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RACE

Ferrari NV Price

Closed
RACE
$334,57
-$3,71(-%1,09)

*Data last updated: 2026-04-08 03:02 (UTC+8)

As of 2026-04-08 03:02, Ferrari NV (RACE) is priced at $334,57, with a total market cap of $59,31B, a P/E ratio of 35,17, and a dividend yield of %1,04. Today, the stock price fluctuated between $330,45 and $337,36. The current price is %1,24 above the day's low and %0,82 below the day's high, with a trading volume of 602,60K. Over the past 52 weeks, RACE has traded between $330,45 to $341,86, and the current price is -%2,13 away from the 52-week high.

RACE Key Stats

Yesterday's Close$338,28
Market Cap$59,31B
Volume602,60K
P/E Ratio35,17
Dividend Yield (TTM)%1,04
Dividend Amount$4,16
Diluted EPS (TTM)9,00
Net Income (FY)$1,59B
Revenue (FY)$7,14B
Earnings Date2026-05-05
EPS Estimate2,64
Revenue Estimate$2,10B
Shares Outstanding175,33M
Beta (1Y)0.601
Ex-Dividend Date2026-04-21
Dividend Payment Date2026-05-05

About RACE

Ferrari N.V., through its subsidiaries, designs, engineers, produces, and sells luxury performance sports cars. The company offers sports, GT, and special series cars; limited edition hyper cars; one-off and track cars; and Icona cars. It also provides racing cars, and spare parts and engines, as well as after sales, repair, maintenance, and restoration services for cars. In addition, the company licenses its Ferrari brand to various producers and retailers of luxury and lifestyle goods; Ferrari World, a theme park in Abu Dhabi, the United Arab Emirates; and Ferrari Land Portaventura, a theme park in Europe. Further, it provides direct or indirect finance and leasing services to retail clients and dealers; manages racetracks, as well as owns and manages two museums in Maranello and Modena, Italy; and develops and sells a line of apparel and accessories through its monobrand stores. As of December 31, 2021, it had a total of 30 retail Ferrari stores, including 14 franchised stores and 16 owned stores. The company also sells its products through a network of 172 authorized dealers operating 191 points of sale worldwide, as well as through its website, store.ferrari.com. Ferrari N.V. was founded in 1947 and is headquartered in Maranello, Italy.
SectorConsumer Cyclical
IndustryAuto - Manufacturers
CEOBenedetto Vigna
HeadquartersMaranello,MO,IT
Official Websitehttps://www.ferrari.com
Employees (FY)5,71K
Average Revenue (1Y)$1,24M
Net Income per Employee$279,27K

Learn More about Ferrari NV (RACE)

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Ferrari NV (RACE) FAQ

What's the stock price of Ferrari NV (RACE) today?

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Ferrari NV (RACE) is currently trading at $334,57, with a 24h change of -%1,09. The 52-week trading range is $330,45–$341,86.

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What is the price-to-earnings (P/E) ratio of Ferrari NV (RACE)? What does it indicate?

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2026-03-25 10:30

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Hot Posts About Ferrari NV (RACE)

SpeculativeAnalyst

SpeculativeAnalyst

1 hours ago
You should listen to Brother Sun, but don't touch the projects he endorses. Sun Yuchen's 2016 prophecy video directly overturned many people's wealth logic that year. He said not to follow the trend of buying houses and cars, split 1 million into 5 parts, and go all in on Nvidia, Tesla, BTC, Tencent, and LC. In 2016, I was still working hard at a big company, just bought a commuter car, saving up for a down payment. Back then, everyone's idle money was either invested in P2P or focused on real estate. Eventually, P2P collapsed in a mess, and all the targets he mentioned were "controversial": BTC was criticized as pyramid schemes, Tesla was on the brink of bankruptcy, Nvidia was just a small company making gaming graphics cards, Tencent's stock price hadn't reached 200 yuan, and LC was barely known. Who would have thought that this would last nearly ten years? Up to now, BTC has risen 136 times, Tesla 32 times, Nvidia a staggering 155 times, Tencent steadily up 4 times, and even though LC lost 30%, 1 million turned into 66 million, a 66-fold return! This level of insight and penetration is truly impressive. Later, Sun Yuchen further embedded "disruption" into his bones: founding TRON, the 33 million-dollar lunch with Buffett that was suddenly canceled, bidding 45 million for a concept art banana at 2.5 yuan and eating it publicly, going to space, ringing the Nasdaq bell—each event constantly pushing the boundaries of public perception. Netizens summarized it perfectly: "You should listen to Brother Sun, but don't touch his projects," precisely capturing his level of insight and controversy. Recently, he made a new prediction: "Short-term chip shortage, long-term energy shortage, always a storage shortage." When viewed in 2025, every word hits the trend — the essence of AI computing power competition is an electricity race, with leading chip stocks soaring continuously, storage demand exploding with data, and energy becoming the fundamental support for all technological development. What was once dismissed as "crazy talk" has become a wealth myth. What opportunities might his new predictions hide? Do you think the three major tracks of chips, energy, and storage are worth investing in? #Gate广场四月发帖挑战 $TRX
TRX-%0,49
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CodeZeroBasis

