Nasdaq Partners With Kraken to Launch Tokenized Stocks in the U.S.

TheNewsCryptoPubblicato 2026-03-09Pubblicato ultima volta 2026-03-09

Introduzione

Nasdaq has partnered with Kraken and its parent company Payward to develop infrastructure for tokenized stocks and equity products in the U.S. The collaboration will enable public companies to issue and trade blockchain-based versions of their shares while preserving legal rights and regulatory oversight. Nasdaq will create an equity token design to integrate tokenized stocks and ETPs into its regulated systems, ensuring holders retain voting rights and dividend benefits. Kraken will handle distribution and settlement via its xStocks framework. The initiative, pending regulatory approval, builds on a 2025 SEC proposal and is expected to launch in the first half of 2027.

Nasdaq has announced a partnership with cryptocurrency exchange Kraken and its parent company Payward to develop infrastructure for tokenized stocks and related equity products. The collaboration aims to enable publicly traded companies to issue and trade blockchain‐based versions of their shares while preserving legal rights and regulatory oversight.

Under the plan, Nasdaq will build an “equity token design” that allows tokenized versions of stocks and exchange‐traded products (ETPs) to be integrated with its regulated market systems. Each tokenized share will remain legally equivalent to the underlying security, with holders entitled to the same voting rights and dividend benefits as traditional shareholders. The approach is designed to maintain issuer control, investor protections, and market integrity.

How Nasdaq and Kraken Plan to Trade Tokenized Shares Securely

Kraken’s role will focus on distribution and settlement infrastructure through its xStocks tokenized equities framework. The companies said they plan to build an “equities transformation gateway” to move tokenized shares between Nasdaq systems and blockchain networks. This allows investors to trade digital shares securely within a regulated environment.

xStocks has already processed significant transaction volume and has tens of thousands of holders, reflecting growing adoption of tokenized equity products.

The initiative builds on a Nasdaq proposal filed with the U.S. Securities and Exchange Commission in 2025 to support trading and settlement of tokenized securities alongside traditional shares. Nasdaq expects the equity token design and related services to become operational in the first half of 2027, subject to regulatory approvals.

Nasdaq President Tal Cohen said tokenization could enhance how investors access markets and how issuers engage with shareholders. The collaboration reflects broader industry interest in bridging traditional finance infrastructure with blockchain‐based systems.

Highlighted Crypto News Today:

Coinbase Introduces Regulated Bitcoin and Ethereum Futures Trading Across Europe

TagsBitcoinCrypto MarketETHEREUMKrakenNASDAQStock

Domande pertinenti

QWhat is the main purpose of the partnership between Nasdaq and Kraken?

AThe partnership aims to develop infrastructure for tokenized stocks and related equity products, enabling publicly traded companies to issue and trade blockchain-based versions of their shares while preserving legal rights and regulatory oversight.

QHow will tokenized shares maintain equivalence to traditional securities?

AEach tokenized share will remain legally equivalent to the underlying security, with holders entitled to the same voting rights and dividend benefits as traditional shareholders.

QWhat role will Kraken play in this initiative?

AKraken will focus on distribution and settlement infrastructure through its xStocks tokenized equities framework, including building an equities transformation gateway to move tokenized shares between Nasdaq systems and blockchain networks.

QWhen does Nasdaq expect the equity token services to become operational?

ANasdaq expects the equity token design and related services to become operational in the first half of 2027, subject to regulatory approvals.

QWhat existing framework has Kraken's xStocks already demonstrated success with?

AxStocks has already processed significant transaction volume and has tens of thousands of holders, reflecting growing adoption of tokenized equity products.

Letture associate

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit4 min fa

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit4 min fa

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit5 min fa

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit5 min fa

Trading

Spot
活动图片