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At The Money: Filtering Noisy News · Masters in Business

• 0:02
  Speaker 1: Bloomberg Audio studios, podcasts, radio news come on on the
• 0:13
  Speaker 1: US investor's face, a flood of noisy news, social media, TV, radio,
• 0:31
  Speaker 1: and more. None of it is tailored to you in particular,
• 0:35
  Speaker 1: and much of it appears to be outrageous algo driven clickbait.
• 0:41
  Speaker 1: What's an investor supposed to do to help us navigate this?
• 0:45
  Speaker 1: Let's bring in Pultzer Prize winning reporter Michael Hiltzik. He
• 0:49
  Speaker 1: covers business for the Los Angeles Times. He's a two
• 0:52
  Speaker 1: time winner of the Gerald Lobe Award, as well as
• 0:56
  Speaker 1: the author of numerous books on finance. Well, Michael, let's
• 1:01
  Speaker 1: start with this endless sea of noise. How do you
• 1:05
  Speaker 1: navigate this? How do you prioritize what's important and what's not.
• 1:11
  Speaker 2: Well, you know, it used to be said that that
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  Speaker 2: newspaper readers know how to read their newspaper. You know,
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  Speaker 2: they sort of, you know, assess what they see there,
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  Speaker 2: you know, how it conforms to their own world views,
• 1:25
  Speaker 2: how it conforms to what they see in the outside
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  Speaker 2: wide world of reality. And I think that's probably more
• 1:34
  Speaker 2: important now than it ever has been before. Newspaper reports
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  Speaker 2: of data are always second order reports. Reporter who's looked
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  Speaker 2: at the data, or an editor has dropped a page
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  Speaker 2: or sent an email and said, you know, do something
• 1:55
  Speaker 2: on this report from this oud or the other. And
• 2:00
  Speaker 2: I think you always have to assess the source. As
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  Speaker 2: we've talked about, you know, if we're talking about a
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  Speaker 2: trade organization, well, a trade organization is basically a pr organization,
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  Speaker 2: and it's going to put its own spin on whatever
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  Speaker 2: number is it cooks up, and sometimes it puts spin
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  Speaker 2: on numbers even before they're cooked up. If a trade
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  Speaker 2: organization is citing a study from a supposedly independent group,
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  Speaker 2: I want to know if they commissioned the study or
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  Speaker 2: who commissioned it. And that's a mixture part of my assessment.
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  Speaker 2: And I always ask, you know, if I'm calling a
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  Speaker 2: lobbyists and saying, you know, you just cited this, you know,
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  Speaker 2: supposedly third party analysis, you know, is it yours? Did
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  Speaker 2: you commission this? And you know, if they're honest, they'll
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  Speaker 2: they'll tell me. I just just one, you know, into
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  Speaker 2: my email box, I think just the other day, and
• 3:00
  Speaker 2: I've asked but haven't gotten a reply. So that's that's
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  Speaker 2: very important, and I think readers have to understand that
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  Speaker 2: more than ever before. Certainly, you know more than in
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  Speaker 2: my long career. Reporters are over worked. They don't have
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  Speaker 2: the time to do their own work, so they will
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  Speaker 2: basically take a press release from some data source and
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  Speaker 2: parrot it regurgitate it. So I think it's important for
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  Speaker 2: readers to do what I do, which is to check
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  Speaker 2: the source and if they can go to the raw
• 3:39
  Speaker 2: material and make a judgment for themselves, intelligent investors should
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  Speaker 2: be able to do that.
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  Speaker 1: So that raises an obvious question, how do you tell
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  Speaker 1: which sources are trustworthy? Who do you put on your
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  Speaker 1: all star list? Who do you eliminate?
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  Speaker 2: Well, there are a few sources and economics sources where
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  Speaker 2: are on my all star list? You know, you and
• 4:02
  Speaker 2: your folks you know because I quote you with some frequency.
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  Speaker 2: There are other organizations that have shown, you know, through
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  Speaker 2: the test of time, to have been to have integrity
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  Speaker 2: and how they interpret data. Uh And and then there
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  Speaker 2: are some where you just want to factor in what
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  Speaker 2: you know about their ideology, their their their funders, their history,
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  Speaker 2: and that just you know, those simple inquiries will tell
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  Speaker 2: you a lot about how you want to assess information
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  Speaker 2: that comes at you from all these sources.
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  Speaker 1: You mentioned reporters are pressed for time. So are investors.
