XRP ETF sees persistent inflows despite price weakness — Is a bullish divergence forming?

ambcryptoPubblicato 2025-12-16Pubblicato ultima volta 2025-12-16

Introduzione

Despite ongoing price weakness, the XRP ETF has recorded persistent inflows, with $10.89 million in fresh investments pushing total net assets to $1.12 billion. This institutional demand, showing consistent daily inflows since mid-November, contrasts with XRP's price decline of over 40% from its yearly peak. Technical indicators suggest weakening bearish momentum, with the MACD hinting at a potential bullish crossover. This divergence between strong ETF accumulation and retail selling pressure often precedes market reversals. Key levels to monitor include support at $1.85 and resistance at $2.05. Sustained high inflows could eventually drive upward price movement.

XRP has recorded yet another day of positive ETF inflows, extending a streak that began shortly after the product launched.

New data from SoSoValue shows $10.89 million in fresh inflows today, pushing total net assets to $1.12 billion, even as XRP’s price continues to trend downward.

This divergence between ETF demand and spot market weakness raises the question: Is institutional accumulation quietly setting the stage for a reversal?

Institutional demand rises as XRP ETF AUM climbs

The SoSoValue chart shows a clear pattern:

  • Consistent green inflow bars since mid-November
  • A steady climb in ETF AUM, now above the $1.1 billion mark
  • No major outflow days since launch

Even during broader market volatility, the XRP ETF has attracted buyers daily. This is a sign that institutional allocators may view current prices as favorable entry points.

If this trend continues, the ETF could become a stabilizing force for XRP’s market structure, similar to how Bitcoin ETFs behaved during their early launch phase.

XRP price still sliding, but momentum shows signs of exhaustion

On the price front, XRP remains under pressure. The chart from TradingView shows:

  • XRP trading near $1.92, down more than 40% from its yearly peak
  • A persistent downtrend stretching from early November
  • Lower lows and lower highs across the daily timeframe

However, the MACD indicator suggests weakening bearish momentum.

The histogram has begun to flatten, while the MACD and signal lines are headed toward a potential bullish crossover — a pattern often associated with trend reversals.

This suggests that although sellers remain active, the intensity of the downtrend may be diminishing.

ETF inflows vs. retail selling: a familiar pattern?

Historically, strong ETF inflows during price weakness can indicate:

  • Institutions accumulating on discount
  • Retail-driven selloffs nearing exhaustion
  • A potential bottom formation in progress

While this setup does not guarantee an immediate rebound, past market cycles show that when ETF flows and price diverge, price often catches up later.

XRP’s current structure fits that profile:

  • Funds are buying the dip, not fleeing
  • Retail sentiment remains weak
  • Momentum indicators are stabilizing

If inflows continue at this pace, they may eventually place upward pressure on the spot market.

Key levels to watch

  • Support: $1.85 — losing this level exposes $1.70
  • Resistance: $2.05 — a key reclaim level for trend strength
  • ETF threshold: Sustained inflows above ~$15M/day could trigger stronger directional moves

Final Thoughts

  • XRP ETF inflows continue to build despite falling prices, creating a clear divergence worth monitoring.
  • Momentum indicators suggest the selloff is weakening, but confirmation rests on reclaiming key price levels.

Letture associate

Huang Renxun Dramatically 'Saves' South Korean Stock Market

In early June, South Korea's stock market experienced a sharp decline, with the KOSPI index dropping over 5% and triggering a trading halt. Amid this volatility, NVIDIA CEO Jensen Huang's visit to Seoul provided a dramatic boost to market sentiment. During his trip, Huang held a dinner meeting with SK Group Chairman Chey Tae-won and SK Hynix CEO Kwak Noh-Jung. He announced that NVIDIA's new Vera CPU would utilize SK Hynix DRAM and confirmed a multi-year technical collaboration between the two companies. This partnership aims to co-develop next-generation memory for NVIDIA's AI infrastructure roadmap, covering products from data center supercomputers to personal AI devices. Huang also publicly commented that AI company stocks were attractively priced. A key announcement was that NVIDIA's upcoming Vera Rubin AI supercomputer systems will use HBM4 memory, with supply qualifications granted to all three major suppliers: SK Hynix, Samsung Electronics, and Micron Technology. Despite this multi-sourcing strategy, Huang warned that the industry-wide chip shortage, affecting everything from wafers to packaging, is expected to persist for several years due to relentless demand from global AI factory construction. The collaboration extends beyond memory supply. SK Hynix will employ NVIDIA's AI platforms and Omniverse digital twin technology to enhance its own semiconductor design, simulation, and manufacturing processes, aiming for more autonomous factory operations. This visit builds upon a prior October 2025 agreement for SK Group to build a large-scale AI data center using over 50,000 NVIDIA GPUs. Huang's itinerary also included meetings with other Korean giants like Hyundai, LG, and Samsung, indicating NVIDIA's broader strategy to deepen ties with South Korea's tech industry.

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Huang Renxun Dramatically 'Saves' South Korean Stock Market

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When Inference Becomes a Scarce Resource, Who Captures the Value?

When Inference Becomes the Scarce Resource, Who Captures the Value? The core AI bottleneck has shifted from model training to inference (runtime execution). While concerns persisted about an "AI compute gap"—initially a $200B, now a $600B problem—the market is now recognizing that the solution and value lie in the inference layer. Nvidia's financial restructuring around "serving tokens" and Cerebras's successful IPO highlight this shift. Inference is a recurring, usage-based cost, estimated to be 10-50x larger than the one-time training market, especially with the rise of agentic AI. The inference stack spans six layers: silicon (e.g., Nvidia), bare metal (e.g., CoreWeave), GPU rental/aggregation, deployment/optimization, model APIs, and end applications. Most companies operate in one layer. However, Hyperbolic uniquely spans three layers (GPU rental, deployment, and model APIs) without owning any hardware. It aggregates fragmented GPU supply from multiple cloud providers into a standardized pool, offering developers the cheapest available compute through intelligent routing. Its multi-cloud aggregation creates a data moat and a flywheel: more supply leads to better pricing data and liquidity, attracting more developers and providers. In contrast, applications like Venice operate at the top of the stack, reselling privacy-wrapped inference but remaining dependent on and constrained by the underlying compute costs they purchase. As inference demand explodes, value accrues not just to consumer applications but increasingly to the aggregation and routing layer that captures their cost of revenue. The coming potential GPU oversupply reinforces this dynamic. While hardware owners may suffer from depreciation, asset-light aggregators like Hyperbolic benefit from price arbitrage, routing workloads to the cheapest available capacity. The ultimate winner in the inference economy may not be the entity with the most GPUs, but the one that can most efficiently discover, aggregate, and route the world's fragmented compute.

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When Inference Becomes a Scarce Resource, Who Captures the Value?

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