1 00:00:02,440 --> 00:00:06,760 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. 2 00:00:08,360 --> 00:00:10,959 Speaker 2: I'm Stephen Carroll and this is Here's Why, where we 3 00:00:11,000 --> 00:00:13,119 Speaker 2: take one news story and explain it in just a 4 00:00:13,119 --> 00:00:20,520 Speaker 2: few minutes with our experts Here at Bloomberg, earning season 5 00:00:20,680 --> 00:00:24,400 Speaker 2: can feel like marking a scorecard. We compare the numbers 6 00:00:24,440 --> 00:00:28,560 Speaker 2: companies reports to what they and industry experts were expecting. 7 00:00:29,000 --> 00:00:31,040 Speaker 1: Another big day on the earnings from then Reno and 8 00:00:31,080 --> 00:00:34,080 Speaker 1: this is crucial maintaining its full year guidance. 9 00:00:34,159 --> 00:00:37,000 Speaker 2: That's after some of its rivals think of Stalantis, think 10 00:00:37,000 --> 00:00:39,440 Speaker 2: of BMWVW Cutts. Therefore you guidance. 11 00:00:39,520 --> 00:00:42,120 Speaker 1: General Motors are up about two percent posted better than 12 00:00:42,159 --> 00:00:43,400 Speaker 1: expected results. 13 00:00:43,120 --> 00:00:44,400 Speaker 2: Or mess numbers coming out here. 14 00:00:44,520 --> 00:00:47,320 Speaker 1: It looks like their sales are actually beating the estimates. 15 00:00:47,320 --> 00:00:48,720 Speaker 1: Remember this is coming right off of the. 16 00:00:48,680 --> 00:00:50,840 Speaker 2: Heels of caring in the last twenty four hours, which 17 00:00:50,880 --> 00:00:54,160 Speaker 2: showed the exact opposite. These reports give us a look 18 00:00:54,240 --> 00:00:57,480 Speaker 2: under the hood whether business is good or not, and 19 00:00:57,600 --> 00:00:59,840 Speaker 2: also give us an insight into what companies are seeing 20 00:01:00,000 --> 00:01:04,400 Speaker 2: coming down the line. Meeting, beating or missing expectations are 21 00:01:04,440 --> 00:01:07,759 Speaker 2: some of the most important metrics for investors, but it's 22 00:01:07,800 --> 00:01:11,280 Speaker 2: not easy to pick exactly where to set your expectations. 23 00:01:11,959 --> 00:01:18,880 Speaker 2: Here's why company earnings are so difficult to forecast. To 24 00:01:18,959 --> 00:01:21,480 Speaker 2: explain how it's done, I've got Gena Martin Adams with me, 25 00:01:21,560 --> 00:01:25,240 Speaker 2: chief equity strategist for Bloomberg Intelligence, Gina, great to have you. 26 00:01:25,240 --> 00:01:28,199 Speaker 2: Can you give us a peek inside your crystal ball? 27 00:01:28,480 --> 00:01:30,520 Speaker 2: What sort of things do you look at when deciding 28 00:01:30,720 --> 00:01:32,640 Speaker 2: what to expect from a company's report? 29 00:01:32,920 --> 00:01:35,760 Speaker 1: Yeah? Great question. The crystal ball is often very cloudy 30 00:01:35,760 --> 00:01:38,160 Speaker 1: when it comes to earnings, in particular, when you're taking 31 00:01:38,360 --> 00:01:43,080 Speaker 1: five hundred companies estimates, aggregating them to an index level, 32 00:01:43,440 --> 00:01:46,160 Speaker 1: and then trying to determine if the degree of accuracy 33 00:01:46,400 --> 00:01:49,920 Speaker 1: becomes a little bit suspicious, you know, I think thats 34 00:01:50,240 --> 00:01:53,480 Speaker 1: what most forecasters do is look at the consensus and 35 00:01:53,880 --> 00:01:58,600 Speaker 1: run through their models how their expectations might deviate from consensus. 