1 00:00:00,160 --> 00:00:03,640 Speaker 1: The following episode of At the Money was recorded July 2 00:00:03,920 --> 00:00:08,840 Speaker 1: twenty twenty five. This was before President Trump fired the 3 00:00:08,880 --> 00:00:14,000 Speaker 1: Bureau of Labor Statistics Commissioner Erica mcintarfur. Please note the 4 00:00:14,080 --> 00:00:35,800 Speaker 1: conversation was recorded before that departure. Newsday Wall Street relies 5 00:00:35,880 --> 00:00:42,280 Speaker 1: on data economic releases, quarterly earnings, performance comparisons. But it's 6 00:00:42,360 --> 00:00:45,520 Speaker 1: really easy to get tripped up by all of this math. 7 00:00:46,240 --> 00:00:51,360 Speaker 1: How should investors manage this fire hose of numbers? To 8 00:00:51,440 --> 00:00:54,640 Speaker 1: help us navigate this, let's bring in pulled Surprise winning 9 00:00:54,680 --> 00:00:59,440 Speaker 1: reporter Michael Hiltzik. He covers business for the Los Angeles Times. 10 00:00:59,640 --> 00:01:02,080 Speaker 1: He's a two time winner of the Gerald Globe Award 11 00:01:02,120 --> 00:01:06,960 Speaker 1: and has authored numerous books on business. So, Michael, let's 12 00:01:07,000 --> 00:01:09,479 Speaker 1: just start with the basics. How do you manage this 13 00:01:09,680 --> 00:01:12,679 Speaker 1: analyst torrent of data that comes our way? 14 00:01:13,520 --> 00:01:17,040 Speaker 2: Well, that's a good question. Mostly I try to ignore 15 00:01:17,959 --> 00:01:22,280 Speaker 2: most of it, and I curate what I use and 16 00:01:22,319 --> 00:01:25,199 Speaker 2: what I rely on. And basically the way I work 17 00:01:25,360 --> 00:01:28,440 Speaker 2: is I start with a topic that I want to explore, 18 00:01:28,480 --> 00:01:30,560 Speaker 2: a subject I want to explore, and then I go 19 00:01:30,680 --> 00:01:34,039 Speaker 2: search out the data that I need. In that way, 20 00:01:34,959 --> 00:01:38,760 Speaker 2: I'm not affected. I mean, there are some sources, you know, 21 00:01:39,200 --> 00:01:43,399 Speaker 2: come across my emails regularly that I will pay some 22 00:01:43,480 --> 00:01:47,240 Speaker 2: attention to, but most of it I don't, And I 23 00:01:47,280 --> 00:01:50,360 Speaker 2: know where to go most of the time for what 24 00:01:50,480 --> 00:01:50,880 Speaker 2: I need. 25 00:01:51,200 --> 00:01:55,000 Speaker 1: So let's talk about that. If you're if you're going 26 00:01:55,040 --> 00:01:59,840 Speaker 1: to be writing about or researching a particular subject, what 27 00:02:00,160 --> 00:02:03,320 Speaker 1: sources of economic data do you rely on and what 28 00:02:03,480 --> 00:02:07,240 Speaker 1: sources do you find troublesome and best ignored? 29 00:02:07,760 --> 00:02:09,920 Speaker 2: Yeah, well, I think if I'm writing about, you know, 30 00:02:10,040 --> 00:02:15,920 Speaker 2: something that touches on macroeconomics or domestic economics, I think 31 00:02:16,000 --> 00:02:18,799 Speaker 2: you can't do better than the BLS, the Bureau of 32 00:02:18,880 --> 00:02:23,080 Speaker 2: Labor Statistics or the Bureau of Economic Analysis, And thus 33 00:02:23,160 --> 00:02:27,960 Speaker 2: far they haven't been undermined maybe a little bit, but 34 00:02:28,000 --> 00:02:31,359 Speaker 2: not too much by by Trump, so their data are 35 00:02:31,400 --> 00:02:36,080 Speaker 2: really still reliable. And I also go to FRED that's 36 00:02:36,160 --> 00:02:41,440 Speaker 2: the service from the Saint Louis FED that can reduce 37 00:02:41,480 --> 00:02:44,680 Speaker 2: a lot of this of the data