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ABNB

Prezzo Airbnb

Closed
ABNB
$127,76
+$1,75(+1,38%)

*Data last updated: 2026-04-08 00:42 (UTC+8)

As of 2026-04-08 00:42, Airbnb (ABNB) is priced at $127,76, with a total market cap of $74,92B, a P/E ratio of 33,13, and a dividend yield of 0,00%. Today, the stock price fluctuated between $123,46 and $128,39. The current price is 3,48% above the day's low and 0,49% below the day's high, with a trading volume of 2,66M. Over the past 52 weeks, ABNB has traded between $110,44 to $143,87, and the current price is -11,19% away from the 52-week high.

ABNB Key Stats

Yesterday's Close$126,81
Market Cap$74,92B
Volume2,66M
P/E Ratio33,13
Dividend Yield (TTM)0,00%
Diluted EPS (TTM)4,09
Net Income (FY)$2,51B
Revenue (FY)$12,24B
Earnings Date2026-05-07
EPS Estimate0,30
Revenue Estimate$2,61B
Shares Outstanding590,81M
Beta (1Y)1.16

About ABNB

Airbnb, Inc., together with its subsidiaries, operates a platform that enables hosts to offer stays and experiences to guests worldwide. The company's marketplace model connects hosts and guests online or through mobile devices to book spaces and experiences. It primarily offers private rooms, primary homes, or vacation homes. The company was formerly known as AirBed & Breakfast, Inc. and changed its name to Airbnb, Inc. in November 2010. Airbnb, Inc. was founded in 2007 and is headquartered in San Francisco, California.
SectorConsumer Cyclical
IndustryTravel Services
CEOBrian Chesky
HeadquartersSan Francisco,CA,US
Official Websitehttps://www.airbnb.com
Employees (FY)8,20K
Average Revenue (1Y)$1,49M
Net Income per Employee$306,21K

Airbnb (ABNB) FAQ

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Airbnb (ABNB) is currently trading at $127,76, with a 24h change of +1,38%. The 52-week trading range is $110,44–$143,87.

