that can reduce a lot of this, the data from BLS and PEA to graphical form. And I've published Fred charts. You know, if there's a month that passes without it, that's, that's rare. So, so how do you rely on.

[Barry Ritholtz]

Those, how do you assess the credibility and accuracy of any source? Obviously, bls, Bea, Fred have a very long track record, but what factors do you consider when you're looking at a source of economic data?

[Michael Hiltzik]

Well, I look 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, and I do that because, you know, over the years or decades that I've been writing about business and finance, 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 basically, you know, I, if I can, I 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.

[Barry Ritholtz]

So. So you mentioned check the math. Are there any other common data quality issues that you encounter that investors should be aware of?

[Michael Hiltzik]

Well, there's some consistent flaws 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 inflation deflator or without an acknowledgement, 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 consistently lacking in reports of the data. When I'm reading a report of an economic release 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 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 their homework, or they come at these programs through a political perspective that basically allows them to ignore what's really happening.

[Barry Ritholtz]

So you mentioned making sure. The 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?

[Michael Hiltzik]

Well, those are the big, those are the big ones. You know, if I'm looking at a chart, if it, if, 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 or small numbers. I want 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, they're using numbers that they get, and they're misinterpreting them sometimes wildly.

[Barry Ritholtz]

So you mentioned, Fred, which I really think of as an online software tool that depicts data series in a graph or an image. Any other software or tools that you find useful?

[Michael Hiltzik]

Well, from time to time, we, you know, we at the LA Times, we've used FactSet, we've used Y charts. I'm not sure, I'm pretty sure that we're not even subscribers to them anymore, but we use them, you know, for raw data and graphical displays. I find Yahoo Finance is as good as anything else. But, you know, when I'm using these sources, I do want to go back and double check the numbers just to make sure that what I'm using are the figures that were produced originally.

[Barry Ritholtz]

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.

[Michael Hiltzik]

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, have something and they all produce figures. If I'm looking for a figure that they produce me, 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. And it's got to be reflected in what I write as well and often. Look, you know, some of these outfits are sources that, 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.

[Barry Ritholtz]

Yeah, we're the easiest people to fool is ourselves is the old joke. We can, we can take that out. What about think tanks? They publish analytical data frequently, but I would hardly consider them objective or disinterested parties.

[Michael Hiltzik]

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 Peterson funded think tank, the Commission for the Responsible Federal Budget. Sometimes I find them useful. Sometimes their analysis is so infected by ideology or partisanship that, you know, I have to walk back what I, what I see. I have to sort of, you know, recalculate what, what they've used.

[Barry Ritholtz]

So you had 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.

[Michael Hiltzik]

Yeah, well, I think, you know, if we're, you know, to the extent they're putting out disclosed financials, you know, subject to SEC oversight, they are, that is what it is, you know, you know, I can say this is what they've disclosed, this is what they've said. Forward guidance. I think, you know, forward guidance to me is basically, you know, trying to shoehorn a long term perspective and into a snapshot. It's very rare, rare that it's useful at all. And of course it also depends on who's doing the forward guidance when we had Elon Musk deliver a financial Q and A just last night. I'm not sure that any of that is useful any more than anything he says is useful. And it was, was very muskian, you know, it was, you know, we're going to have, you know, you know, we're going to have robots, you know, cleaning our house and, you know, take care of our children by the end of next year. You know, I mean his, his timelines are always suspect and, and others, a company that's in trouble, you want to be, you know, very cautious about, you know, what they're saying. You know, a company that's revising its forward guidance or dropping its forward guidance. I think we all know these are red flags.

[Barry Ritholtz]

Yeah, I've been waiting for fully self driving cars now for 10 years and it's always two years away. So so let me ask a slightly offbeat question. Early in my career, there was this entire group of conspiracy theorists who believed that the BLS was cooking the data that you couldn't trust Bea, that all of the government sources of information were partisan and biased and completely unreliable. That hasn't been my experience, but. But what's your experience like?

[Michael Hiltzik]

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 them as benchmarks. And we know that BLS and bea, 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 the trend line, then I think we can deal with it safely. But politicians are always sort of attacking these sources when the numbers that they produce are inimical to their partisan goals. And we have to get used to it. We're certainly seeing that now. I think we'll see it more. We're going to see an attack on Fed data just intensifying.

[Barry Ritholtz]

So what, what other, I'm gonna say it again. What are the other pitfalls that investors should be aware of when it comes to economic data?

[Michael Hiltzik]

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 of data. You know, the data certainly on a macroeconomic or agency level is always accessible. But you know, I sympathize with investors 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, you know, and 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 for reliance in that.

[Barry Ritholtz]

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 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 Ritholtz, you're listening to at the Money on Bloomberg.

[Karen Moscow]

Bloomberg Daybreak is your best way to get informed first thing in the morning, right in your podcast feed. Hi, I'm Karen Moscow.

[Nathan Hager]

And I'm Nathan Hager. Each morning we're up early putting together the latest episode of Bloomberg Daybreak US Edition. It's your daily 15 minute podcast on the latest in global news, politics and international relations.

[Karen Moscow]

What's special about Bloomberg Daybreak is the immediacy of the news we bring you each day in your podcast feed by 6am Eastern Time.

[Nathan Hager]

This isn't a deep dive on yesterday's news. Instead, you get the latest stories with confidence, context.

[Karen Moscow]

And that's something you don't get from other news podcasts. So join us for the best from Bloomberg's 3,000 journalists and analysts around the world, with reporting backed by data and journalists at the center of the stories we cover.

[Nathan Hager]

Listen to the Bloomberg Daybreak US Edition podcast each morning for the stories that matter with the context you need.

[Karen Moscow]

Find us on Apple, Spotify or anywhere you listen.