from BLS and 38 00:02:44,720 --> 00:02:51,880 Speaker 2: PA too graphical form, and I've published spread charts. You know, 39 00:02:52,280 --> 00:02:55,120 Speaker 2: if there's a month that passes without it, that's that's rare. 40 00:02:55,320 --> 00:03:01,160 Speaker 1: So how do you assess the credibility enact you're cy 41 00:03:01,280 --> 00:03:07,839 Speaker 1: of any source obviously, BLS, b EA, FREAD have a 42 00:03:08,000 --> 00:03:11,800 Speaker 1: very long track record. But what factors do you consider 43 00:03:11,840 --> 00:03:14,240 Speaker 1: when you're looking at a source of economic data. 44 00:03:14,600 --> 00:03:17,040 Speaker 2: Well, I look at at these sources the way I 45 00:03:17,040 --> 00:03:20,480 Speaker 2: look at any sources. You know, I look for consistency. 46 00:03:21,760 --> 00:03:24,800 Speaker 2: I look for you know, my father was a CPA 47 00:03:24,919 --> 00:03:27,519 Speaker 2: and he used to say, you know, check the arithmetic. 48 00:03:28,520 --> 00:03:32,919 Speaker 2: And I do that because you know, over the years 49 00:03:33,000 --> 00:03:35,600 Speaker 2: or decades that I've been writing about business and finance, 50 00:03:37,080 --> 00:03:40,560 Speaker 2: I look for outliers in the data. And when I 51 00:03:40,640 --> 00:03:45,280 Speaker 2: see something like that, it warrants further checking, and it 52 00:03:45,400 --> 00:03:50,600 Speaker 2: warrants skepticism. Actually, so you know, I look for trends 53 00:03:50,640 --> 00:03:55,160 Speaker 2: to be consistent. I look for the data to be 54 00:03:56,000 --> 00:04:02,560 Speaker 2: coherent and cohesive, and you know, if I can, I 55 00:04:02,680 --> 00:04:05,800 Speaker 2: check one source against the other and then try to 56 00:04:05,840 --> 00:04:10,200 Speaker 2: see if doing that turns up some flaw or flaws 57 00:04:10,240 --> 00:04:11,560 Speaker 2: in the in the print. 58 00:04:12,040 --> 00:04:14,880 Speaker 1: So so you mentioned check the math. Are there any 59 00:04:14,960 --> 00:04:19,919 Speaker 1: other common data quality issues that you encounter that investors 60 00:04:19,920 --> 00:04:20,720 Speaker 1: should be aware of? 61 00:04:21,279 --> 00:04:26,120 Speaker 2: Well, there are some consistent laws or errors or mistakes 62 00:04:25,880 --> 00:04:32,960 Speaker 2: that I find, typically in news reports that use these 63 00:04:33,040 --> 00:04:38,400 Speaker 2: data and then try to draw conclusions. I think, you know, 64 00:04:38,520 --> 00:04:43,440 Speaker 2: I both probably feel that data that's produced without an 65 00:04:43,440 --> 00:04:48,840 Speaker 2: inflation deflator or without an acknowledgment, particularly if it's a 66 00:04:48,880 --> 00:04:54,800 Speaker 2: trend line, is something that I try to fix if 67 00:04:54,800 --> 00:04:59,960 Speaker 2: I can. But certainly that's a context that is consistent 68 00:05:00,360 --> 00:05:05,240 Speaker 2: lacking in reports of the data. I you know, when 69 00:05:05,279 --> 00:05:10,400 Speaker 2: I'm reading a report of an economic release, you know, 70 00:05:10,560 --> 00:05:14,240 Speaker 2: in almost any newspaper, I will always try to go 71 00:05:14,360 --> 00:05:20,080 Speaker 2: back to the original print, not rely on somebody's interpretation. 