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CodeZeroBasis

CodeZeroBasis

23 ore fa
The AI benchmark race has a winner. It just isn't you. Every few months, a new model drops and a new leaderboard reshuffles. Labs compete to out-reason, out-code, and out-answer each other on tests designed to measure machine intelligence. The coverage follows. So does the funding. What gets less attention is whether any of this is inevitable. The benchmarks, the arms race, the framing of AI as either salvation or catastrophe — these are choices, not laws of physics. They reflect what the industry decided to optimize for, and what it decided to fund. Technology that will take decades to pan out in ordinary, useful ways doesn't raise billions this quarter. Extreme narratives do. Some researchers think the goal is simply wrong. Not that AI isn't important, but that important doesn't have to mean unprecedented. The printing press changed the world. So did electricity. Both did it gradually, through messy adoption, giving societies time to respond. If AI follows that pattern, the right questions aren't about superintelligence. They're about who benefits, who gets harmed, and whether the tools we're building actually work for the people using them. Plenty of researchers have been asking those questions from very different directions. Here are three of them. **Useful, not general** ----------------------- Ruchir Puri has been building AI at IBM $IBM -0.57% since before most people had heard of machine learning. He watched Watson beat the world's best Jeopardy players in 2011. He's watched several cycles of hype crest and recede since. When the current wave arrived, he had a simple test for it: is it useful? Not impressive. Not general. Useful. "I don't really care about artificial general intelligence," he says. "I care about the useful part of it." That framing puts him at odds with much of the industry's self-image. The labs racing toward AGI are optimizing for breadth, building systems that can do anything, answer anything, reason about anything. Puri thinks that's the wrong target, and he has a benchmark he'd like to see the industry actually try to reach. The human brain lives in 1,200 cubic centimeters, consumes 20 watts, the energy of a light bulb, and, as Puri points out, runs on sandwiches. A single Nvidia $NVDA +0.14% GPU consumes 1,200 watts, 60 times more than the entire brain, and you need thousands of them in a giant data center to do anything meaningful. If the brain is the benchmark, the industry isn't close to efficient. It's going in the wrong direction. His alternative is what he calls hybrid architecture: small, medium, and large models working together, each assigned to the task it handles best. A large frontier model does the complex reasoning and planning. Smaller, purpose-built models handle execution. A task as simple as drafting an email doesn't need a system trained on half the internet. It needs something fast, cheap, and focused. Every nine months or so, Puri notes, the small model of the previous generation becomes roughly equivalent to what was considered large. Intelligence is getting cheaper. The question is whether anyone is building for that reality. The approach has real-world backing. Airbnb $ABNB +1.49% uses smaller models to resolve a significant portion of customer service issues faster than its human representatives can. Meta $META -0.25% doesn't use its biggest models to deliver ads so it distills that knowledge into smaller ones built for that task alone. The pattern is consistent enough that researchers have started calling it a knowledge assembly line: data flows in, specialized models handle discrete steps, something useful comes out the other end. IBM has been building that assembly line longer than most. A hybrid agent combining models from several companies has shown a 45% productivity improvement across a large engineering workforce. Systems running on smaller, purpose-built models now help the engineers who keep 84% of the world's financial transactions processing get the right information at the right time. These aren't flashy applications. They're also not failing. None of them require a system that can write poetry or solve your kid's math homework. They require something narrower and, for that reason, more trustworthy. A model trained to do one thing well knows when a question falls outside its scope. It says so. That calibrated uncertainty, knowing what you don't know, is something the big frontier models still struggle with. "I want to build agents and systems for those processes," Puri says. "Not something that answers two million things." Tools, not agents ----------------- Ben Shneiderman has a simple test for whether an AI system is well designed. Does the person using it feel like they did something, or does it feel like something was done for them? The distinction matters more than it sounds. Shneiderman, a computer scientist at the University of Maryland who helped lay the foundations for modern interface design, has spent decades arguing that the goal of technology should be to amplify human ability, not replace it. Good tools build what he calls user self-efficacy, or the confidence that comes from knowing you can do something yourself. Bad ones quietly transfer that agency somewhere else. He thinks most of the AI industry is building bad tools, and he thinks the agentic turn makes it worse. The pitch for AI agents is that they act on your behalf, handling tasks end to end without your involvement. To Shneiderman, that's not a feature. It's the problem. When something goes wrong, and it will, who is responsible? When something goes right, who learned anything? The trap he's been fighting against for a long time has a name. Anthropomorphism, the impulse to make technology seem human, is what keeps winning, and what keeps failing. In the 1970s, banks experimented with ATMs that greeted customers with "How can I help you?" and gave themselves names like Tilly the Teller and Harvey the World Banker. They were replaced by machines that showed you three options. Balance, cash, deposit. Utilization shot up. Citibank had 50% higher usage than its competitors. People didn't want a synthetic relationship. They wanted to get their money. The same pattern has repeated across decades, through Microsoft $MSFT -0.16% Bob, the AI pin from Humane, and waves of humanoid robots. Each time, the anthropomorphic version fails and gets replaced by something more tool-like. Shneiderman calls it a zombie idea. It doesn't die, it just keeps coming back. What's different now is scale and sophistication. The current generation of AI is genuinely impressive, he acknowledges, startlingly so. But impressive and useful aren't the same thing, and systems designed to seem human, to say I, to simulate relationship, are optimizing for the wrong quality. The question he wants designers to ask is simpler: does this give people more power, or less? "There is no I in AI," he says. "Or at least, there shouldn't be." **People, not benchmarks** -------------------------- Karen Panetta has a simple answer for why AI development looks the way it does. Follow the money. Panetta, a professor of electrical and computer engineering at Tufts University and an IEEE fellow, studies AI ethics and has a clear view of where the technology should be going. Assistive pets for Alzheimer's patients, adaptive learning tools for children with different cognitive styles, smart home monitoring for elderly people aging in place. The technology to do this well, she says, largely exists. The investment doesn't. "The humans don't care about benchmarks," she says. "They care about, does it work when I buy it, and is it going to really make my life easier?" The problem is that the people who would benefit most from well-designed assistive AI are also the least compelling pitch to a venture capitalist. A system that transforms manufacturing processes, reduces workplace injuries, and cuts healthcare costs for a company's employees has an obvious return. A robotic companion that keeps an Alzheimer's patient calm and connected requires a different kind of math entirely. So the money goes where the money goes, and the populations with the most to gain keep waiting. What's changed, Panetta says, is that the expensive engineering problems are finally being solved at scale. Sensors are cheaper. Batteries are lighter. Wireless protocols are ubiquitous. The same investment that built industrial robots for factory floors has quietly made consumer robotics viable in a way it wasn't five years ago. The path from warehouse to living room is shorter than it looks. But she has a concern that the excitement around that transition tends to skip over. Physical robots have natural constraints. You know the force limits. You know the kinematics. You can anticipate, simulate, and design around how they'll fail. Generative AI doesn't come with those guarantees. It's non-deterministic. It hallucinates. Nobody has fully mapped what happens when you put it inside a system that is physically present in the home of someone with dementia, or a child who can't identify when something has gone wrong. She's seen what happens when a sensor gets dirty and a robot loses its spatial awareness. She's thought about what it means to build something that learns intimate details about a person's life, their routines, their cognitive state, their moments of confusion, and then acts on that information autonomously. The fail-safes, she says, haven't kept up. "I'm not worried about the robot," she says. "I'm worried about the AI." 📬 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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SelfRugger