36 00:01:58,600 --> 00:02:03,160 Speaker 1: The consensus, of course, is the community of other analysts, 37 00:02:03,720 --> 00:02:06,640 Speaker 1: which I think ultimately creates a lot of error in 38 00:02:06,880 --> 00:02:11,080 Speaker 1: company analysis because ultimately the consensus should be the market, 39 00:02:11,720 --> 00:02:15,320 Speaker 1: and the market is not always priced exactly in line 40 00:02:15,360 --> 00:02:18,520 Speaker 1: with how analysts are expecting earnings to play out. And 41 00:02:18,520 --> 00:02:21,440 Speaker 1: we see this quite often through the options markets, which 42 00:02:21,480 --> 00:02:25,040 Speaker 1: probably provide us with a greater degree of accuracy than 43 00:02:25,160 --> 00:02:30,119 Speaker 1: analysts forecasts do over time because of participation. There are 44 00:02:30,200 --> 00:02:35,079 Speaker 1: just so many people betting on earnings anticipating earnings growth 45 00:02:35,160 --> 00:02:38,160 Speaker 1: that the consensus view of the market tends to be 46 00:02:38,240 --> 00:02:40,400 Speaker 1: more correct. But we do a lot of work on this. 47 00:02:40,520 --> 00:02:45,680 Speaker 1: We assess analyst consensus, compare that to company guidance, Compare 48 00:02:45,720 --> 00:02:49,119 Speaker 1: that to what's priced in the market. It's different across 49 00:02:49,240 --> 00:02:52,560 Speaker 1: all industries, it's different across all companies, it's different across 50 00:02:52,560 --> 00:02:55,400 Speaker 1: all sectors, and then we aggregate it to the index 51 00:02:55,520 --> 00:02:58,440 Speaker 1: level to try to get an assessment of what's anticipated 52 00:02:58,840 --> 00:03:04,240 Speaker 1: and what might ultimately beat or meet or miss those expectations. 53 00:03:04,639 --> 00:03:07,200 Speaker 2: It's a complex task and a massive one that you 54 00:03:07,280 --> 00:03:11,200 Speaker 2: take on every earning season. I wonder does the disclaimer 55 00:03:11,280 --> 00:03:15,160 Speaker 2: you know, past performance does not guarantee future returns relevant 56 00:03:15,240 --> 00:03:19,000 Speaker 2: when it comes to these source of expectations. Is it 57 00:03:19,080 --> 00:03:21,600 Speaker 2: that you look at what's gone before and try to 58 00:03:21,600 --> 00:03:24,400 Speaker 2: extrapolate what's happening next or talk us through a bit 59 00:03:24,440 --> 00:03:25,440 Speaker 2: of the science involved. 60 00:03:25,600 --> 00:03:29,600 Speaker 1: Yeah. So oftentimes what happens with the analyst community anyway, 61 00:03:29,639 --> 00:03:32,480 Speaker 1: at the individual stock level is they run a company model, 62 00:03:33,280 --> 00:03:37,000 Speaker 1: and that company model is based upon past relationships as 63 00:03:37,040 --> 00:03:41,200 Speaker 1: well as anticipated future market or market share changes, future 64 00:03:41,280 --> 00:03:45,160 Speaker 1: cost input changes. There's a lot that goes into each 65 00:03:45,200 --> 00:03:48,160 Speaker 1: individual analyst forecast, and then what we do is we 66 00:03:48,240 --> 00:03:51,560 Speaker 1: aggregate those analysts forecasts to an index level and get 67 00:03:51,560 --> 00:03:55,000 Speaker 1: an assessment of what is anticipated by the analyst community 68 00:03:55,120 --> 00:03:58,560 Speaker 1: for all stocks in a given index. The other way 69 00:03:58,600 --> 00:04:01,240 Speaker 1: that you can do this we definitely do this in 70 00:04:01,280 --> 00:04:03,680 Speaker 1: our team as well, is look at company guidance and 71 00:04:04,560 --> 00:04:09,800 Speaker 1: use historical guidance as an indication of what is likely 72 00:04:09,840 --> 00:04:12,560 Speaker 1: to come in the earning season. For example, the last 73 00:04:12,560 --> 00:04:16,200 Speaker 1: two earning seasons are guidance based model, which really just 74 00:04:16,240 --> 00:04:20,719 Speaker 1: takes what companies say is likely has anticipated much stronger 75 00:04:20,760 --> 00:04:23,679 Speaker 1: growth than the analyst community has anticipated and has actually 76 00:04:23,720 --> 00:04:26,800 Speaker 1: given us some pretty good direction as to what to expect. 