CodeZeroBasis

1 hours ago
American AI has started speaking in the booming baritone of national purpose. But it’s doing a lot of flag-waving for an industry that keeps letting Chinese models into the building. The U.S.’ patriotic sales pitch is everywhere now — “global AI dominance,” “national mission,” “strategic race,” “democratic” values, and all the usual chest-thumping language that the AI industry has started borrowing from Washington. But behind the red, white, and blue branding, developers and platforms keep making a different calculation: Chinese models are good, cheap, open, and increasingly hard to avoid. While the public face of AI in the U.S. still looks comfortably domestic, more Chinese technology keeps slipping into the guts of the machine — the coding tools, the cloud marketplaces, and the parts of the stack most people never see. The stars-and-stripes rhetoric is getting harder to square. Patriotic branding is easy. Patriotic procurement is where things can get ugly. Washington has already been warned that this growing migration isn’t some niche side plot for engineers with tabs open on Hugging Face. In mid-March, the U.S.-China Economic and Security Review Commission warned that Chinese open-weight models have become hard to wave away. The report said that China has gone “all in” on open-source AI, that widespread adoption is feeding faster iteration, and that the result is creating “alternative pathways to AI leadership.” The open ecosystem, the report said, “enables ​China to innovate close to the frontier despite significant compute constraints” — and now “Chinese labs have narrowed performance gaps with top Western large language models.”  That’s a lot of fancy bureaucrat language for a very simple problem: The U.S. keeps grandstanding about a national mission while China keeps shipping a product that travels well.  China’s open approach has essentially created a feedback loop where adoption drives iteration and then more adoption — a “self-reinforcing competitive advantage,” as the USCC said; some estimates now put Chinese open-source models inside around 80% of U.S. AI startups. A Stanford HAI’s DigiChina brief says that Chinese-made open-weight models are now “unavoidable” in the competitive AI landscape and are increasingly being adopted in the U.S. Washington is selling sovereignty. The market is buying whatever works. **Chinese models are already getting into the stack** ----------------------------------------------------- The easiest way to miss what’s happening is to stare at the consumer apps and congratulate yourself on spotting the obvious. On that surface, the U.S. still gets to feel nice and sovereign. SSRS said this month that 52% of Americans use AI platforms weekly, with ChatGPT at 36%, Gemini at 26%, and Copilot at 14%. Similarweb’s U.S. rankings still lean heavily American, too, putting ChatGPT, Gemini, Claude, Grok, and OpenAI in the top five. The storefront looks domestic enough to keep the branding neat and the nerves calm. The more consequential shift is happening backstage, where engineers pick base models, companies choose tooling, and procurement decisions turn into architecture before anybody bothers to call them strategy. According to Hugging Face, China has surpassed the U.S. in both monthly and overall downloads on its platform, with Chinese models accounting for 41% of downloads over the past year. Stanford HAI’s DigiChina brief says that between August 2024 and August 2025, Chinese open-model developers made up 17.1% of all Hugging Face downloads, slightly ahead of U.S. developers at 15.8%. Last week, seven of the 10 most popular models on OpenRouter were Chinese. OpenRouter’s 100 trillion-token study found that Chinese open-source models rose from a negligible base in late 2024 to nearly 30% of total usage in some weeks, averaging about 13% of weekly token volume over the year it studied. DeepSeek was the single largest open-source contributor by volume on the platform, with Qwen ranked second. The work itself is changing, too. OpenRouter says Chinese open models are no longer mainly for roleplay and hobbyist messing around; programming and technology together now make up a combined 39% of Chinese open-source use on the platform. Cursor, one of the hottest American AI companies around, admitted this month that its Composer 2 coding model was, in a licensed partnership, built on top of Moonshot AI’s Kimi K2.5 before layering on its own training. Moonshot, one of China’s most promising AI startups, is based in Beijing — and valued at around $18 billion, more than quadrupling its value in three months. “Seeing our model integrated effectively through Cursor’s continued pretraining & high-compute RL training is the open model ecosystem we love to support,” Moonshot wrote on X $TWTR 0.00%. Cursor executives said that Kimi performed best in the company’s evaluations, and Business Insider reported that the resulting product came in at about one-tenth the cost of Anthropic’s Opus 4.6.  Companies ranging from Airbnb $ABNB -1.45% to Siemens have openly used Chinese models. So AI startup darlings and established companies alike are increasingly passing over expensive proprietary U.S. models in favor of lower-cost Chinese ones that have closed much of the performance gap. The market has started treating model nationality as secondary — and largely irrelevant — to whether the thing works well, ships fast, and costs less. **“Open” has become a geopolitical business model** --------------------------------------------------- The White House itself has said that open-source and open-weight systems matter because startups need flexibility and because companies with sensitive data can’t always ship to a closed-model vendor. That’s true. That’s also exactly why Chinese open models have become such a headache for the American AI nationalism story. The U.S. government’s recognition arrives after years where American AI prestige became bound up with closed APIs, elite model subscriptions, and the idea that the best systems should be tightly controlled by a handful of companies. That approach may still win at the very frontier, but it’s less obviously suited to winning the layer underneath, where developers pick and choose what they can actually afford to use.  