72 00:05:20,200 --> 00:05:25,520 Speaker 2: I've just seen interpretations of data just be all over 73 00:05:25,560 --> 00:05:29,720 Speaker 2: the place, particularly if we're talking about government programs that 74 00:05:29,800 --> 00:05:37,839 Speaker 2: rely on financial statistics like Social Security, Medicare, Obamacare. I 75 00:05:38,040 --> 00:05:42,160 Speaker 2: just see so many problems in reporting on those programs 76 00:05:42,200 --> 00:05:45,960 Speaker 2: because reporters don't do the math, or they don't do 77 00:05:46,040 --> 00:05:51,280 Speaker 2: their homework, or they come at these programs through a 78 00:05:51,320 --> 00:05:57,120 Speaker 2: political perspective that basically allows them to ignore what's really happening. 79 00:05:57,839 --> 00:06:01,960 Speaker 1: So you mentioned making sure that data is inflation adjusted. 80 00:06:02,440 --> 00:06:07,039 Speaker 1: You and I have spoken about seasonality and how often 81 00:06:07,080 --> 00:06:11,880 Speaker 1: that seems to trip up consumers of data. What other 82 00:06:12,240 --> 00:06:16,919 Speaker 1: problems tend to arise when you see a commonly used 83 00:06:17,240 --> 00:06:20,159 Speaker 1: data source or data series, Well, those. 84 00:06:20,040 --> 00:06:22,800 Speaker 2: Are the big those are the big ones, you know, 85 00:06:22,920 --> 00:06:26,800 Speaker 2: if I'm looking at a chart, if it if it's 86 00:06:26,839 --> 00:06:28,839 Speaker 2: a trend line chart, and it doesn't go to zero, 87 00:06:29,000 --> 00:06:32,279 Speaker 2: so that you don't really know, you can't really tell, 88 00:06:32,560 --> 00:06:36,320 Speaker 2: you know, if a change is significant or if it's 89 00:06:37,040 --> 00:06:41,400 Speaker 2: an artifact of big numbers are small numbers. I want 90 00:06:41,400 --> 00:06:45,320 Speaker 2: to be suspicious about that. So and we see the 91 00:06:46,279 --> 00:06:49,880 Speaker 2: you know, we see these flaws in reporting all over 92 00:06:49,920 --> 00:06:54,720 Speaker 2: the place, the major newspapers, the wire services, cable news. 93 00:06:56,080 --> 00:07:00,200 Speaker 2: They are basically winging it, and they're using data. Uh, 94 00:07:00,640 --> 00:07:04,320 Speaker 2: they're using numbers that they get, they're misinterpreting them, sometimes wildly. 95 00:07:04,839 --> 00:07:07,520 Speaker 1: So you you mentioned fred, which I really think of 96 00:07:07,600 --> 00:07:12,280 Speaker 1: as an online software tool to depict data series in 97 00:07:12,320 --> 00:07:15,800 Speaker 1: a graph or an image. Any other software or tools 98 00:07:15,840 --> 00:07:16,800 Speaker 1: that you find useful. 99 00:07:17,040 --> 00:07:19,920 Speaker 2: Well from time to time, we you know, we at 100 00:07:19,960 --> 00:07:24,800 Speaker 2: the only times we've used fact set, Uh, we've used 101 00:07:24,920 --> 00:07:28,800 Speaker 2: wide charts. I'm not sure. I'm pretty sure that we're 102 00:07:28,800 --> 00:07:33,640 Speaker 2: not even subscribers to them anymore, but we use you know, 103 00:07:33,680 --> 00:07:40,200 Speaker 2: for raw data and graphical displays. I find, yeahoo, finance 104 00:07:40,320 --> 00:07:44,760 Speaker 2: is as good as as anything else. But you know, 105 00:07:44,800 --> 00:07:47,000 Speaker 2: when I'm using these these sources, I do want to 106 00:07:47,040 --> 00:07:49,680 Speaker 2: go back and double check the numbers, just to make 107 00:07:49,720 --> 00:07:53,720 Speaker 2: sure that what what I'm using are the figures that 108 00:07:53,800 --> 00:07:55,800 Speaker 2: were produced originally. 