77 00:04:27,279 --> 00:04:29,160 Speaker 1: And then the third way that we look at the 78 00:04:29,360 --> 00:04:32,719 Speaker 1: broader market is also to use macro inputs to try 79 00:04:32,720 --> 00:04:35,200 Speaker 1: to forecast earnings. We don't do this necessarily on a 80 00:04:35,240 --> 00:04:38,200 Speaker 1: quarterby quarterbasis, but really do this looking out over the 81 00:04:38,200 --> 00:04:40,080 Speaker 1: next twelve months, what are the moving parts of the 82 00:04:40,120 --> 00:04:43,880 Speaker 1: macro anticipate for earnings? And these are very very simple 83 00:04:43,880 --> 00:04:48,240 Speaker 1: regressions where we just look at typical what has historically 84 00:04:49,080 --> 00:04:52,400 Speaker 1: proven indicative of earnings from a macro perspective. Use those 85 00:04:52,440 --> 00:04:55,560 Speaker 1: inputs to try to estimate what's happened what's likely to 86 00:04:55,560 --> 00:04:58,760 Speaker 1: happen with earnings over the next twelve months. This is 87 00:04:59,440 --> 00:05:03,280 Speaker 1: actually proven to be a pretty effective way of forecasting 88 00:05:03,320 --> 00:05:06,400 Speaker 1: long term earning strends. It's been terrible the last two 89 00:05:06,520 --> 00:05:08,760 Speaker 1: years as the index, especially in the S and P 90 00:05:08,839 --> 00:05:12,880 Speaker 1: five hundred, has deviated materially from the macro economy. Given 91 00:05:13,520 --> 00:05:17,960 Speaker 1: this abnormally strong performance and earnings leadership of the mag 92 00:05:18,080 --> 00:05:23,040 Speaker 1: seven which has created this very strong earnings recovery absent 93 00:05:23,279 --> 00:05:25,920 Speaker 1: a very strong macro backdrop. 94 00:05:25,640 --> 00:05:28,800 Speaker 2: I wonder who gets it most right of everyone that's 95 00:05:28,839 --> 00:05:31,520 Speaker 2: looking and trying making these forecasts. Do the analyst community 96 00:05:31,520 --> 00:05:34,600 Speaker 2: tends be closer to what happens? Or are companies themselves 97 00:05:34,720 --> 00:05:37,280 Speaker 2: generally a good guide to what actually transpires. 98 00:05:37,520 --> 00:05:40,680 Speaker 1: Yeah, it's a great question, and the short answer is 99 00:05:40,680 --> 00:05:45,840 Speaker 1: that changes. So sometimes. The macro is a great forecast tool. 100 00:05:45,920 --> 00:05:48,840 Speaker 1: As I mentioned, historically, we've got a very high R 101 00:05:48,880 --> 00:05:51,440 Speaker 1: squared in our regression model. It exists for a reason. 102 00:05:51,480 --> 00:05:54,320 Speaker 1: It does a very good job of anticipating where earnings 103 00:05:54,320 --> 00:05:57,520 Speaker 1: are likely to head normally, but we've been in an 104 00:05:57,560 --> 00:06:03,640 Speaker 1: abnormal environment. Analysts have gotten less precise over time. I 105 00:06:03,839 --> 00:06:07,440 Speaker 1: can't explain exactly why that has happened, but they've become 106 00:06:07,560 --> 00:06:11,560 Speaker 1: in particular, over the last three years almost perma bears 107 00:06:11,839 --> 00:06:16,960 Speaker 1: permanently underestimating company potential for earnings growth. It's difficult to 108 00:06:17,000 --> 00:06:20,040 Speaker 1: determine if that's because of this macro divergence that has 109 00:06:20,080 --> 00:06:23,839 Speaker 1: come out or not. And recently guidance has given much 110 00:06:23,880 --> 00:06:26,800 Speaker 1: better indication of what's likely to come for the index 111 00:06:26,839 --> 00:06:30,160 Speaker 1: at large. Now, the most interesting thing about guidance is 112 00:06:30,400 --> 00:06:36,080 Speaker 1: it's pretty anomaloust to see such fantastic accuracy emerge from 113 00:06:36,080 --> 00:06:39,039 Speaker 1: the guidance numbers. And I say