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