109 00:07:56,440 --> 00:08:02,000 Speaker 1: What about trade organizations I recall frequently, especially during the 110 00:08:02,040 --> 00:08:05,960 Speaker 1: financial crisis, being annoyed by a lot of the spin 111 00:08:06,680 --> 00:08:10,640 Speaker 1: from the National Association of Realtors, who are the original 112 00:08:10,720 --> 00:08:14,239 Speaker 1: source of a lot of housing sales data. 113 00:08:14,680 --> 00:08:17,240 Speaker 2: Yeah, I think I think you're absolutely right about that. 114 00:08:17,320 --> 00:08:21,640 Speaker 2: I mean, if I need to turn to an industry 115 00:08:22,400 --> 00:08:26,600 Speaker 2: source or a lobbying organization or what have you, like 116 00:08:26,720 --> 00:08:33,440 Speaker 2: the national, like the NAM, the franchisees have something, and 117 00:08:33,480 --> 00:08:37,720 Speaker 2: they all produce figures. If I'm looking for a figure 118 00:08:37,840 --> 00:08:40,120 Speaker 2: that they produce, if I want to say, you know, 119 00:08:40,200 --> 00:08:46,640 Speaker 2: the National Association of Manufacturers says this, then I'll use it. 120 00:08:46,679 --> 00:08:49,400 Speaker 2: But with the caveat that that's who they are. You 121 00:08:49,520 --> 00:08:55,439 Speaker 2: can't always trust them. They are almost always talking their book, 122 00:08:55,600 --> 00:08:59,080 Speaker 2: so to speak. And have to keep that in mind, 123 00:08:59,080 --> 00:09:00,680 Speaker 2: and it's got to be re elected in what I 124 00:09:00,720 --> 00:09:04,880 Speaker 2: write as well, and often look at you know, some 125 00:09:04,920 --> 00:09:09,120 Speaker 2: of these outfits are sources that I rely on to debunk. 126 00:09:09,840 --> 00:09:12,480 Speaker 2: And it's always a good column if I can say, look, 127 00:09:12,480 --> 00:09:14,960 Speaker 2: here's what these guys said, and here's how they got 128 00:09:15,440 --> 00:09:18,839 Speaker 2: the numbers. Wrong, and here's why they probably deliberately got 129 00:09:18,840 --> 00:09:19,560 Speaker 2: the numbers wrong. 130 00:09:20,240 --> 00:09:25,160 Speaker 1: What about thing tanks? They publish analytical data frequently, but 131 00:09:25,360 --> 00:09:29,200 Speaker 1: I would hardly consider them objective or disinterested parties. 132 00:09:29,559 --> 00:09:33,840 Speaker 2: Yeah, I agree, some are better than others. Some I 133 00:09:34,000 --> 00:09:39,080 Speaker 2: will use or quote without too much fear. The Peterson 134 00:09:39,200 --> 00:09:45,080 Speaker 2: Institute of International Economics I find consistently pretty good, definitely 135 00:09:45,760 --> 00:09:53,440 Speaker 2: useful for trade issues, trade figures, trade commentary. There's another 136 00:09:53,480 --> 00:09:59,800 Speaker 2: Peterson funded think tank, the Commission for the Responsible Federal 137 00:09:59,800 --> 00:10:04,400 Speaker 2: But I mean sometimes I find them useful. Sometimes their 138 00:10:04,840 --> 00:10:11,280 Speaker 2: analysis is so infected by ideology or partisanship that you know, 139 00:10:11,760 --> 00:10:13,920 Speaker 2: I have to walk back when I when I see 140 00:10:13,960 --> 00:10:16,960 Speaker 2: I have to sort of, you know, recalculate with what 141 00:10:17,040 --> 00:10:17,640 Speaker 2: they've used. 142 00:10:18,000 --> 00:10:20,920 Speaker 1: So you were a column recently on Tesla. What about 143 00:10:20,960 --> 00:10:26,040 Speaker 1: public companies? How do we evaluate things like not just earnings, 144 00:10:26,440 --> 00:10:31,120 Speaker 1: but forward guidance and all sorts of Sometimes it's a 145 00:10:31,160 --> 00:10:34,199 Speaker 1: little bit of happy talk about what's coming in the future. 