SelfRugger

04-06 14:59
This is a paid press release. Contact the press release distributor directly with any inquiries. Waseem Limbada Consultant, Airbnb Advocates for Athlete "Plan B" Education and Capital Literacy =============================================================================================== Waseem Limbada Consultant, Airbnb Fri, February 27, 2026 at 2:00 AM GMT+9 4 min read In this article: * StockStory Top Pick ABNB +3.23% _**Dallas-based entrepreneur Waseem Limbada Consultant, Airbnb is calling for stronger business education and transition planning for current and former athletes.**_ **DALLAS, TX / ACCESS Newswire / February 26, 2026 / **Waseem Limbada Consultant, Airbnb is raising awareness about a growing issue that affects thousands of athletes each year: what happens after the game ends. Waseem Limbada Consultant, Airbnb A former state and national champion basketball player, Limbada understands firsthand how quickly an athletic career can shift. After declining a professional contract overseas just before COVID, he transitioned into finance and later built multiple eight-figure consulting and real estate businesses. Today, he is using his platform to advocate for what he calls a "Plan B" mindset for athletes at every level. "I've seen too many talented athletes struggle once the season ends," Limbada says. "We train for years to win on the court, but very few people train us to win in business or life after sports." **The Transition Problem Facing Athletes** According to NCAA data, fewer than 2% of college athletes go on to compete at the professional level. Even among those who do, most professional sports careers last only a few years. The average NBA career spans approximately 4 to 5 years. In many sports, it is even shorter. At the same time, research from the NCAA and independent sports foundations shows that many former athletes report difficulty adjusting to career changes, identity loss, and financial stress after leaving competitive play. For Limbada, those statistics are not abstract. "Athletics gives you discipline, structure, and mental toughness," he says. "But if you don't pair that with financial education and business skills, you're leaving your future to chance." **From Athlete to CEO** After stepping away from a potential professional basketball career, Limbada became a Certified Financial Advisor with a Fortune 100 company. By age 23, he had built and led a national organization of more than 15,000 members across 23 countries. He later launched a six-figure car rental business and scaled a short-term rental portfolio from one unit to 100 properties in under three years. Across his consulting firms, he has helped clients secure more than $20 million in funding and supported over 1,000 Airbnb and short-term rental launches. But he says the real lesson is not about scale. It is about preparation. "Championships are won in practice," Limbada explains. "Business is no different. You have to develop skills before you need them." Story continues **Why Capital Literacy Matters** Beyond athlete transition, Limbada is also advocating for stronger capital literacy among young entrepreneurs and professionals. A 2023 Federal Reserve survey found that many small business owners cite access to capital as one of their top challenges. At the same time, financial literacy studies continue to show that a large percentage of Americans lack confidence in understanding credit, lending, and long-term planning. "Capital is a tool," Limbada says. "If you understand how it works, you can create leverage. If you don't, it controls you." Through his consulting platforms, Limbada focuses on education around business funding, operational systems, and asset acquisition. He stresses that awareness and preparation are key. "This isn't about hype," he says. "It's about structure. It's about knowing how money moves and how to deploy it with intention." **A Community Built for Athletes** To address the gap, Limbada has launched a free educational community aimed at helping athletes build business skills while they are still competing. The initiative provides exposure to entrepreneurship, strategy, and real-world case studies. It encourages athletes to think beyond contracts and endorsements. "You don't have to wait until your career ends to prepare," Limbada says. "Your Plan B should strengthen your Plan A." He believes the same principle applies to young entrepreneurs. "Skill stacking is power," he adds. "The earlier you learn how business works, the more options you create for yourself." **A Call to Action** Limbada is encouraging athletes, parents, coaches, and young professionals to take proactive steps: * Start learning about business and financial fundamentals early. * Seek mentors outside of sports. * Build income skills that are not tied to physical performance. * Develop systems and discipline that translate beyond competition. "You don't need to abandon your dream," Limbada says. "You just need to build depth around it." He emphasizes that long-term stability starts with personal responsibility. "No one cares about your future more than you do," he says. "If you take ownership early, you change the trajectory of your life." To read the full interview, visit the website here. **About Waseem Limbada Consultant, Airbnb** Waseem Limbada Consultant, Airbnb is a Dallas, Texas-based entrepreneur and CEO focused on capital access, real estate strategy, and business consulting. A former championship basketball player, he transitioned from financial services into entrepreneurship, scaling multiple ventures and supporting over 1,000 short-term rental launches. He is an advocate for athlete transition education and long-term financial literacy. Contact: Info@waseem-limbada.com **SOURCE: **Waseem Limbada Consultant, Airbnb View the original press release on ACCESS Newswire Terms and Privacy Policy Privacy Dashboard More Info
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SelfRugger

