Another Dogecoin ETF Has Gone Live For Trading, How Did It Perform?

bitcoinistОпубликовано 2026-01-24Обновлено 2026-01-24

Введение

21Shares has launched a new Spot Dogecoin ETF, TDOG, on NASDAQ, providing investors with direct, physically-backed exposure to Dogecoin. This brings the total number of US Dogecoin ETFs to three, alongside offerings from Grayscale and Bitwise. The launch was endorsed by the Dogecoin Foundation's corporate arm, House of Doge. However, the new ETF saw a weak start, with no inflows and a slight decline of 0.07% on its first day. This lackluster performance is consistent with other Dogecoin ETFs, which have also reported zero inflows recently, indicating limited investor interest in these products.

The US crypto market has welcomed a new entrant as 21Shares rolls out its Spot Dogecoin ETF, giving investors another avenue to engage with the infamous dog-themed meme coin. Trading kicked off amid a mix of curiosity and caution, with on-chain data already showing how much the DOGE ETF has performed so far.

21Shares Launches Dogecoin ETF

In a press release on Thursday, January 22, 21Shares announced the official launch of its Spot Dogecoin ETF, TDOG, which began trading on NASDAQ the same day. The new ETF provides investors with direct exposure to Dogecoin through a fully backed, regulated, and transparent vehicle. Each ETF share is also backed 1:1 by DOGE held in institutional-grade custody.

Notably, the launch of the new TDOG ETF brings the total number of US Dogecoin ETFs to three, joining Grayscale’s GDOG and Bitwise’s BWOW. 21Shares is also the only ETF provider endorsed by House of Doge, the official corporate arm of the Dogecoin foundation, highlighting the global asset manager’s close ties to the meme coin.

As one of the largest crypto ETF issuers, 21Shares continues to expand its crypto product lineup with the introduction of TDOG. This follows the investment company’s previous ETF offerings, including TSOL, a Solana ETF released in November 2025; ARKB, a Spot Bitcoin ETF launched in January 2024; and TETH, an Ethereum ETF introduced in July of the same year. Together, these products demonstrate 21Shares’ commitment to providing institutional-grade access to high-demand digital assets.

Federick Brokate, Global Head of Business Development at 21Shares, highlighted DOGE’s large and active global community, calling it a unique digital asset with constantly growing use cases. He added that the new TDOG ETF will give investors regulated, physically backed exposure through a familiar ETF structure they know and trust.

Marco Margiotta, the CEO of House of Doge, also shared comments on the recently launched 21Shares ETF. He said that TDOG is a step toward making Dogecoin easier to access through traditional financial systems. He also disclosed that House of Doge’s partnership with 21Shares will help more people get involved as the Dogecoin ecosystem grows.

How 21Shares Dogecoin ETF Has Performed So Far

Contrary to expectations, 21Shares’ recently launched Dogecoin ETF saw weak performance on the first day of trading, signaling investors’ lack of interest in the investment product. Data from SoSoValue shows that TDOG experienced no inflows on January 22 and instead declined by about 0.07%. Despite it being the second day of trading, the DOGE ETF has still not registered any flows.

Source: Chart from SoSoValue

This lackluster performance has been observed across all Dogecoin ETFs this week. Grayscales’ GDOG and Bitwise BWOW have reported zero inflows over the last week. The last time GDOG saw positive activity was on January 8, when it received around $333,083 in investments. Before that, the ETF recorded its highest inflows on January 2, totaling roughly $2.3 million. Since its launch in November 2025, GDOG ETF inflows have been unstable, with more days of inactivity than significant investment.

DOGE trading at $0.12 on the 1D chart | Source: DOGEUSDT on Tradingview.com

Связанные с этим вопросы

QWhat is the name of the new Dogecoin ETF launched by 21Shares and on which exchange did it begin trading?

AThe new Dogecoin ETF is called TDOG and it began trading on NASDAQ.

QHow many Dogecoin ETFs are now available in the US market with the launch of TDOG?

AWith the launch of TDOG, there are now three Dogecoin ETFs available in the US market.

QHow did the 21Shares TDOG ETF perform on its first day, according to data from SoSoValue?

AAccording to SoSoValue, the TDOG ETF experienced no inflows on its first day and instead declined by about 0.07%.

QWhich organization has endorsed 21Shares as its only ETF provider, as mentioned in the article?

A21Shares is the only ETF provider endorsed by the House of Doge, the official corporate arm of the Dogecoin foundation.

QWhat was the performance trend for other existing Dogecoin ETFs (GDOG and BWOW) in the week leading up to the article?

AGrayscale's GDOG and Bitwise's BWOW both reported zero inflows over the last week.

Похожее

The First Case on AI Agents: What Was Adjudicated?

