Speaker 1: Bloomberg Audio Studios, Podcasts, radio news.
Speaker 2: I'm Stephen Carroll and this is Here's Why, where we
Speaker 2: take one news story and explain it in just a
Speaker 2: few minutes with our experts Here at Bloomberg, earning season
Speaker 2: can feel like marking a scorecard. We compare the numbers
Speaker 2: companies reports to what they and industry experts were expecting.
Speaker 1: Another big day on the earnings from then Reno and
Speaker 1: this is crucial maintaining its full year guidance.
Speaker 2: That's after some of its rivals think of Stalantis, think
Speaker 2: of BMWVW Cutts. Therefore you guidance.
Speaker 1: General Motors are up about two percent posted better than
Speaker 1: expected results.
Speaker 2: Or mess numbers coming out here.
Speaker 1: It looks like their sales are actually beating the estimates.
Speaker 1: Remember this is coming right off of the.
Speaker 2: Heels of caring in the last twenty four hours, which
Speaker 2: showed the exact opposite. These reports give us a look
Speaker 2: under the hood whether business is good or not, and
Speaker 2: also give us an insight into what companies are seeing
Speaker 2: coming down the line. Meeting, beating or missing expectations are
Speaker 2: some of the most important metrics for investors, but it's
Speaker 2: not easy to pick exactly where to set your expectations.
Speaker 2: Here's why company earnings are so difficult to forecast. To
Speaker 2: explain how it's done, I've got Gena Martin Adams with me,
Speaker 2: chief equity strategist for Bloomberg Intelligence, Gina, great to have you.
Speaker 2: Can you give us a peek inside your crystal ball?
Speaker 2: What sort of things do you look at when deciding
Speaker 2: what to expect from a company's report?
Speaker 1: Yeah? Great question. The crystal ball is often very cloudy
Speaker 1: when it comes to earnings, in particular, when you're taking
Speaker 1: five hundred companies estimates, aggregating them to an index level,
Speaker 1: and then trying to determine if the degree of accuracy
Speaker 1: becomes a little bit suspicious, you know, I think thats
Speaker 1: what most forecasters do is look at the consensus and
Speaker 1: run through their models how their expectations might deviate from consensus.
Speaker 1: The consensus, of course, is the community of other analysts,
Speaker 1: which I think ultimately creates a lot of error in
Speaker 1: company analysis because ultimately the consensus should be the market,
Speaker 1: and the market is not always priced exactly in line
Speaker 1: with how analysts are expecting earnings to play out. And
Speaker 1: we see this quite often through the options markets, which
Speaker 1: probably provide us with a greater degree of accuracy than
Speaker 1: analysts forecasts do over time because of participation. There are
Speaker 1: just so many people betting on earnings anticipating earnings growth
Speaker 1: that the consensus view of the market tends to be
Speaker 1: more correct. But we do a lot of work on this.
Speaker 1: We assess analyst consensus, compare that to company guidance, Compare
Speaker 1: that to what's priced in the market. It's different across
Speaker 1: all industries, it's different across all companies, it's different across
Speaker 1: all sectors, and then we aggregate it to the index
Speaker 1: level to try to get an assessment of what's anticipated
Speaker 1: and what might ultimately beat or meet or miss those expectations.
Speaker 2: It's a complex task and a massive one that you
Speaker 2: take on every earning season. I wonder does the disclaimer
Speaker 2: you know, past performance does not guarantee future returns relevant
Speaker 2: when it comes to these source of expectations. Is it
Speaker 2: that you look at what's gone before and try to
Speaker 2: extrapolate what's happening next or talk us through a bit
Speaker 2: of the science involved.
Speaker 1: Yeah. So oftentimes what happens with the analyst community anyway,
Speaker 1: at the individual stock level is they run a company model,
Speaker 1: and that company model is based upon past relationships as
Speaker 1: well as anticipated future market or market share changes, future
Speaker 1: cost input changes. There's a lot that goes into each
Speaker 1: individual analyst forecast, and then what we do is we
Speaker 1: aggregate those analysts forecasts to an index level and get
Speaker 1: an assessment of what is anticipated by the analyst community
Speaker 1: for all stocks in a given index. The other way
Speaker 1: that you can do this we definitely do this in
Speaker 1: our team as well, is look at company guidance and
Speaker 1: use historical guidance as an indication of what is likely
Speaker 1: to come in the earning season. For example, the last
Speaker 1: two earning seasons are guidance based model, which really just
Speaker 1: takes what companies say is likely has anticipated much stronger
Speaker 1: growth than the analyst community has anticipated and has actually
Speaker 1: given us some pretty good direction as to what to expect.
Speaker 1: And then the third way that we look at the
Speaker 1: broader market is also to use macro inputs to try
Speaker 1: to forecast earnings. We don't do this necessarily on a
Speaker 1: quarterby quarterbasis, but really do this looking out over the
Speaker 1: next twelve months, what are the moving parts of the
Speaker 1: macro anticipate for earnings? And these are very very simple
Speaker 1: regressions where we just look at typical what has historically
Speaker 1: proven indicative of earnings from a macro perspective. Use those
Speaker 1: inputs to try to estimate what's happened what's likely to
Speaker 1: happen with earnings over the next twelve months. This is
Speaker 1: actually proven to be a pretty effective way of forecasting
Speaker 1: long term earning strends. It's been terrible the last two
Speaker 1: years as the index, especially in the S and P
Speaker 1: five hundred, has deviated materially from the macro economy. Given
Speaker 1: this abnormally strong performance and earnings leadership of the mag
Speaker 1: seven which has created this very strong earnings recovery absent
Speaker 1: a very strong macro backdrop.