Beijing has increasingly framed open-weight AI as part of a broader diplomatic and commercial pitch — a model of shared technological development contrasted against U.S. export controls, supply-chain restrictions, and closed systems. Open models as a soft-power product. They tell countries that Chinese AI is modifiable and not locked behind an American API tollbooth. Stanford researchers have warned that broad adoption of Chinese open-weight models could reshape global “reliance patterns,” creating new technological dependencies even when the model weights themselves are downloadable. Alibaba’s Qwen family has built the largest model ecosystem on Hugging Face, with more than 113,000 derivative models, or more than 200,000 if you count everything tagged Qwen — surpassing Meta $META +0.35%’s Llama in cumulative downloads on the platform. RAND found in January that traffic to China-based LLMs had jumped 460% in two months and that Chinese models’ global market share had risen from 3% to 13% over that stretch. RAND also said Chinese models — such as DeepSeek, Qwen, and Zhipu’s ChatGLM — can run about one-sixth to one-fourth the cost of U.S. rivals. That’s a nasty combination for any American company trying to sell patriotic virtue at premium pricing. The old story had America building the tools and the rest of the world renting access. The newer one has Chinese labs becoming the substrate for tools that may still wear American branding on the surface. More than a dozen Chinese organizations are openly releasing powerful models. Hugging Face says the number of repositories from popular Chinese organizations exploded in 2025, with ByteDance and Tencent sharply increasing releases and firms that once leaned closed moving toward open releases. China has been shipping a coherent theory of spread. The U.S. has been shipping a mixed economy of premium closed models, open-weight branding, and internal arguments about what “open” even means. The U.S.’ open field is split among open-weight branding, genuinely open research, lightweight portable families, and agent-focused stacks — see: Meta’s open-weight-but-restricted Llama, Ai2’s genuinely open OLMo line, Google $GOOGL +1.82%’s lighter Gemma family, NVIDIA’s agentic stack — which makes the ecosystem stronger in spots but less unified as a doctrine. Even China’s own market has started treating openness less as an ideology than as a go-to-market plan. In February, Baidu — long one of the loudest defenders of closed models — said it would make its next-generation Ernie model open-source, a major strategic reversal. DeepSeek had upended the sector, and Baidu’s CEO said opening things up would help the technology spread faster. “Open” in this race increasingly means scalable distribution, faster adoption, and broader developer lock-in. **U.S. cloud giants are normalizing Chinese models** ---------------------------------------------------- It would be one thing if Chinese open models were still living out on the internet as vaguely exotic artifacts for hobbyists. In that case, the patriotism problem would be manageable. But they aren’t. The hyperscalers have brought them inside.  Amazon $AMZN +0.46% Bedrock says it supports more than 100 foundation models, including DeepSeek, Moonshot AI, MiniMax, and OpenAI. AWS has also rolled out specific DeepSeek and Qwen offerings, and its marketing around DeepSeek is enterprise-grade security, unified infrastructure, and customer data that “is not shared with model providers.” Microsoft $MSFT -0.16% is doing the same thing in a tidier corporate dialect. Azure Foundry’s catalog includes DeepSeek and Moonshot’s Kimi among the models sold directly by Azure, and Microsoft’s own Foundry updates have touted Kimi’s reasoning chops as part of the platform’s expanding lineup. Foreign model in, respectable enterprise product out. The geopolitical edge gets sanded down by procurement convenience, unified billing, and the general corporate desire to pretend every uncomfortable choice is merely a feature. A Chinese open model inside an American cloud, billed on an American invoice, wrapped in American enterprise controls, stops looking like a geopolitical event and starts looking like procurement.  Google Cloud’s Vertex AI has gone down the same road. Its DeepSeek docs say the models are available as fully managed, serverless APIs, and Google explicitly recommends pairing DeepSeek R1 with Model Armor for production safety. Elsewhere in Vertex AI, Google lists open models with global endpoint support that include DeepSeek, Kimi, MiniMax, Qwen, and GLM right alongside OpenAI’s gpt-oss models. Any geopolitical edge gets sanded down by the product design itself: same console, same endpoint logic, same managed-service vocabulary, same enterprise reassurances.  Nvidia $NVDA +0.26% lists DeepSeek in its model catalog. Databricks has joined the party, too. This month, it put Qwen3-Embedding-0.6B into public preview for retrieval and agent workloads, pitching it as a state-of-the-art multilingual embedding model optimized for vector search and AI agents. That’s how dependencies settle in. One team adopts it for search. Another team plugs it into agents. A few quarters later, the strategic problem has release notes and a renewal cycle.  There are two different China problems hiding in the AI story. One is the Chinese-hosted app problem. DeepSeek’s privacy policy says it directly collects, processes, and stores personal data in the People’s Republic of China. The other is the Chinese-origin model problem — weights and model families that get pulled into U.S. clouds, U.S. products, and U.S. workflows. A “national” project starts looking a lot less national when its most useful parts keep showing up from somewhere else. American AI wants the pageantry of sovereignty and the convenience of a global shopping aisle. It wants Washington to treat it like a national champion and developers to treat every foreign model like a harmless bargain. But markets are funny that way. They keep buying what works. Running an open model locally or on trusted infrastructure can mitigate some data and governance risks. That’s why the hyperscalers matter here. They turn a politically fraught dependency into something that feels manageable and corporate. The result is that many enterprise buyers can have Chinese model performance without the unnerving part of feeling as though they are leaving the American stack. That leaves the U.S. in a strange position. It still has enormous advantages in chips, cloud infrastructure, capital markets, and top-end frontier labs. But the country’s political language around AI keeps assuming that technical leadership will naturally translate into downstream loyalty. It won’t. Not in open models — and not in software generally. Developers are promiscuous. Procurement teams are unsentimental. Cloud platforms are agnostic right up until the invoice clears. If Washington wants “American values” to matter in AI purchasing, it’ll need more than speeches about bias and dominance. It’ll need American models that are open enough, cheap enough, and ubiquitous enough that choosing them doesn’t feel like a patriotic sacrifice. Right now, the market seems increasingly unwilling to pay that premium. 📬 Sign up for the Daily Brief ------------------------------ ### Our free, fast and fun briefing on the global economy, delivered every weekday morning. Sign me up
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LidoStakeAddict