"The First 'Agent' Ruling: What Was Decided?" On April 30, the Guangzhou Internet Court issued a ruling—China's first behavior preservation order in the intelligent agent (AI agent) field. The defendant, an open-source AI agent software, was ordered to stop downloads, cease actions that bypassed a platform's technical protection measures, and delete related tutorials and data. The core issue: the software used the operating system's "accessibility service" permissions to automate user interactions within other apps without those platforms' authorization. This mirrors a recent US case where Amazon sued Perplexity for similar reasons—bypassing Amazon's API to directly scrape and interact with its pages—and won a preliminary injunction. Both rulings establish a crucial legal boundary for the AI agent era: agents cannot operate unchecked. The article argues the fundamental legal principle emerging is one of **dual authorization**. An AI agent requires both **user consent** AND **platform consent** to operate legitimately within that platform's ecosystem. Bypassing platform rules through system-level permissions, even with user permission, undermines platform responsibilities for content moderation, data security, and user privacy, creating liability issues. The piece uses the evolution of "Doubao Phone" (an AI-integrated smartphone) as a case study. Its initial, aggressive version that bypassed platform controls faced roadblocks. Its upcoming 2.0 version is reportedly pivoting to negotiate API access and authorization deals with major platforms (like Alibaba's ecosystem), seen as a strategic adaptation to the new regulatory reality. A global trend is identified: the era of unregulated, "wild west" growth for AI agents is ending, replaced by a **compliance race**. This raises barriers to entry, as securing platform authorizations becomes a new cost. Open-source status is also not a legal shield if the code facilitates bypassing technical protections. In conclusion, these first rulings target not the largest, but the most **aggressive and representative** cases. By setting precedent with them, regulators are efficiently steering the entire industry towards a new, more regulated operating paradigm defined by dual authorization and platform cooperation.

marsbit3 мин. назад

The First Case on AI Agents: What Was Adjudicated?

marsbit3 мин. назад

Fired by Google Over a 14-Page Paper, Over 4,000 Rallied for Her. 6 Years Later: She Almost Predicted the Entire AI Era Back Then.

In late 2020, Google AI researcher Timnit Gebru was effectively dismissed following a conflict over a 14-page, unpublished research paper she co-authored titled "On the Dangers of Stochastic Parrots." The paper, which has since been cited over 14,000 times, raised critical early warnings about the risks of large language models (LLMs). It argued that these models, trained on vast, biased internet data, are essentially "stochastic parrots" that mimic language without true understanding, potentially amplifying societal biases, generating plausible but false information (later termed "AI hallucination"), consuming massive energy, and obscuring their training data contents. Gebru's stance led to a clash with Google management, who requested the paper's withdrawal. Her subsequent internal criticism of the company's diversity efforts and handling of the matter culminated in her termination, which sparked protests from over 4,000 Google employees and researchers. Six years later, the paper's predictions have proven remarkably prescient. Issues like AI hallucination, embedded bias (evident in resume screening and healthcare algorithms), soaring energy consumption from AI data centers, unvetted training data containing harmful content, and the risk of "model collapse" from AI-generated internet content have become central industry challenges. The incident also highlighted concerns about AI development being driven primarily by commercial competition within a handful of powerful tech companies, often at the expense of ethical considerations. After leaving Google, Gebru founded the Distributed AI Research Institute (DAIR) to explore these issues independently. The controversy underscores how her early, critical insights into the fundamental limitations and societal impacts of LLMs anticipated many of the most pressing dilemmas in today's AI era.

marsbit4 мин. назад

Fired by Google Over a 14-Page Paper, Over 4,000 Rallied for Her. 6 Years Later: She Almost Predicted the Entire AI Era Back Then.

marsbit4 мин. назад

Elderly Borrow Money to Trade Stocks, Entire Nation Adds Leverage: 'Ant Army' Panics as South Korean Stock Market Plunges

Titled "Panic Among 'Ant Army' as South Korean Stocks Plunge After Elders Borrow to Invest, Everyone Leverages Up," this article details a dramatic reversal in South Korea's red-hot stock market. After a sustained rally toward 9,000 points driven by AI semiconductor hype, the KOSPI index recently crashed, triggering circuit breakers. The sell-off was led by major chipmakers Samsung Electronics and SK Hynix, whose combined weight in the index is over 50%. The plunge exposed the extreme leverage and speculative behavior that fueled the boom. Individual investors, dubbed the "ant army," had borrowed heavily or used leverage ETFs to chase gains, with trading accounts outnumbering the population. A significant portion of this leveraged money came from older citizens, some of whom reportedly cashed out insurance policies to invest. ETF trading became dominated (over 90%) by high-risk leveraged and inverse products. The correction was triggered by a pullback in U.S. tech stocks, leading to a foreign capital exodus and a weakening Korean won, creating a vicious cycle. While President Lee Jae-myung attempted to reassure markets and NVIDIA's CEO signaled support during a visit, officials like Finance Minister Ju Yeong-geun expressed concern over the dangerous "herd mentality." The article highlights a pervasive, high-risk investment culture where everyone from office workers to retirees and even parents opening accounts for newborns sought quick profits, largely concentrated in a few tech stocks, setting the stage for a sharp and painful correction.