Speaker 2: I wonder who gets it most right of everyone that's
Speaker 2: looking and trying making these forecasts. Do the analyst community
Speaker 2: tends be closer to what happens? Or are companies themselves
Speaker 2: generally a good guide to what actually transpires.
Speaker 1: Yeah, it's a great question, and the short answer is
Speaker 1: that changes. So sometimes. The macro is a great forecast tool.
Speaker 1: As I mentioned, historically, we've got a very high R
Speaker 1: squared in our regression model. It exists for a reason.
Speaker 1: It does a very good job of anticipating where earnings
Speaker 1: are likely to head normally, but we've been in an
Speaker 1: abnormal environment. Analysts have gotten less precise over time. I
Speaker 1: can't explain exactly why that has happened, but they've become
Speaker 1: in particular, over the last three years almost perma bears
Speaker 1: permanently underestimating company potential for earnings growth. It's difficult to
Speaker 1: determine if that's because of this macro divergence that has
Speaker 1: come out or not. And recently guidance has given much
Speaker 1: better indication of what's likely to come for the index
Speaker 1: at large. Now, the most interesting thing about guidance is
Speaker 1: it's pretty anomaloust to see such fantastic accuracy emerge from
Speaker 1: the guidance numbers. And I say that because only about
Speaker 1: a fifth of S and P five hundred companies give
Speaker 1: us guidance at any point in time, and therefore we're
Speaker 1: relying on just twenty percent of companies guiding on expectations
Speaker 1: to drive our expectation for the broad market. But nonetheless
Speaker 1: their guidance has proven to be very accurate and quite
Speaker 1: aive of earnings trends, at least over the last few years.
Speaker 2: That's so interesting because I did wonder. I mean, obviously,
Speaker 2: everyone wants to surpass expectations when it comes to the
Speaker 2: reported numbers. But I did wonder if there was ever
Speaker 2: a trend of companies under promising so that they can
Speaker 2: then over deliver.
Speaker 1: Yeah, I think that that has historically been the case,
Speaker 1: and there's still somewhat under promising, but the analyst community
Speaker 1: has still been so pessimistic and undershooting reality that guidance,
Speaker 1: even though they're underpromising and over delivering, their under promises
Speaker 1: are still above analyst consensus.
Speaker 2: Wow.
Speaker 1: And I think that that's largely down to tech and
Speaker 1: this phenomenon that has occurred in the tech industry with
Speaker 1: respect to AI and the mag seven earners. When we
Speaker 1: look at guidance, the vast majority of guidance comes from
Speaker 1: tech and consumer discretionary sectors anyway. So we get a
Speaker 1: really good feel from companies on what's happening with the
Speaker 1: consumer outlook, the global consumer out look. We get a
Speaker 1: pretty good feel from companies what's happening with global tech,
Speaker 1: and those industries have been quite dominant in the index.
Speaker 1: So I think that's what's happening. But it is fascinating
Speaker 1: to see how analysts really just have not jumped on
Speaker 1: the bandwagon of optimism, even though companies continually tell them
Speaker 1: they should be more optimistic.
Speaker 2: When we think about how the reaction comes to the
Speaker 2: reports as we get them, is there room for nuance?
Speaker 2: Can you beat on one metric, disappoint on another and
Speaker 2: kind of end up with a positive share price reaction
Speaker 2: in the end?
Speaker 1: Oh? Absolutely, And you can also have the reverse and
Speaker 1: that ends up being where the options market really comes
Speaker 1: in handy. Is oftentimes what's priced in a stock is
Speaker 1: not actually reflected in analyst expectations or guidance at all.
Speaker 1: And we saw this emerge a little bit in the
Speaker 1: second quarter. If you recall, going into that July reporting season,
Speaker 1: expectations were very high for tech companies at large, in
Speaker 1: particular AI related industries. Across the board. Tech commpanies beat
Speaker 1: those expectations. They even guided for higher growth going forward,
Speaker 1: but in many cases the stocks did not perform particularly
Speaker 1: well in response to earnings. And that's because there was
Speaker 1: a bunch of nuance in the detail. And that detail
Speaker 1: really with that sector was a regarding margin and the
Speaker 1: potential sustainability of margin recovery. That has emerged as a
Speaker 1: powerful driver of earnings growth in that segment for the
Speaker 1: last year and a half or so, a company started
Speaker 1: talking about, well, we're still going to beat expectations, we
Speaker 1: still see very strong growth, but we might have to
Speaker 1: spend a little bit more than we had anticipated. We're
Speaker 1: really adjusting our spending plans, we're really assessing digging a
Speaker 1: bit deeper into the spending outlook, and that created a
Speaker 1: lot of nervousness in the market. So you can absolutely
Speaker 1: have these key issues that emerge that are oftentimes very
Speaker 1: nuanced or oftentimes extremely detailed. Even though they beat expectations,
Speaker 1: raised guidance, generally had a very positive earning season, that
Speaker 1: commentary can so drive stock prices. This is a big
Speaker 1: part of the reason why we also track sentiment. We
Speaker 1: have a transcript analysis tool that we've developed. We track
Speaker 1: sentiment toward keywords, sentiment in management commentary as well as
Speaker 1: in analyst questions toward keywords because you can often get
Speaker 1: enormous stock price reactions and a lot of signals from
Speaker 1: the words and the reactions to the words.
Speaker 2: Gina Mark Adam's chief equity strategist a Bloomberg Intelligence, thank
Speaker 2: you so much for joining us and giving us an
Speaker 2: insight into how these sorts of forecasts are put together
Speaker 2: for more explanations like this from our team of twenty
Speaker 2: seven hundred journalists and analysts around the world. Search for
Speaker 2: quick take on the Bloomberg website or Bloomberg Business app.
Speaker 2: I'm Stephen Carol. This is here's why. I'll be back
Speaker 2: next week with more. Thanks for listening.