Is Shiba Inu About to Repeat Its Bearish Playbook Again This Month?

TheNewsCryptoPublicado em 2025-12-26Última atualização em 2025-12-26

Resumo

Shiba Inu is on track to repeat its typical bearish December performance, declining 14.15% so far in December 2025 with only five days remaining. Historical trends show SHIB often struggles in the final month of the year, with significant losses in December 2021 (29.5%), 2022 (13.5%), and 2024 (21%). The only exception was a 24.6% gain in December 2023. To finish this month positively, SHIB would need to rally approximately 16.64% from current levels, reaching at least $0.0000084.

Shiba Inu is on track to repeat its bearish December performance as the month draws to a close. With only five days remaining in December 2025, the meme coin has declined 14.15% and shows limited signs of recovery. The pattern reinforces historical trends that have seen SHIB struggle during the year’s final month.

The fourth quarter of 2025 has proven difficult for Shiba Inu holders amid broader cryptocurrency market weakness. This downward pressure extended into December, with the token losing value consistently throughout the month. Historical data reveals December has frequently been an unfavorable period for SHIB price action.

Historical December performance shows consistent losses

December 2021 saw Shiba Inu close down 29.5% as investors took profits following the 2021 bull run. The token had achieved massive gains earlier in the year, prompting holders to exit positions during the final month.

December 2022 brought another 13.5% decline. The FTX exchange collapse in November triggered widespread panic across crypto markets, with billions wiped from total market capitalization. SHIB continued declining through December as investors reduced risk exposure.

December 2023 provided the only exception to this bearish pattern. Shiba Inu closed that month with a 24.6% gain, delivering double-digit returns that defied the historical trend. Many investors anticipated this positive performance would continue into the following year.

Instead, December 2024 reversed course with a 21% decline. Investors locked in gains after SHIB rallied to $0.000033 during the post-election surge earlier that month. Profit-taking pressure overwhelmed buying demand as the year concluded.

Current month mirrors bearish December trend

December 2025 is following the established pattern of negative performance. Shiba Inu opened the month trading at $0.000008385 and has already fallen close to 14%.

For SHIB to finish December in positive territory, the price must climb to at least $0.0000084 within the remaining five days. This would require a rally of approximately 16.64% from current levels.

TagsShiba Inu

Perguntas relacionadas

QWhat is the current performance trend of Shiba Inu in December 2025?

AShiba Inu is declining, having fallen 14.15% so far in December 2025, and is on track to repeat its historical bearish performance for the month.

QWhich previous December was the only exception to Shiba Inu's typical bearish trend?

ADecember 2023 was the only exception, as Shiba Inu closed that month with a 24.6% gain.

QWhat major event in November 2022 contributed to Shiba Inu's decline that continued into December?

AThe collapse of the FTX exchange in November 2022 triggered widespread panic across crypto markets, leading to a 13.5% decline for SHIB in December as investors reduced risk exposure.

QHow much does Shiba Inu need to rally in the remaining days to finish December 2025 in positive territory?

AShiba Inu needs to rally approximately 16.64% from its current level to reach at least $0.0000084 and finish December in positive territory.

QWhat was cited as the reason for Shiba Inu's 21% decline in December 2024?

AThe decline was due to investors locking in gains after SHIB rallied to $0.000033 during the post-election surge earlier that month, with profit-taking pressure overwhelming buying demand.

Leituras Relacionadas

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

The article titled "Gate Research Institute: Are Crypto Financial Products Sparking a 'Wall Street' Wave—Competition or Convergence?" explores the evolving relationship between the crypto ecosystem and traditional finance (TradFi). The piece begins by reflecting on Bitcoin's original 2009 vision of decentralization, disintermediation, and moving away from banks. It then contrasts this with the 2024 landscape, where key crypto assets like Bitcoin are increasingly held through Wall Street products like ETFs issued by giants like BlackRock. The article questions whether this signifies that TradFi is systematically taking over the rights to issue, price, custody, and distribute crypto financial assets. The core argument is that this is not a zero-sum takeover but rather a bidirectional convergence where each side addresses the other's weaknesses. Crypto offers 24/7 global markets, programmable settlement, and open access but lacks compliant channels, institutional-grade custody, deep fiat liquidity, and mainstream distribution. TradFi possesses these but is constrained by legacy systems, limited operating hours, and slow settlement. Two primary convergence paths are highlighted: * **Path A (CEX to TradFi):** Exemplified by Gate, which has progressed from offering tokenized stocks and CFDs to providing direct, real stock trading (US, Hong Kong, South Korea) within its platform, using USDT. * **Path B (TradFi to Crypto):** Exemplified by Robinhood, which has integrated crypto trading, acquired exchanges like Bitstamp, and is moving traditional assets like stocks onto the blockchain via tokenization and its own Layer 2. Both paths are ultimately competing to become the next-generation, unified financial account—a "super account" where users can seamlessly trade cryptocurrencies, stocks, ETFs, RWA (Real World Assets), and tokenized treasury products in one interface. The growth of RWA and tokenized treasuries (e.g., BlackRock's BUIDL) is presented as the asset-layer fusion, providing stable, yield-bearing assets on-chain and acting as a bridge between the two worlds. In conclusion, the "Wall Street-ization" of crypto is framed as a mutual transformation. Decentralized ideals persist in the protocol layer, while at the application layer, a more efficient, global, and accessible unified capital market is emerging from this convergence. The future competition lies not between crypto exchanges and stockbrokers, but between platforms vying to offer the most comprehensive asset coverage, liquidity, and user experience within a single account.

marsbitHá 4m

Gate Research Institute: The 'Wall Street-ization' Wave of Crypto Financial Products – Competition or Integration?

marsbitHá 4m

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.

marsbitHá 8m

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

marsbitHá 8m

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.

marsbitHá 10m

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

marsbitHá 10m

Trading

Spot
活动图片