Sushi and Synthetix get the boot in Grayscale DeFi fund rebalancing

CointelegraphPubblicato 2022-04-07Pubblicato ultima volta 2022-05-09

Introduzione

Digital asset management firm Grayscale, has added three new cryptocurrency assets across three main investment funds.

Digital asset management firm Grayscale, has added three new cryptocurrency assets across three main investment funds, while removing two other assets from its Decentralized Finance Fund as part of this year’s first quarterly rebalance. 
Grayscale removed tokens from crypto-derivatives decentralized exchange Synthetix (SNX), and decentralized exchange SushiSwap (SUSHI), from its DeFi fund after the two crypto assets failed to meet the required minimum market capitalization. No other cryptocurrencies were removed during the rebalancing.
Grayscale’s DeFi fund, which was launched in July last year, currently holds approximately $8 million in assets. The digital assets remaining in the DeFi fund after the quarterly rebalance include Uniswap (UNI), Aave (AAVE), Curve (CRV), MakerDAO (MKR), Amp (AMP), Yearn Finance (YFI) and Compound (COMP).
The crypto asset manager added Avalanche (AVAX) and Polkadot (DOT) to its Digital Large Cap Fund, alongside adding Cosmos (ATOM) to its Smart Contract Platform Ex-Ethereum Fund (GSCPxE Fund).
The GSCPxE Fund, which was launched on March 22nd, offers investors the ability to bet on an index of Ethereum’s largest competitors. The GSCPxE Fund’s current holdings listed by the total amount held are ADA, SOL, AVAX, DOT, MATIC, ALGO, XLM and ATOM.
Grayscale remains the world’s leading crypto asset manager, reporting that it held $43.5 billion in assets under management as of Jan. 3rd, this year. The Grayscale Bitcoin Trust (GBTC) remains the largest fund with just over $30 billion in AUM, but has traded at an increasing discount to its net-asset-value for the past year. GBTC is followed in size by the Grayscale Ethereum Trust (ETCG) which currently holds approximately $11.8 billion in AUM.
01/03/22 UPDATE: Net Assets Under Management, Holdings per Share, and Market Price per Share for our Investment Products.
Total AUM: $43.5 billion$BTC $BAT $BCH $LINK $MANA $ETH $ETC $FIL $ZEN $LTC $LPT $XLM $ZEC $UNI $AAVE $COMP $CRV $MKR $SUSHI $SNX $YFI $ADA $SOL $AMP pic.twitter.com/67Pb7xneoQ
— Grayscale (@Grayscale) January 4, 2022
In 2021, cryptocurrency investment funds generated over $9.3 billion in inflows as institutional adoption rose to new highs. Grayscale is gearing up to offer a Bitcoin Spot exchange-traded fund (ETF) and said it was willing to pursue legal action if the investment product remains barred by the SEC.

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From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

"Old Dogs" Become AI's New Darlings: Revaluing Legacy Infrastructure The AI investment narrative is shifting. Beyond the spotlight on core chipmakers like Nvidia, a new wave of interest is rising for legacy tech companies—Dell, HPE, Nokia, Cisco, Corning, Western Digital—once labeled as slow-growth, outdated stories. This resurgence stems from AI's evolution from model development to real-world deployment, creating massive demand for physical infrastructure. As AI moves into data center construction and enterprise adoption, the focus turns to who can actually build and deliver complex systems. These established players hold decades of experience in supply chains, integration, networking, and enterprise delivery—assets now critical for scaling AI. The revaluation can be grouped into three key infrastructure areas: 1. **Servers & Integration (e.g., Dell, HPE):** They are becoming essential system integrators, transforming GPUs into full-scale AI servers with networking, power, and cooling, then delivering them to clients. Strong recent earnings and AI-specific revenue/order growth for Dell and HPE underscore this shift. 2. **Networking & Connectivity (e.g., Corning, Nokia, Cisco):** As AI clusters grow, high-speed data transfer becomes paramount. Corning benefits from fiber demand for data center links, Nokia is exploring AI-integrated wireless networks (AI-RAN), and Cisco sees surging orders for data center switches—all critical for efficient AI operations. 3. **Storage (e.g., Western Digital, Seagate):** The AI data explosion requires vast capacity. Beyond high-speed memory (HBM), there's growing need for high-capacity HDDs to store training data, logs, video, and cold/archival data cost-effectively. This revaluation, however, is not a blanket endorsement. True reassessment requires concrete proof: AI-driven orders and revenue growth, upward revisions to company guidance, and sustainable improvements in profit quality, not just top-line sales. In essence, AI is not turning all old tech firms into high-growth stocks; it is selectively re-pricing the "old assets" of companies that are mission-critical for building the new AI infrastructure, transforming their legacy capabilities into renewed growth engines.

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From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

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Probability in the Price: How World Cup Odds Are Calculated

**The Probability in the Price: How World Cup Odds Are Calculated** Two major systems released their "championship probabilities" before the 2026 World Cup, and they disagreed on the favorite. Prediction market aggregators listed France at around **17%**, while the Opta supercomputer gave European champion Spain **16.1%**. These numbers look similar, but their production methods are fundamentally different. The market's **17%** is the **price** that clears after hundreds of millions of dollars in trading across platforms like Polymarket and Kalshi, where contracts trade between 0 and 100 cents, directly representing implied probability. This liquidity is provided by crypto-native market makers like Wintermute, though the market still has "the liquidity profile of an early-stage" asset class. In contrast, Opta's **16.1%** is a **simulated frequency**. Its model uses team data (including betting market odds as an input) to estimate match probabilities, then runs **10,000 full tournament simulations**, counting how often each team wins. Which is more accurate? There is **no rigorous, cross-tournament academic study** directly comparing their track records. However, a persistent **longshot bias**—where low-probability outcomes are systematically overvalued—observed in traditional betting for nearly a century, has also been found in modern crypto prediction markets. Research shows low-price contracts on Kalshi/Polymer less likely to pay out than their implied odds suggest. Unlike traditional bookmakers, prediction markets operate on **public blockchain ledgers**, making every transaction auditable and enabling such research. However, price formation is also influenced by **regulatory uncertainty**, as seen in recent US state-level bans and legal battles over jurisdiction. In summary, the "probability" you see is either a **market-clearing price** subject to behavioral biases and liquidity constraints, or a **model-simulated frequency** that partially incorporates market data. The question of which method is more reliable remains open, highlighting the importance of asking: **How was this number produced?**

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Probability in the Price: How World Cup Odds Are Calculated

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