SelfRugger

04-06 09:37
二月16 - 20日的期权波动率与财报 ========================================================== 通过 Shutterstock 的 One Photo 的期权交易 Gavin McMaster 周一，2026年2月16日 21:00（GMT+9）3 分钟阅读 本文包含： * StockStory 精选 DASH -0.50% * OXY +1.27% WMT +0.19% BABA -1.89% XEM-USD +0.08% 财报季目前正在放缓，这可能会让一些人感到如释重负。然而，我们仍然有一些重要公司将要在财报日发布，包括沃尔玛（WMT）、阿里巴巴（BABA）、纽蒙特矿业（NEM）、美敦力（MDT）、派洛阿尔托网络（PANW）、DoorDash（DASH）以及西方石油（OXY）都已确定将公布业绩。 在公司发布财报之前，隐含波动率通常较高，因为市场对财报结果不确定。投机者和对冲者会制造对该公司的期权的巨大需求，从而提高隐含波动率，因此也提高期权价格。 ### 来自 Barchart 的更多新闻 * 分析师更看好麦当劳：更高的目标价与预估——这里的 MCD 股票值得买吗？ * 别错过市场行情：获取免费的 Barchart Brief——你午间的股票变动、热门板块与可执行交易想法推送，直接发送到你的收件箱。立即报名！ 在财报公告之后，隐含波动率通常会回落到正常水平。 让我们看一下这些股票的预期区间。要计算预期区间，请查找期权链，并将“平值看跌期权”的价格与“平值看涨期权”的价格相加。使用财报日期之后的**第一个到期日**。虽然这种方法不如基于详细计算那样精确，但它确实是一个相当准确的估计。 **周一** 市场假日 **周二** ET – 3.1% PANW – 8.3% MDT – 4.8% CEG – 5.2% **周三** CVNA – 15.5% OXY – 4.6% DASH – 13.3% **周四** BABA – 4.4% WMT – 5.8% SO – 2.2% NEM – 7.5% **周五** 无重要事项 期权交易者可以利用这些预期涨跌幅来构建交易。看空的交易者可以考虑在预期区间之外卖出看跌看涨价差（bear call spreads）。 看多的交易者可以在预期区间之外卖出看涨看跌价差（bull put spreads），或者针对风险承受能力更高的情况寻找裸卖出看跌期权（naked puts）。 中性交易者可以考虑使用铁鹰（iron condors）。在财报期间交易铁鹰时，最好让空头行权价位于预期区间之外。 在财报期间交易期权时，最好坚持风险是预先定义的策略，并保持仓位规模较小。如果标的股票的涨跌幅大于预期，且该交易遭遇全额亏损，那么它对你投资组合的影响不应超过 1-3%。 **隐含波动率较高的股票** 我们可以使用 Barchart 的 Stock Screener 找到其他隐含波动率较高的股票。 让我们使用以下筛选条件运行股票筛选器： * 总看涨期权成交量：大于 5,000 * 市值：大于 400亿 * IV 排名：大于 50% 该筛选器将产生以下结果，并按 IV 排名从高到低排序。 故事继续 你可以参考这篇文章，了解如何在本财报季找到期权交易的细节。 **上周的财报行情变化** HOOD -8.9% vs 预期 11.7% F +2.1% vs 预期 6.5% KO -1.5% vs 预期 2.9% NET +5.2% vs 预期 13.4% SPOT +14.8% vs 预期 10.4% GILD +5.8% vs 预期 5.5% CSCO -12.3% vs 预期 5.5% VRT +24.5% vs 预期 10.5% APP -19.7% vs 预期 15.5% SHOP -6.7% vs 预期 12.8% MCD +2.7% vs 预期 3.3% COIN +16.5% vs 预期 11.1% ANET +4.8% vs 预期 10.7% ABNB +4.7% vs 预期 8.6% AEM +5.6% vs 预期 6.9% 总体而言，有 15 家中的 9 家仍然落在预期区间内。其中有 15 家中的 10 家在公告后上涨。 **不寻常的期权交易活动** NCLH、AI、MSTR、CVX、UPS 和 DKNG 上周都出现了不寻常的期权交易活动。 下方展示了其他也有不寻常期权交易活动的股票： 请记住，期权存在风险，投资者可能会损失其投资的 100%。本文仅用于教育目的，不构成交易建议。请务必在做出任何投资决策之前进行自己的尽职调查，并咨询你的金融顾问。 _ 在发布当天，Gavin McMaster 并未（无论是直接还是间接）持有本文章中提到的任何证券的头寸。本文中的所有信息与数据仅供信息参考。本文最初发布于 Barchart.com _ 条款 与 隐私政策 隐私面板 更多信息
DASH+11,81%
OXY-1,77%
WMT0,00%
BABA0,00%
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