LidoStakeAddict

1 hours ago
Been thinking about where the real opportunities are in this AI boom, and honestly most people are missing something pretty obvious. Everyone's focused on the AI race right now. All the hyperscalers throwing billions at compute capacity, the GPU shortage, all that noise. But there's actually a bigger play happening in parallel that hardly anyone's talking about seriously yet: quantum computing. Here's the thing though. If you're looking for a quantum AI stock that actually bridges both trends, you're probably thinking about pure-play quantum companies. But what if I told you the smartest move might be going with a company that's already dominating AI AND making serious moves in quantum at the same time? That company is Alphabet. And I'm not just saying this because it's obvious. Let's start with what they've already built. Google Search is still absolutely crushing it, even with all the AI competition concerns. Q2 showed 12% growth year-over-year. Not declining. Not struggling. Growing. And a lot of that is because they integrated their Gemini AI directly into search results. That generative AI summary at the top of every result? That's actually one of the most-used large language models in the world right now. The training data advantage alone is massive. Gemini consistently ranks among the best-performing AI models out there. Alphabet's basically won the AI accessibility game without cannibalizing their core business. That's actually harder than it sounds. But here's where it gets interesting. Last December, Alphabet announced their Willow quantum chip. And it did something wild: completed a computational task that would take traditional computers 10 septillion years. Now, that test was specifically designed to prove quantum viability, so take it with a grain of salt. But it proves they're actually making progress, not just talking about it. Why does Alphabet care about building their own quantum chip? Simple. Right now they're buying GPUs from Nvidia and custom accelerators from Broadcom. These are middlemen. Middlemen mean markup. Middlemen mean dependency. If Alphabet can build quantum computing in-house, they cut out the middleman entirely and suddenly their AI infrastructure becomes way more efficient. They could also rent out quantum capabilities through their cloud business. That's a whole new revenue stream. Think about it: an AI leader that's also developing quantum computing capability. That's not just a good quantum AI stock play. That's potentially a generational advantage if they execute. Most investors are betting on pure quantum plays, but those are still years away from real utility. Alphabet's already a cash machine in AI with the resources to dominate quantum too. That's a different risk profile entirely. The combination of these two trends playing out over the next decade in a single company? That's the kind of conviction play I'm comfortable with. Not financial advice obviously, but worth thinking about seriously if you're building a tech portfolio right now.
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