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  Speaker 1: You're kind of hinting at hey, this requires some time, effort,
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  Speaker 1: and work in order to figure out what is a
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  Speaker 1: credible source of reliable news and what is a little
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  Speaker 1: more let's just call it unreliable.
• 5:03
  Speaker 2: Well, you know, I think I remember, you know, reading
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  Speaker 2: Andy Tobias's book, you know, probably one of its earliest incarnations,
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  Speaker 2: where he said, you know, what do you do if
• 5:16
  Speaker 2: a broker comes to you, you know, cold call or
• 5:19
  Speaker 2: someone you know and pitches an investment for you? And
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  Speaker 2: what his advice was to say, well, you know, don't
• 5:29
  Speaker 2: invest in that investment, but see how it does. And
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  Speaker 2: if it's done well, then maybe you want to listen
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  Speaker 2: to this guy or col a little bit more closely
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  Speaker 2: the next time. And I think that that's sort of
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  Speaker 2: a good idea. More generally, you know, some reports will
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  Speaker 2: be refuted or debunked fairly quickly, and some of these
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  Speaker 2: sources will compile a record of inaccuracy or dishonesty, and
• 6:02
  Speaker 2: some will compile the record of reliability. And it takes time,
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  Speaker 2: it takes attention, and I think investors, like readers and
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  Speaker 2: reporters need to do their homework and we just see
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  Speaker 2: that being more and more difficult, or just happening a
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  Speaker 2: lot less than it used to.
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  Speaker 1: I've been noticing what, at least to me, it seems
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  Speaker 1: like more than ever, anecdotes and one off stories and
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  Speaker 1: narrative tales. They just seem to be increasingly popular.
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  Speaker 2: How do you.
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  Speaker 1: Navigate through what is a compelling story? My Matthew friends
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  Speaker 1: always say, and equals one? All right, so you have
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  Speaker 1: not a data series, but that's one ancdote. How do
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  Speaker 1: you manage that?
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  Speaker 2: That's right? And sometimes the end one is oneself? So yeah, well, look, basically,
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  Speaker 2: I've always been sort of averse to men on the
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  Speaker 2: street stories because you know, a man on the street
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  Speaker 2: or a woman on the street that's that is that
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  Speaker 2: is an an equals one, and you really have no
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  Speaker 2: idea what You can't really always tell what questions have
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  Speaker 2: been asked, what this person knows. We've certainly seen, you know,
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  Speaker 2: certainly during the inflation era under of the last few years,
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  Speaker 2: we saw a lot of interviews with families in which
• 7:34
  Speaker 2: they talked about how much they're spending on this or
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  Speaker 2: that commodity or or product and got it totally wrong.
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  Speaker 2: And so I think that people can sort of compare
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  Speaker 2: what they're reading to their own experience and if it's
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  Speaker 2: really at odds that that's important to keep in mind.
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  Speaker 2: So I think, I mean, I've gone, you know, I've
• 8:02
  Speaker 2: been assigned to do man on the street stories. But
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  Speaker 2: you know, when they work, it's because you have a
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  Speaker 2: narrow subject and you are dealing with people who are
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  Speaker 2: in a position to know the answers to the questions
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  Speaker 2: you were talking about, but sort of throwing in you know,
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  Speaker 2: somebody who's standing online or you know, I used to say,
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  Speaker 2: I was always suspicious of stories that quoted the driver
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  Speaker 2: who brought the reporter from the airport to the first
• 8:34
  Speaker 2: class hotel in town. And we used to see that
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  Speaker 2: when I was in Africa. You know, I could tell
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  Speaker 2: you know, you know, the show for you know, sometimes
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  Speaker 2: you know was labeled to hide. But yeah, so you
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  Speaker 2: want to make sure that you know, if there's if
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  Speaker 2: there are interviews with individuals or families or couples, that
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  Speaker 2: there's more than one and that they seem to actually
• 9:00
  Speaker 2: know what they're talking about and they're talking about their
• 9:03
  Speaker 2: own experiences, and that they sound plausible. So these are wrong.
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  Speaker 2: You know, the sort of tests that we have to
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  Speaker 2: conduct in our in our daily lives.
• 9:15
  Speaker 1: What about social media? How do we avoid the worst
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  Speaker 1: aspects of algorithmic height that seems to work its way
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  Speaker 1: into mainstream media as well.