that because only about 114 00:06:39,040 --> 00:06:41,880 Speaker 1: a fifth of S and P five hundred companies give 115 00:06:41,960 --> 00:06:46,560 Speaker 1: us guidance at any point in time, and therefore we're 116 00:06:46,800 --> 00:06:51,760 Speaker 1: relying on just twenty percent of companies guiding on expectations 117 00:06:51,800 --> 00:06:56,040 Speaker 1: to drive our expectation for the broad market. But nonetheless 118 00:06:56,200 --> 00:06:59,719 Speaker 1: their guidance has proven to be very accurate and quite 119 00:06:59,760 --> 00:07:03,640 Speaker 1: aive of earnings trends, at least over the last few years. 120 00:07:03,920 --> 00:07:06,400 Speaker 2: That's so interesting because I did wonder. I mean, obviously, 121 00:07:06,480 --> 00:07:09,600 Speaker 2: everyone wants to surpass expectations when it comes to the 122 00:07:09,920 --> 00:07:12,160 Speaker 2: reported numbers. But I did wonder if there was ever 123 00:07:12,200 --> 00:07:14,880 Speaker 2: a trend of companies under promising so that they can 124 00:07:14,920 --> 00:07:16,160 Speaker 2: then over deliver. 125 00:07:16,520 --> 00:07:19,800 Speaker 1: Yeah, I think that that has historically been the case, 126 00:07:20,160 --> 00:07:24,560 Speaker 1: and there's still somewhat under promising, but the analyst community 127 00:07:24,600 --> 00:07:30,160 Speaker 1: has still been so pessimistic and undershooting reality that guidance, 128 00:07:30,200 --> 00:07:33,080 Speaker 1: even though they're underpromising and over delivering, their under promises 129 00:07:33,640 --> 00:07:35,640 Speaker 1: are still above analyst consensus. 130 00:07:36,000 --> 00:07:36,360 Speaker 2: Wow. 131 00:07:36,640 --> 00:07:39,080 Speaker 1: And I think that that's largely down to tech and 132 00:07:39,120 --> 00:07:42,480 Speaker 1: this phenomenon that has occurred in the tech industry with 133 00:07:42,520 --> 00:07:45,920 Speaker 1: respect to AI and the mag seven earners. When we 134 00:07:45,960 --> 00:07:49,080 Speaker 1: look at guidance, the vast majority of guidance comes from 135 00:07:49,440 --> 00:07:53,240 Speaker 1: tech and consumer discretionary sectors anyway. So we get a 136 00:07:53,240 --> 00:07:56,200 Speaker 1: really good feel from companies on what's happening with the 137 00:07:56,240 --> 00:07:58,400 Speaker 1: consumer outlook, the global consumer out look. We get a 138 00:07:58,400 --> 00:08:00,840 Speaker 1: pretty good feel from companies what's happening with global tech, 139 00:08:01,600 --> 00:08:06,320 Speaker 1: and those industries have been quite dominant in the index. 140 00:08:06,520 --> 00:08:09,240 Speaker 1: So I think that's what's happening. But it is fascinating 141 00:08:09,280 --> 00:08:13,800 Speaker 1: to see how analysts really just have not jumped on 142 00:08:13,880 --> 00:08:17,040 Speaker 1: the bandwagon of optimism, even though companies continually tell them 143 00:08:17,080 --> 00:08:18,240 Speaker 1: they should be more optimistic. 144 00:08:18,480 --> 00:08:21,120 Speaker 2: When we think about how the reaction comes to the 145 00:08:21,640 --> 00:08:24,640 Speaker 2: reports as we get them, is there room for nuance? 146 00:08:24,800 --> 00:08:27,720 Speaker 2: Can you beat on one metric, disappoint on another and 147 00:08:27,960 --> 00:08:30,120 Speaker 2: kind of end up with a positive share price reaction 148 00:08:30,160 --> 00:08:30,560 Speaker 2: in the end? 149 00:08:30,720 --> 00:08:33,520 Speaker 1: Oh? Absolutely, And you can also have the reverse and 150 00:08:33,559 --> 00:08:37,079 Speaker 1: that ends up being where the options market really comes 151 00:08:37,120 --> 00:08:42,080 Speaker 1: in handy. Is oftentimes what's priced in a stock is 152 00:08:42,120 --> 00:08:47,160 Speaker 1: not