DailyIQ
Last updated1 minute ago

NVDQ·GraniteShares 2x Short NVDA Daily ETF

$8.60
+0.48 (+5.91%)
After Hours
High
$8.73
Open
$8.31
Net Assets
$17M
52W High
—
Low
$8.02
P. Close
$8.60
Expense Ratio
1.05%
52W Low
—
Div. Yield
0.55%
Next Ex-Div
~Dec 23
Category
Trading--Inverse Equity
Inception
Oct 18, 2023
Technical Score (1D)
5
BEARISH
News Sentiment
64
BULLISH

What's happening to NVDQ today?

NVDQ (GraniteShares 2x Short NVDA Daily ETF) is a fund giving 2x inverse daily exposure to NVIDIA Corp (Inverse Leveraged Exposure) (NVDA); it moves opposite to that security. On Oct 11, analysts highlighted that Nvidia’s $150 billion share‑buyback authorization signals management confidence in cash generation, while the company’s 953.4 % five‑year price gain raised questions about valuation sustainability. The same day, a Motley Fool piece noted that Nvidia had been flagged by a historic “Double Down” signal in 2009 and that a similar signal is now appearing for a company 1/100th the size of Nvidia, positioning the chipmaker as a top sector pick for new investors. A separate article from the same day reviewed five decades of dividend data, citing the 2009 signal that flagged Nvidia and suggesting that dividend‑paying firms, including Nvidia, have delivered superior long‑term returns. Another Motley Fool analysis praised Nvidia as a leading AI company in the early R&D phase of its rollout, projecting significant upside as the AI boom expands globally. Meanwhile, Ray Dalio warned that rising rates are eroding the equity‑bond advantage, noting Nvidia’s $21 billion free cash flow last quarter and suggesting that high‑growth tech firms may face tighter capital constraints. An article on Oct 11 also reported that Mirantis validated its k0rdent AI virtualization stack on Nvidia GB200 systems, earning NVIDIA‑Certified Hypervisor status and demonstrating Nvidia hardware’s suitability for high‑performance virtualization workloads. Finally, a piece from the same day described Nvidia’s strategy to broaden its AI ecosystem beyond GPUs, emphasizing land, power, networking, and cooling needs for large‑scale AI factories and noting that new financing initiatives are accelerating adoption.