On the Eve of the US Stock Inflation Test, Wall Street Faces the Most Severe 'Data Deception' in History

marsbitPublicado a 2026-07-13Actualizado a 2026-07-13

Resumen

On the eve of the crucial US June CPI release, a significant credibility gap is emerging between official inflation data and consumer sentiment. While May CPI and PCE figures suggested a "concerning but not critical" picture, the University of Michigan Consumer Sentiment Index plummeted to its lowest level in nearly 50 years. This contradiction is prompting economists to question the reliability of key macroeconomic indicators. The core issue, as highlighted by labor economist Kathryn Anne Edwards, lies in a systemic flaw within the current inflation measurement framework. The Consumer Price Index (CPI) averages prices across a "market basket" meant for a "typical consumer," thereby masking vastly different inflation experiences across demographic groups. For instance, Bureau of Labor Statistics (BLS) research indicates that from 2006 to 2023, the lowest income quintile faced a cumulative inflation rate 7.7 percentage points higher than the highest quintile—a disparity largely invisible in the headline CPI number. This averaging effect means investors and policymakers relying on aggregate CPI may be basing decisions on a statistically smoothed figure that fails to capture the true distribution of economic pressure. Edwards argues that expanding this measurement framework is technically feasible, requiring primarily political will rather than new data collection. The BLS already tracks 100,000 items monthly; creating more granular indices for different family types, income l...

Author: Wall Street News

Official inflation data shows the situation is under control, but US consumer confidence has fallen to its lowest level in nearly half a century—this discrepancy is shaking the market's fundamental trust in macroeconomic data.

US June CPI data will be released tomorrow. Prior to this, the Consumer Price Index in May rose 4.2% year-on-year, and the Personal Consumption Expenditures Price Index (PCE) rose 3.4%. Official data presents a picture of "concerns, but no crisis."

However, the University of Michigan Consumer Sentiment Index hit a record low in May since records began in 1978, and the June reading was the second lowest in history—the fifty years covered by this index include the oil crisis, two stock market bubbles, one pandemic, and six recessions, yet Americans still regard the present as the worst economic period.

This contradiction is triggering deep reflection in the economics community.

Labor economist and independent policy advisor Kathryn Anne Edwards wrote in a Bloomberg column that the huge gap between official inflation indicators and the real experiences of the public stems from systematic flaws in the current measurement system—it uses an averaged "market basket" to conceal the vastly different inflation realities faced by different household groups. For investors who rely on this data for asset pricing and policy predictions, this means the core indicators they have long referenced may not accurately reflect the true pressures in the economy.

One Number Hides Millions of Inflation Experiences

The US Bureau of Labor Statistics (BLS) tracks price changes for approximately 100,000 goods and services each month and weights them through consumer expenditure surveys to generate the CPI, which reflects the purchasing behavior of a "typical consumer."

Currently, the BLS only maintains three consumption baskets: All Consumers, All Urban Consumers, and Urban Wage Earners and Clerical Workers.

Edwards points out that the fundamental limitation of this framework is that it compresses highly heterogeneous consumer groups into a single average.

Research by the BLS itself has proven that such differences cannot be ignored: a study covering 2006 to 2023 shows that the average annual inflation rate for the lowest income quintile households was about 0.28 percentage points higher than for the highest income quintile, with a cumulative gap of 7.7 percentage points.

In other words, over the past two decades, the poor have actually borne far greater inflationary pressure than the rich, and this gap is almost invisible in the standard CPI.

The impact of this "averaging" on the market is substantive. When investors and policymakers judge monetary policy directions based on the overall CPI, what they see is a statistic that has been smoothed, not the true distribution of pressure within the economy.

The Data Foundation is Ready, What's Lacking is Policy Will

Edwards' core argument is not to overturn the existing system, but to point out that the technical threshold for expanding measurement dimensions is extremely low.

The BLS has already done the heaviest lifting—collecting price change data for 100,000 goods and services every month. On this basis, constructing more segmented indices by household type (single, married without children, married with minor children, etc.), income level, renter or homeowner, age, and other dimensions is essentially just a matter of re-weighting and presenting the same set of raw data in different ways.

The BLS already has several precedents: a CPI for the elderly, a CPI for new tenants, a CPI excluding product specification changes, and a research series on CPI by income quintile.

These series are released less frequently than the monthly CPI, but they prove the feasibility of the technical path. Edwards suggests that the existing three baskets should be expanded by at least tenfold, with monthly data provided for each typical household type, while increasing the BLS research staff and expanding the consumer expenditure survey sample size.

Beyond Data Distortion, Real Economic Pressures Cannot Be Ignored

Edwards makes it clear that improving the measurement system will not solve the problems of the economy itself.

She enumerates the multiple pressures currently facing the US economy: slowing hiring, stagnant wage growth, persistently high prices, rising credit card debt, high interest rates suppressing housing market vitality, and the potential impact of artificial intelligence on the job market.

