SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

Odaily星球日报Опубліковано о 2024-03-04Востаннє оновлено о 2024-03-04

Анотація

风险资产上周持续反弹，即使又有一家区域性银行传出信贷问题，加上一位著名经济学家宣称美联储在2024年不会降息，也无法阻止上涨列车的前进。在加密货币方面，创纪录的资金仍不断通过ETF流入，正面的市场情绪似乎也蔓延到了altcoins以及memecoin。

风险资产上周持续反弹，即使又有一家区域性银行传出信贷问题，加上一位著名经济学家宣称美联储在 2024 年不会降息，也无法阻止上涨列车的前进。根据德意志银行的数据，SPX 指数在过去 18 周中上涨了 16 周，这是自 1971 年以来从未出现过的涨势。

就在 SVB 倒闭一周年之际，纽约社区银行 (New York Community Bancorp) 管理层称，该银行发现内部贷款审查流程存在重大缺陷，导致 100% 的商誉减值（24 亿美元）以及管理团队的整顿，其股价在上周五出现暴跌。然而，与去年广泛的恐慌情绪不同，华尔街似乎认为这仅是一个个案，该股市净率（约 0.5）的疲软在很大程度上已经充分反映了这些担忧，即便 NYCB 股价在周五暴跌了 20% ，但 KBW 区域银行指数仅出现些微调整（约 0.3% ）。

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

美国经济衰退遥遥无期（亚特兰大联储预估第一季度 GDP 增长约 3% ），美国消费者仍相当强劲（美国银行提到 2 月份消费者支出增长超过 3% ），就业/薪资/通胀增长保持稳定，Apollo 的首席经济学家表示，由于央行将在 2024 年大部分时间里应对通胀并保持高收益率，美联储将不会在 2024 年降息。Slok 指出，美国增长预期大幅上升，就业市场依然紧绷，核心通胀居高不下，加上金融形势过于宽松，这些因素会共同抑制宽松预期，此外，美联储从 2023 年第四季度开始的口头政策转向已经为资产价格提供了足够的推动力。

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

Powell 主席将于 3 月 7 日向参议院银行委员会提供半年度货币政策更新，届时他将有机会讨论近期美国通胀数据的反弹，并就 12 月鸽派转向的理由接受质询。此外，财报季已经过去，企业盈利可观，也已反映在股票价格中，根据 Bloomberg，SPX 盈利超出季前预测的幅度有机会成为 2021 年第四季度以来最大。不断增长的盈利无疑让华尔街措手不及，策略分析师们急忙上调 SPX 年终目标，仅在过去两周内媒体就报导了超过六次的预测上调。

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

与加密货币类似，散户交易活动激增，个股期权的交易量不断增加，甚至远超过 2021 年的高点，且在 NVDA 和 META 的正向盈利结果后进一步加速。

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

在中国，股票与债券市场也终于迎来令人振奋的相关性变化，两种资产类别的价格终于开始同步（利率下降=债券价格上升）。中国资产是否迎来了转捩点？人民银行的宽松政策是否终于开始反映在股价上？希望正面的价格走势得以持续...

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

在加密货币方面，创纪录的资金仍不断通过 ETF 流入，正面的市场情绪似乎也蔓延到了 altcoins 以及 memecoin， SOL (+ 17% )、XPR (+ 16% )、Cardano (+ 21% ) 、Polkadot (+ 19% )、Doge (+ 7% ) 和 Shiba (+ 131% ) 在过去 7 天都在加速上涨。

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

在期权方面，短天期期权需求激增，未平仓量集中在 6.5 至 7.5 万美元的水平附近。期权交易所 Deribit 上周公布了创纪录的交易量， 24 小时交易量超过 124 亿美元，未平仓量超过 270 亿美元。市场对短天期看涨期权的巨大兴趣正在推高隐含波动率，并导致期权卖方出现小型 gamma 挤压，其中大部分敞口将于 3 月 29 日到期，因此预计本月大部分时间里价格将反复波动（上下震荡），享受上涨的同时请务必谨慎管理风险。

SignalPlus宏观分析(20240304)：降息预期推迟，SPX持续上涨

您可在 ChatGPT 4.0 的 Plugin Store 搜索 SignalPlus ，获取实时加密资讯。如果想即时收到我们的更新，欢迎关注我们的推特账号@SignalPlus_Web3 ，或者加入我们的微信群（添加小助手微信：SignalPlus 123）、Telegram 群以及 Discord 社群，和更多朋友一起交流互动。

SignalPlus Official Website：https://www.signalplus.com

Пов'язані матеріали

As Channel Dividends Fade, What Can DeFi Protocols Rely On to Resist Giant Harvesting?

The article discusses how major tech companies like Coinbase, Stripe, and Kraken are increasingly acquiring or building their own underlying infrastructure to capture profits, moving away from relying on external DeFi protocols. It uses the example of Coinbase's Base blockchain, which generates sequencing fees for Coinbase from protocols like Morpho that deploy on it. Similarly, Stripe acquired Bridge to capture stablecoin yield instead of paying it to Circle, and Kraken bought NinjaTrader for its derivatives licenses. The core argument is that when channel-based advantages (like user traffic) fade, control over the profit-generating infrastructure becomes critical. While this could lead to a future dominated by a few integrated giants, the article suggests a more balanced outcome is possible. Protocols that have already achieved deep, multi-chain integration and embedded themselves into corporate backends create high switching costs, making them difficult to replace. Examples include Morpho (despite reliance on Base, it's widely deployed) and Uniswap. The piece concludes that the race between institutional expansion and open-source protocol proliferation will ultimately shape the industry's structure.

marsbit6 хв тому

As Channel Dividends Fade, What Can DeFi Protocols Rely On to Resist Giant Harvesting?

marsbit6 хв тому

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbit7 хв тому

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit7 хв тому

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbit11 хв тому

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbit11 хв тому

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbit12 хв тому

Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the 'Barbarians at the Gate'?

"Insurance Industry Faces New Rival: Are Prediction Markets the 'Barbarians at the Gate'?" Prediction markets, exemplified by platforms like Kalshi and Polymarket, are emerging as potential disruptors to the traditional insurance industry by offering alternative risk-hedging mechanisms. These markets allow users to bet on specific event outcomes, effectively creating a form of customizable, on-demand insurance. Key examples highlight this shift. In sports, Kalshi partnered with insurance broker Game Point Capital to provide NBA teams with more affordable options to hedge performance bonuses compared to traditional insurers. In real estate, Polymarket's collaboration with Parcl lets users speculate on city-specific housing price indices, allowing homeowners to hedge against price drops or buyers against price increases. Furthermore, businesses like a New York bar have used Kalshi to hedge marketing promotions (e.g., offering free drinks if a team wins), framing the transaction explicitly as placing a "hedge." The article argues prediction markets offer advantages over traditional insurance and even sports betting in transparency, liquidity, and flexibility. They provide a wider range of event coverage, act as neutral platforms rather than counterparties, and offer clearer pricing. The piece cites historical precedents like large "refund promotion" hedges by businesses using sportsbooks but notes prediction markets modernize the concept. However, challenges remain for widespread adoption as an insurance alternative, including limited liquidity in some markets, unclear regulatory status, and potential vulnerabilities in event resolution mechanisms. Despite these hurdles, prediction markets are positioning themselves as new tools for risk management, directly challenging certain segments of the conventional insurance landscape.

Odaily星球日报18 хв тому

Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the 'Barbarians at the Gate'?

Odaily星球日报18 хв тому

Торгівля

Спот

Ф'ючерси