marsbit10 мин. назад

Elderly Borrow Money to Trade Stocks, Entire Nation Adds Leverage: 'Ant Army' Panics as South Korean Stock Market Plunges

marsbit10 мин. назад

From Hunyuan to WeChat AI: Tencent's Slow Paced Journey Reaches the Delivery Juncture

On June 8, 2026, WeChat's developer platform announced the internal testing of "WeChat AI," an AI assistant integrated into the WeChat ecosystem. It allows users to invoke, access, and operate Mini Programs through natural language conversation. The platform offers two access modes: an "Automatic Mode" where developers authorize platform access to their source code for zero-configuration AI operation, and a "Developer Mode" for building custom skills. While the name "WeChat AI" is provisional, this marks WeChat's first step in opening its vast Mini Program ecosystem—comprising over 400,000 developers and hundreds of millions of daily active users—to AI-driven conversational interaction. This move represents the latest step in Tencent's deliberate AI strategy, moving from technical R&D and standalone product validation to integration within its super-app. The underlying foundation is Tencent's self-developed Hunyuan large language model. Ranked first domestically in application-oriented capabilities like Agent task execution in 2025, Hunyuan's focus on stability and precision over raw parameter count aligns with WeChat AI's need for reliable, low-latency operations involving sensitive tasks like payments and bookings. Prior C-side validation came from "Yuanbao," a standalone AI app whose Monthly Active Users (MAU) surpassed 114 million during the 2026 Chinese New Year红包 campaign, though daily activity later subsided. This "pulse growth" highlighted the challenge of user retention for standalone apps, informing the decision to integrate AI natively into WeChat's high-frequency scenarios. However, WeChat AI's "Automatic Mode," which requires source code access, raises developer concerns about code security, data visibility, and liability for AI errors. A deeper, ecosystem-level tension exists between the efficiency of centralized AI task调度 and the potential "short-circuiting" of merchant pages, which could erode their branding, advertising revenue, and user engagement. As Tencent Chairman Pony Ma noted, balancing centralized AI调度 with the protection of decentralized merchant traffic is a core challenge. In summary, Tencent's AI path—comprising the stable Hunyuan base model, the user-validated Yuanbao app, and the newly testing WeChat AI integration—is logically coherent. The success of WeChat AI now hinges on resolving developer trust, establishing fair ecosystem rules for merchants, and ensuring operational reliability to gain user confidence for deep, transactional use.

marsbit11 мин. назад

From Hunyuan to WeChat AI: Tencent's Slow Paced Journey Reaches the Delivery Juncture

marsbit11 мин. назад

STRC Briefly Fell Below $91: Will Strategy Be Hunted by 'Market Fear'?

The article draws a parallel between FTX's 2022 collapse and the current situation facing MicroStrategy (Strategy), a major corporate holder of Bitcoin. The author argues that MicroStrategy's financial model, heavily reliant on issuing equity and convertible debt at a premium to its Bitcoin holdings, is under stress. The core issue is the compression of MSTR's stock premium over its Bitcoin holdings (NAV). This erodes the viability of its "flywheel" – using equity sales to buy more Bitcoin. The company has shifted towards preferred shares (like STRC) and debt to raise capital, incurring significant dividend and interest obligations (approximately $1.7 billion annually). With cash reserves dwindling and debt maturities looming, MicroStrategy faces mounting pressure to generate cash. The article outlines three problematic options: 1) cutting preferred dividends, damaging investor confidence; 2) issuing more MSTR stock at low premiums, diluting existing shareholders; or 3) selling Bitcoin, which founder Michael Saylor had vowed against but recently did in a small symbolic transaction. The author suggests that, like FTX, a crisis of confidence could trigger a rapid downward spiral as investors flee. While noting Saylor's actions are legal—unlike SBF's fraud at FTX—the article warns the structural risk born from financial engineering and over-leverage is significant. The preferred path out is a sharp rise in Bitcoin's price to restart the premium flywheel, but this would only create a larger, more complex system vulnerable to future failure. The author concludes by advocating for direct Bitcoin ownership over exposure through MicroStrategy's increasingly risky financial structure.

Foresight News25 мин. назад

STRC Briefly Fell Below $91: Will Strategy Be Hunted by 'Market Fear'?

Foresight News25 мин. назад

Торговля

Спот
Фьючерсы
活动图片