These structural pressures collectively explain why there is such a deep chasm between consumer sentiment and official data. In Edwards' view, the correct path to bridge this contradiction is not to ask the public to trust the existing data more, but to make the data system more accurately reflect the living realities of different groups.

For market participants, the significance of this discussion is: as tomorrow's CPI data approaches, investors may need to re-examine to what extent a single aggregate indicator can accurately capture the true inflationary pressures and divergences in consumer behavior in the current economic cycle—and such divergence is precisely the key variable for understanding the Fed's policy path and risks on the consumer side.

Criptos en tendencia

Preguntas relacionadas

QWhat is the core contradiction highlighted in the article between official U.S. inflation data and consumer sentiment?

AThe core contradiction is that official U.S. inflation data (like CPI and PCE) portrays a picture of 'concern but no crisis,' while consumer sentiment, as measured by the University of Michigan Consumer Sentiment Index, has plummeted to its lowest levels in nearly half a century, indicating deep economic pessimism.

QAccording to economist Kathryn Anne Edwards, what is the fundamental flaw in the current U.S. inflation measurement system that causes this disconnect?

AThe fundamental flaw is that the current system uses an average 'market basket' to represent consumption, which masks the vastly different inflation experiences of different household groups (e.g., by income, family type). This averaging creates a single number that fails to reflect the real economic pressures faced by specific segments, particularly lower-income households.

QWhat evidence does the article provide to show that inflation impacts different income groups unequally?

ACiting research by the Bureau of Labor Statistics (BLS) covering 2006 to 2023, the article states that the average annual inflation rate for the lowest income quintile households was about 0.28 percentage points higher than for the highest income quintile, resulting in a cumulative 7.7 percentage point difference over that period.

QWhat practical solution does the article suggest for making inflation data more representative, and why is it considered feasible?

AThe article suggests expanding the number of consumer baskets (beyond the current three) by at least tenfold to provide monthly data for different household types based on income, family structure, age, etc. This is considered technically feasible because the BLS already collects the vast underlying price data; creating more indices would mainly involve reweighting and presenting this existing data differently.

QBeyond data measurement issues, what other economic pressures does the article list as factors contributing to low consumer confidence?

AThe article lists several structural economic pressures: slowing hiring, stagnant wage growth, persistently high prices, rising credit card debt, a housing market subdued by high interest rates, and the potential disruption of the job market by artificial intelligence.

Lecturas Relacionadas

DistributeX Unveils DX Coin Ecosystem Roadmap, Advancing Preparations for Its On-Chain Launch

DistributeX has unveiled the DX Coin Ecosystem Roadmap, detailing its preparations for the upcoming on-chain launch of DX Coin. The roadmap outlines key phases focusing on community governance, technical readiness, blockchain deployment, and ecosystem growth. Current efforts are centered on building community consensus and completing technical groundwork. This includes launching community votes to select the official DX Coin logo and decide on the preferred blockchain network for integration. The platform will also implement features like on-chain wallet binding and contribution tracking to prepare for future data synchronization and rewards. Before the official launch, DistributeX will publish a Tokenomics White Paper detailing the token's issuance, governance, and long-term strategy, and will take a snapshot of eligible community accounts for future asset allocation. The subsequent deployment phase will involve smart contract deployment, security audits, community airdrops, and developing liquidity on decentralized exchanges. Looking further ahead, planned utilities for DX Coin encompass cross-chain interoperability, decentralized governance, digital rights, staking, and other Web3 applications. This roadmap aims to provide the community with clear visibility into the platform's plans, as DistributeX works to establish a solid foundation for DX Coin's on-chain ecosystem and long-term expansion.

TheNewsCryptoHace 37 min(s)

DistributeX Unveils DX Coin Ecosystem Roadmap, Advancing Preparations for Its On-Chain Launch

TheNewsCryptoHace 37 min(s)

AI at a Crossroads: Why Wall Street is Saying "No" to ChatGPT and Claude?

