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As you might know, Vinci Compass Investments Ltd. (NASDAQ:VINP) last week released its latest quarterly, and things did not turn out so great for shareholders. Results showed a clear earnings miss, with R$241m revenue coming in 4.4% lower than what the analystsexpected. Statutory earnings per share (EPS) of R$0.74 missed the mark badly, arriving some 47% below what was expected. The analysts typically update their forecasts at each earnings report, and we can judge from their estimates whether their view of the company has changed or if there are any new concerns to be aware of. With this in mind, we’ve gathered the latest statutory forecasts to see what the analysts are expecting for next year.

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Following the latest results, Vinci Compass Investments’ five analysts are now forecasting revenues of R$1.14b in 2026. This would be a notable 19% improvement in revenue compared to the last 12 months. Statutory earnings per share are predicted to soar 105% to R$5.67. Yet prior to the latest earnings, the analysts had been anticipated revenues of R$1.14b and earnings per share (EPS) of R$4.84 in 2026. Although the revenue estimates have not really changed, we can see there’s been a decent improvement in earnings per share expectations, suggesting that the analysts have become more bullish after the latest result.

Check out our latest analysis for Vinci Compass Investments

The consensus price target was unchanged at US$13.39, implying that the improved earnings outlook is not expected to have a long term impact on value creation for shareholders. It could also be instructive to look at the range of analyst estimates, to evaluate how different the outlier opinions are from the mean. Currently, the most bullish analyst values Vinci Compass Investments at US$14.43 per share, while the most bearish prices it at US$11.96. With such a narrow range of valuations, the analysts apparently share similar views on what they think the business is worth.

Of course, another way to look at these forecasts is to place them into context against the industry itself. The period to the end of 2026 brings more of the same, according to the analysts, with revenue forecast to display 15% growth on an annualised basis. That is in line with its 16% annual growth over the past five years. By contrast, our data suggests that other companies (with analyst coverage) in a similar industry are forecast to see their revenues grow 6.9% per year. So it’s pretty clear that Vinci Compass Investments is forecast to grow substantially faster than its industry.

The biggest takeaway for us is the consensus earnings per share upgrade, which suggests a clear improvement in sentiment around Vinci Compass Investments’ earnings potential next year. Happily, there were no major changes to revenue forecasts, with the business still expected to grow faster than the wider industry. The consensus price target held steady at US$13.39, with the latest estimates not enough to have an impact on their price targets.

With that said, the long-term trajectory of the company’s earnings is a lot more important than next year. We have forecasts for Vinci Compass Investments going out to 2027, and you can see them free on our platform here.

And what about risks? Every company has them, and we’ve spotted 4 warning signs for Vinci Compass Investments (of which 1 is a bit concerning!) you should know about.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com.

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

When then Tropical Storm Melissa was churning south of Haiti, Philippe Papin, a National Hurricane Center (NHC) meteorologist, had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the coast of Jamaica. No NHC forecaster had ever issued such a bold forecast for rapid strengthening.

But Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Forecasters at the NHC are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion and on social media that Google’s model was a primary reason he was so confident: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5. While I am not ready to forecast that intensity yet given the track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over very warm ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Google DeepMind is the first AI model dedicated to hurricanes, and now the first to beat traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the disaster, possibly saving lives and property.

Google DeepMind has been making weather forecasts for a few years now, and the parent forecast system from which the new hurricane model is derived also performed spectacularly well in diagnosing large-scale weather patterns last year.

Google’s model works by spotting patterns that traditional time-intensive physics-based weather models may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” Michael Lowry, a former NHC forecaster, said.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require some of the biggest supercomputers in the world.

Still, the fact that Google’s model could outperform previous gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

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“I’m impressed,” said James Franklin, a retired NHC forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean. It also struggled with Typhoon Kalmaegi – which made landfall in the Philippines on Monday.

In the coming offseason, Franklin said he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess exactly why it is coming up with the its answers.

“The one thing that nags at me is that while these forecasts seem to be really, really good, the output of the model is kind of a black box,” said Franklin.

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its methods – unlike nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them. While Google has made top-level output of DeepMind publicly available in real time on a dedicated website, its methods have still largely been hidden.

Google is not alone in starting to use AI to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network, which has recently been downsized by the Trump administration.