actually reflected in analyst expectations or guidance at all. 153 00:08:47,200 --> 00:08:49,160 Speaker 1: And we saw this emerge a little bit in the 154 00:08:49,200 --> 00:08:52,679 Speaker 1: second quarter. If you recall, going into that July reporting season, 155 00:08:52,800 --> 00:08:55,960 Speaker 1: expectations were very high for tech companies at large, in 156 00:08:56,000 --> 00:09:00,560 Speaker 1: particular AI related industries. Across the board. Tech commpanies beat 157 00:09:00,600 --> 00:09:04,880 Speaker 1: those expectations. They even guided for higher growth going forward, 158 00:09:04,920 --> 00:09:07,920 Speaker 1: but in many cases the stocks did not perform particularly 159 00:09:07,960 --> 00:09:11,600 Speaker 1: well in response to earnings. And that's because there was 160 00:09:11,640 --> 00:09:14,840 Speaker 1: a bunch of nuance in the detail. And that detail 161 00:09:15,600 --> 00:09:18,880 Speaker 1: really with that sector was a regarding margin and the 162 00:09:18,920 --> 00:09:23,280 Speaker 1: potential sustainability of margin recovery. That has emerged as a 163 00:09:23,280 --> 00:09:25,679 Speaker 1: powerful driver of earnings growth in that segment for the 164 00:09:25,760 --> 00:09:28,560 Speaker 1: last year and a half or so, a company started 165 00:09:28,559 --> 00:09:31,880 Speaker 1: talking about, well, we're still going to beat expectations, we 166 00:09:31,920 --> 00:09:34,240 Speaker 1: still see very strong growth, but we might have to 167 00:09:34,280 --> 00:09:36,280 Speaker 1: spend a little bit more than we had anticipated. We're 168 00:09:36,280 --> 00:09:40,440 Speaker 1: really adjusting our spending plans, we're really assessing digging a 169 00:09:40,440 --> 00:09:42,920 Speaker 1: bit deeper into the spending outlook, and that created a 170 00:09:42,920 --> 00:09:46,040 Speaker 1: lot of nervousness in the market. So you can absolutely 171 00:09:46,080 --> 00:09:49,120 Speaker 1: have these key issues that emerge that are oftentimes very 172 00:09:49,200 --> 00:09:54,400 Speaker 1: nuanced or oftentimes extremely detailed. Even though they beat expectations, 173 00:09:54,600 --> 00:09:58,319 Speaker 1: raised guidance, generally had a very positive earning season, that 174 00:09:58,440 --> 00:10:02,040 Speaker 1: commentary can so drive stock prices. This is a big 175 00:10:02,200 --> 00:10:05,080 Speaker 1: part of the reason why we also track sentiment. We 176 00:10:05,120 --> 00:10:08,960 Speaker 1: have a transcript analysis tool that we've developed. We track 177 00:10:09,080 --> 00:10:13,880 Speaker 1: sentiment toward keywords, sentiment in management commentary as well as 178 00:10:13,880 --> 00:10:18,280 Speaker 1: in analyst questions toward keywords because you can often get 179 00:10:18,440 --> 00:10:21,760 Speaker 1: enormous stock price reactions and a lot of signals from 180 00:10:21,800 --> 00:10:23,760 Speaker 1: the words and the reactions to the words. 181 00:10:24,040 --> 00:10:27,439 Speaker 2: Gina Mark Adam's chief equity strategist a Bloomberg Intelligence, thank 182 00:10:27,440 --> 00:10:29,440 Speaker 2: you so much for joining us and giving us an 183 00:10:29,440 --> 00:10:32,920 Speaker 2: insight into how these sorts of forecasts are put together 184 00:10:33,000 --> 00:10:35,319 Speaker 2: for more explanations like this from our team of twenty 185 00:10:35,320 --> 00:10:37,800 Speaker 2: seven hundred journalists and analysts around the world. Search for 186 00:10:37,880 --> 00:10:40,720 Speaker 2: quick take on the Bloomberg website or Bloomberg Business app. 187 00:10:42,160 --> 00:10:44,680 Speaker 2: I'm Stephen Carol. This is here's why. I'll be back 188 00:10:44,720 --> 00:10:46,480 Speaker 2: next week with more. Thanks for listening.