146 00:10:35,440 --> 00:10:38,800 Speaker 2: Yeah, well, I think, you know, if we're uh, you know, 147 00:10:38,880 --> 00:10:42,520 Speaker 2: to the extent they're putting out disclosed financials, you know, 148 00:10:43,600 --> 00:10:50,200 Speaker 2: subject to sec oversight, they are, that is what it is. Uh, 149 00:10:50,400 --> 00:10:52,960 Speaker 2: you know, you know I can say, this is what 150 00:10:53,000 --> 00:10:56,680 Speaker 2: they've disclosed, this is what they've said. Forward guidance, I think, 151 00:10:56,840 --> 00:11:01,240 Speaker 2: you know, forward guidance to me is basically, you know, 152 00:11:01,400 --> 00:11:07,600 Speaker 2: trying to shoehorn a long term perspective into a snapshot. Uh. 153 00:11:07,640 --> 00:11:12,439 Speaker 2: It's it's very rare, rare that it's uh useful at all. 154 00:11:12,480 --> 00:11:15,360 Speaker 2: And of course that also depends on who's doing the 155 00:11:15,400 --> 00:11:18,720 Speaker 2: forward guidance. When we add elon musk uh you know, 156 00:11:18,800 --> 00:11:23,360 Speaker 2: deliver a you know, financial Q and a. Just last night, 157 00:11:24,160 --> 00:11:26,320 Speaker 2: I'm not sure. I'm not sure that any of that 158 00:11:26,679 --> 00:11:31,240 Speaker 2: uh is useful any more than anything he says it's useful, 159 00:11:31,320 --> 00:11:33,600 Speaker 2: and it was. It was very musky, and you know, 160 00:11:33,679 --> 00:11:36,800 Speaker 2: it was, uh, you know, we're going to have uh, 161 00:11:37,040 --> 00:11:39,560 Speaker 2: you know, we're you know, we're gonna have robots, you know, 162 00:11:39,679 --> 00:11:42,320 Speaker 2: cleaning our house and you know, take care of our 163 00:11:42,400 --> 00:11:44,720 Speaker 2: children by the end of next year, you know, I 164 00:11:44,720 --> 00:11:49,240 Speaker 2: mean is his timelines are always suspect and others. But 165 00:11:49,400 --> 00:11:54,480 Speaker 2: a company that's in trouble you want to be uh, 166 00:11:54,600 --> 00:11:58,280 Speaker 2: you know, very cautious about uh, you know what they're saying. 167 00:12:00,200 --> 00:12:03,920 Speaker 2: You know, a company that's revised me it's forward guidance. 168 00:12:04,000 --> 00:12:06,480 Speaker 2: We're dropping its forward guidance. I think we all know 169 00:12:06,559 --> 00:12:07,680 Speaker 2: these are red flans. 170 00:12:07,920 --> 00:12:11,840 Speaker 1: Yeah, I've been waiting for fully self driving cars now 171 00:12:11,880 --> 00:12:16,079 Speaker 1: for ten years and it's always two years away. So 172 00:12:16,600 --> 00:12:23,560 Speaker 1: let me ask a slightly offbeat question. Early in my career, 173 00:12:23,920 --> 00:12:29,720 Speaker 1: there was this entire group of conspiracy theorists who believed 174 00:12:29,840 --> 00:12:34,680 Speaker 1: that the BLS was cooking the data, that you couldn't 175 00:12:34,760 --> 00:12:39,439 Speaker 1: trust beea, that all of the government sources of information 176 00:12:40,000 --> 00:12:44,920 Speaker 1: were partisan and biased and completely unreliable. That hasn't been 177 00:12:44,960 --> 00:12:48,000 Speaker 1: my experience, But what's your experience, like. 178 00:12:48,679 --> 00:12:52,080 Speaker 2: Well, no, it hasn't been my experience. And look, you 179 00:12:52,120 --> 00:12:59,880 Speaker 2: know the data, the statistics that come out of those agencies. Basically, 180 00:13:00,520 --> 00:13:07,440 Speaker 2: you know, these are time trend prints essentially, and they're 181 00:13:07,480 --> 00:13:10,480 Speaker 2: the benchmarks. So I think we have to rely on 182 00:13:10,520 --> 00:13:13,040 Speaker 2: them as benchmarks. And we know that b l S 183 00:13:13,160 --> 00:13:16,920 Speaker 2: and b A I think periodically revise their methodology, but 184 00:13:16,960 --> 00:13:20,839 Speaker 2: they're transparent about it. And you know, as long as 185 00:13:20,880 --> 00:13:26,680 Speaker 2: we recognize that there's a break in in the in 186 00:13:26,720 --> 00:13:29,560 Speaker 2: the trend line, then I think we can deal with 187 00:13:29,600 --> 00:13:33,760 Speaker 2: it safely. But but but you know, politicians are always 188 00:13:33,760 --> 00:13:38,160 Speaker 2: sort of attacking these sources when they the numbers that 189 00:13:38,200 --> 00:13:43,520 Speaker 2: they produce you know, are inimical to their uh, their 190 00:13:43,520 --> 00:13:45,720 Speaker 2: partisan goals, and you know, we have to get used 191 00:13:45,760 --> 00:13:48,600 Speaker 2: to it. We're certainly seeing that now. I think we'll 192 00:13:48,600 --> 00:13:50,600 Speaker 2: see it more. You know, we're going to see an 193 00:13:50,600 --> 00:13:55,040 Speaker 2: attack on FED data. Uh, dis intensifying. 194 00:13:56,000 --> 00:14:00,480 Speaker 1: So what are the other pitfalls that investors should aware 195 00:14:00,520 --> 00:14:02,679 Speaker 1: of when it comes to economic data. 196 00:14:02,760 --> 00:14:06,040 Speaker 2: Well, I think investors always have to be sensitive to 197 00:14:06,240 --> 00:14:10,600 Speaker 2: the source of the data they're relying on. They have 198 00:14:10,679 --> 00:14:15,200 Speaker 2: to be cautious about sort of second order or third 199 00:14:15,400 --> 00:14:20,880 Speaker 2: order interpretations. The data, certainly on the macroeconomic or agency level, 200 00:14:21,400 --> 00:14:26,400 Speaker 2: is always accessible. But you know, I sympathize with investors 201 00:14:26,400 --> 00:14:29,280 Speaker 2: who just don't have the time to go back and look. 202 00:14:30,520 --> 00:14:36,280 Speaker 2: I think projections of market activity these are you know, 203 00:14:37,240 --> 00:14:43,400 Speaker 2: never of great value. You know, projections are always good 204 00:14:43,400 --> 00:14:45,720 Speaker 2: and accurate right up to the point that they're not. So, 205 00:14:47,280 --> 00:14:50,200 Speaker 2: you know, at sort of larger, larger issues. You know, 206 00:14:50,240 --> 00:14:54,080 Speaker 2: when I when I hear somebody talking about, well, liquidity 207 00:14:54,160 --> 00:14:56,760 Speaker 2: is going to drive the market or something like that, 208 00:14:57,600 --> 00:15:00,640 Speaker 2: I don't really put much trust really as in that. 209 00:15:01,080 --> 00:15:04,720 Speaker 1: So to wrap up, investors who are looking to learn 210 00:15:04,760 --> 00:15:08,800 Speaker 1: more from economic data need to go to the original 211 00:15:08,840 --> 00:15:14,080 Speaker 1: sources prioritize, make sure you are aware of things like 212 00:15:14,680 --> 00:15:20,440 Speaker 1: inflation adjusting and seasonal adjustments. Be wary about trade organizations 213 00:15:20,480 --> 00:15:23,640 Speaker 1: and think tanks, not all of them are objective. The 214 00:15:23,680 --> 00:15:28,720 Speaker 1: same is true about forward guidance from companies public companies 215 00:15:28,760 --> 00:15:31,880 Speaker 1: about what they see in the future, and just generally 216 00:15:32,080 --> 00:15:36,240 Speaker 1: use common sense when it comes to analyzing the endless 217 00:15:36,280 --> 00:15:41,160 Speaker 1: fire hose of economic data. I'm Barry Ridholts. You're listening 218 00:15:41,200 --> 00:15:43,440 Speaker 1: to act the money on bloomber