The article "AI at a Crossroads: Why Wall Street Says 'No' to ChatGPT and Claude" explores the growing tension between the adoption of powerful, closed-source AI models and the imperative for data privacy and intellectual property (IP) protection in enterprises, particularly in high-stakes sectors like finance. It details how the fundamental architecture of services like OpenAI and Anthropic involves sending user data in plaintext to the vendors' servers, creating risks of IP leakage ("alpha transfer"). While enterprise contracts with "zero-data-retention" clauses offer some assurance, they rely on trust. A significant problem is "shadow AI," where employees use personal accounts, bypassing corporate policies and leading to data breaches. For consumers, the article highlights that AI conversations lack legal protections like attorney-client privilege and can be subpoenaed in legal cases, a fact many users are unaware of. The core of the piece analyzes the technical spectrum of privacy solutions, contrasting **protocol-level privacy** (contracts, anonymous proxies) with more robust **structural-level privacy**. The latter includes: * **TEEs (Trusted Execution Environments) / Confidential Computing:** Running models in hardware-sealed enclaves with remote attestation. * **End-to-End Encryption (E2EE):** Encrypting prompts so only the target enclave can read them. * **Fully Homomorphic Encryption (FHE):** Performing computations on encrypted data without decryption (currently very slow). * **Local Inference:** Running models entirely on-premise, the most private but costly and limited to less powerful models. The article argues that verifiable privacy (via attestation) is only possible with **open-source models**, as closed-source vendors cannot reveal their serving code without losing competitive advantage. While the performance and cost gap between open and closed models is narrowing, a key dilemma remains: sacrifice some model capability for privacy or risk data exposure for a competitive edge. A case study from Bridgewater and Thinking Machines demonstrates that a finely-tuned open-source model (Qwen) can outperform leading closed models in specific, expert financial tasks, both in accuracy and lower cost. However, the training process itself often isn't private. The discussion extends to the **"harness layer"**—the tools and data sources surrounding an AI agent. Here, privacy becomes even more complex, as each external API call can expose data. Current solutions are mostly at the protocol level (gateways, PII masking), with true encrypted search for open-ended queries still in the research phase. In conclusion, the demand for private AI is growing, with services like Venice AI and Proton gaining users. While privacy-enabling infrastructure (like enclaves) is becoming more affordable and performant, the article posits that the most defensible value lies in solving the remaining hard problems: private training cycles, fully private tool calls, and practical encrypted search. For enterprises, the path forward is to use their proprietary "alpha" (expert knowledge) to fine-tune open-source models within a verifiably private environment, securing their most valuable strategic insights.

链捕手Hace 44 min(s)

AI at a Crossroads: Why Wall Street is Saying "No" to ChatGPT and Claude?

链捕手Hace 44 min(s)

Let Funds Flow at Internet Speed

Tokenization bridges the distinct worlds of always-on, permissionless DeFi and traditional funds with scheduled, permissioned settlements, unlocking significant value for those who can manage this integration. The tokenized Real World Asset (RWA) market exceeds $33 billion, with U.S. Treasuries comprising nearly half. It offers corporate treasurers options from low-risk, liquid Treasury funds to higher-yield, programmable investments, all benefiting from the same audit standards as traditional bonds. The core advantage is *composability*: tokenized funds can combine yield, liquidity, and transferability simultaneously, unlike traditional finance which forces a trade-off. However, achieving this requires sophisticated coordination. Tokenized funds remain legally bound to daily net asset value (NAV) updates, KYC-verified holder lists, and redemption cut-offs based on traditional settlement infrastructure (e.g., 5 PM ET). Key challenges in this hybrid model are: 1) **Price**: Determining token value between NAV updates to prevent manipulation; 2) **Compliance**: Embedding KYC/whitelisting (e.g., within a vault) to allow free circulation of receipt tokens in DeFi; and 3) **Cross-chain Consistency**: Maintaining a single source of truth for ownership and value across multiple blockchains. Projects like Centrifuge (with its deRWA framework and V3 architecture) and LayerZero address these by using a hub-and-spoke model. A central "hub" chain manages NAV, accounting, and compliance, while a messaging layer (LayerZero) synchronizes this data with "spoke" chains where tokens are used, enabling DeFi composability. This coordination layer, which handles in-transit asset accounting and prevents redemption gateway conflicts, becomes a critical and valuable piece of infrastructure—akin to SWIFT or Visa in traditional finance. For institutions, effective tokenization enables strategies like rehypothecation, where tokenized Treasury funds are used as collateral to borrow stablecoins for reinvestment, amplifying yield. However, failures in price synchronization, redemption limits, or cross-chain messaging pose risks that must be meticulously managed to build institutional trust. Ultimately, the goal is to break the old rules that force a choice between yield, liquidity, and transferability. If tokenization can make capital work simultaneously in multiple ways without compromising security, it will attract the attention of institutions managing billions in idle cash. The entities that successfully orchestrate this coordination between traditional finance timelines and blockchain speed are positioning themselves for a central role in the future capital markets.

链捕手Hace 1 hora(s)

Let Funds Flow at Internet Speed

链捕手Hace 1 hora(s)

Trading

Spot

Artículos destacados

Cómo comprar DATA

¡Bienvenido a HTX.com! Hemos hecho que comprar DATA Network (DATA) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar DATA Network (DATA) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu DATA Network (DATA)Después de comprar tu DATA Network (DATA), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear DATA Network (DATA)Tradear fácilmente con DATA Network (DATA) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

33 Vistas totalesPublicado en 2026.07.01Actualizado en 2026.07.01

Cómo comprar DATA

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de DATA (DATA).

活动图片