2026-08-10 Segunda

Notícias de cripto - Página 428

Mantenha-se a par do mercado de cripto. Notícias em tempo real, análises, preços, histórias em alta e análise de especialistas — tudo num só lugar.

U.S. Stock Market Trends: Dow Hits New High, Nasdaq Falls, Whom Did Broadcom's Slap Wake Up?

U.S. Stocks Split: Dow Hits Record High as Nasdaq Slips; Broadcom's Plunge Sparks Rotation On June 4, the U.S. stock market saw a sharp divergence. The Dow Jones surged 875 points (+1.73%) to a record high of 51,561.93, while the Nasdaq Composite edged down 0.09%. The S&P 500 rose 0.41%. The primary catalyst was a sharp sell-off in AI-related chip stocks, led by Broadcom (AVGO). Despite reporting a 143% year-over-year jump in AI semiconductor revenue to $10.8 billion, the company's shares plunged about 14%. This was triggered by its maintained long-term AI revenue target, which failed to meet heightened expectations for a stock that had gained 55% this quarter and traded at a high P/E ratio. The slide dragged down the broader semiconductor sector and the technology板块. Conversely, money rotated into sectors like Healthcare (+3.14%), Financials (+2.67%), and Real Estate (+1.87%). UnitedHealth and Goldman Sachs were major contributors to the Dow's gains. The rotation was attributed to a search for value outside overheated tech names and a slight dip in Treasury yields. In other major news, SpaceX confirmed its IPO for June 12, targeting a record $75 billion raise at a ~$1.75 trillion valuation. Additionally, initial jobless claims rose to a four-month high, adding nuance to the labor market narrative ahead of the key May non-farm payrolls report. The day's action signaled that while the AI growth story remains intact, excessive valuations are prompting a market reassessment. Funds are moving, at least temporarily, from high-flying tech to more defensive and value-oriented sectors. The sustainability of this rotation hinges on upcoming economic data, particularly the jobs report, and the market's absorption of the massive SpaceX IPO.

marsbit06/05 01:04

U.S. Stock Market Trends: Dow Hits New High, Nasdaq Falls, Whom Did Broadcom's Slap Wake Up?

marsbit06/05 01:04

From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

"Old Dogs" Become AI's New Darlings: Revaluing Legacy Infrastructure The AI investment narrative is shifting. Beyond the spotlight on core chipmakers like Nvidia, a new wave of interest is rising for legacy tech companies—Dell, HPE, Nokia, Cisco, Corning, Western Digital—once labeled as slow-growth, outdated stories. This resurgence stems from AI's evolution from model development to real-world deployment, creating massive demand for physical infrastructure. As AI moves into data center construction and enterprise adoption, the focus turns to who can actually build and deliver complex systems. These established players hold decades of experience in supply chains, integration, networking, and enterprise delivery—assets now critical for scaling AI. The revaluation can be grouped into three key infrastructure areas: 1. **Servers & Integration (e.g., Dell, HPE):** They are becoming essential system integrators, transforming GPUs into full-scale AI servers with networking, power, and cooling, then delivering them to clients. Strong recent earnings and AI-specific revenue/order growth for Dell and HPE underscore this shift. 2. **Networking & Connectivity (e.g., Corning, Nokia, Cisco):** As AI clusters grow, high-speed data transfer becomes paramount. Corning benefits from fiber demand for data center links, Nokia is exploring AI-integrated wireless networks (AI-RAN), and Cisco sees surging orders for data center switches—all critical for efficient AI operations. 3. **Storage (e.g., Western Digital, Seagate):** The AI data explosion requires vast capacity. Beyond high-speed memory (HBM), there's growing need for high-capacity HDDs to store training data, logs, video, and cold/archival data cost-effectively. This revaluation, however, is not a blanket endorsement. True reassessment requires concrete proof: AI-driven orders and revenue growth, upward revisions to company guidance, and sustainable improvements in profit quality, not just top-line sales. In essence, AI is not turning all old tech firms into high-growth stocks; it is selectively re-pricing the "old assets" of companies that are mission-critical for building the new AI infrastructure, transforming their legacy capabilities into renewed growth engines.

marsbit06/05 00:55

From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

marsbit06/05 00:55

Probability in the Price: How World Cup Odds Are Calculated

**The Probability in the Price: How World Cup Odds Are Calculated** Two major systems released their "championship probabilities" before the 2026 World Cup, and they disagreed on the favorite. Prediction market aggregators listed France at around **17%**, while the Opta supercomputer gave European champion Spain **16.1%**. These numbers look similar, but their production methods are fundamentally different. The market's **17%** is the **price** that clears after hundreds of millions of dollars in trading across platforms like Polymarket and Kalshi, where contracts trade between 0 and 100 cents, directly representing implied probability. This liquidity is provided by crypto-native market makers like Wintermute, though the market still has "the liquidity profile of an early-stage" asset class. In contrast, Opta's **16.1%** is a **simulated frequency**. Its model uses team data (including betting market odds as an input) to estimate match probabilities, then runs **10,000 full tournament simulations**, counting how often each team wins. Which is more accurate? There is **no rigorous, cross-tournament academic study** directly comparing their track records. However, a persistent **longshot bias**—where low-probability outcomes are systematically overvalued—observed in traditional betting for nearly a century, has also been found in modern crypto prediction markets. Research shows low-price contracts on Kalshi/Polymer less likely to pay out than their implied odds suggest. Unlike traditional bookmakers, prediction markets operate on **public blockchain ledgers**, making every transaction auditable and enabling such research. However, price formation is also influenced by **regulatory uncertainty**, as seen in recent US state-level bans and legal battles over jurisdiction. In summary, the "probability" you see is either a **market-clearing price** subject to behavioral biases and liquidity constraints, or a **model-simulated frequency** that partially incorporates market data. The question of which method is more reliable remains open, highlighting the importance of asking: **How was this number produced?**

marsbit06/05 00:26

Probability in the Price: How World Cup Odds Are Calculated

marsbit06/05 00:26

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