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  Speaker 2: Uh yeah, well, certainly mainstream media stories that rely on
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  Speaker 2: social media sourcing very suspect social media. Look, you know,
• 9:40
  Speaker 2: I was always a fan of Twitter. I would I
• 9:44
  Speaker 2: still do, you know, I still am a fan. I
• 9:46
  Speaker 2: think Twitter, you know, for all its faults, still has
• 9:50
  Speaker 2: a critical mass that alternatives like Bousky just haven't quite reached. So,
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  Speaker 2: you know, with with X as we call it, I
• 10:04
  Speaker 2: think you can sort of wean out the wild nonsense.
• 10:10
  Speaker 2: I always liked X because I could curate my my
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  Speaker 2: tweet timeline and rely on sources on that platform that
• 10:24
  Speaker 2: that I had come to know. It's more and more
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  Speaker 2: difficult now, and it's harder to uh sort of you know,
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  Speaker 2: get rid of, you know, some of the straws. But
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  Speaker 2: you know, there are websites that that I go back
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  Speaker 2: to over and over again. They're not all economic websites.
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  Speaker 2: Sometimes they're writers who think the way I do. So
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  Speaker 2: you know, I get reinforcement where I need it, and
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  Speaker 2: then there are some that I just ignore, you know,
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  Speaker 2: I'm you know, I have more time to myself because
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  Speaker 2: I'm not paying any attention to a lot of the stuff.
• 11:03
  Speaker 1: Yeah, I found on Twitter curating your own lists on
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  Speaker 1: different topics. For me, it's the economy, it's data and analytics,
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  Speaker 1: it's behavioral finance. There are experts out there talking about
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  Speaker 1: subjects that I like, and at least it's there's some
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  Speaker 1: filtering process by creating a list, it's not going to
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  Speaker 1: stop spam and other junk stuff from coming through. All right,
• 11:29
  Speaker 1: So we're talking about social media. I guess if we're
• 11:32
  Speaker 1: talking about news and problems, we have to discuss AI,
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  Speaker 1: not just the hallucination, but the risk that that news
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  Speaker 1: story you're reading is literally fake news, just something created
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  Speaker 1: by AI cheaply. How do we navigate a world where
• 11:53
  Speaker 1: AI is cranking out a lot of artificial intelligence is
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  Speaker 1: cranking out a lot of news that isn't exactly following
• 12:03
  Speaker 1: the rules of journalism.
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  Speaker 2: Yeah, I should tell you that I am an AI skeptic.
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  Speaker 2: I think, you know, some large percentage of AI claims
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  Speaker 2: by developers and by clients is marketing in the same
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  Speaker 2: way that you know dot Com used to be the
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  Speaker 2: big marketing truth. Sure twenty five years ago now, and
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  Speaker 2: so a lot of what is pitched as AI is
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  Speaker 2: not really AI, and none of it is intelligence. I'm
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  Speaker 2: not sure. I'm of two minds about whether people are
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  Speaker 2: going to become better at at detecting AI creation or worse.
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  Speaker 2: I think at the moment, there are giveaways that anyone
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  Speaker 2: can see, you know, in the sometimes in the language
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  Speaker 2: that's used, sometimes in the images that are created. We
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  Speaker 2: see this over and over again, just you know, clearly
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  Speaker 2: AI hallucinations. So you know, right now there's an AI
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  Speaker 2: craze in an industry and including in the news industry,
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  Speaker 2: and I think that's going to be a problem. I
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  Speaker 2: think it's going to go bad. And I think, you know,
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  Speaker 2: relying on AI is going to be something that you know,
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  Speaker 2: when we look at it in the rearview mirror, we're
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  Speaker 2: going to say, what were we thinking? You know, why
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  Speaker 2: did we spend any money on this?
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  Speaker 1: So to wrap up, apply common sense to your consumption
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  Speaker 1: of news. Figure out who's trustworthy, what sources are accurate.
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  Speaker 1: Put the time and effort in to identifying who's worthy
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  Speaker 1: of your time and trust. Beware of social media, be
• 13:58
  Speaker 1: wary of a and don't be afraid to follow people
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  Speaker 1: who have bylines that you trust, rather than just blindly
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  Speaker 1: paying attention to any particular media source. It's worth understanding
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  Speaker 1: what you're consuming and why, and staying on the right
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  Speaker 1: side of accuracy. I'm Barry Ridults. You're listening to Bloomberg's
• 14:22
  Speaker 1: at the Money