JPYStableCoin (JPYSC) Sentiment & Fear and Greed Index
As of July 5, 2026, JPYStableCoin's Ruma Fear & Greed Index is 0 (Extreme Fear), it holds 0.00% of crypto social mindshare. These signals are computed by Ruma from social posts across crypto Twitter/X and other sources, scored with large language models rather than keyword counts.
Updated continuously · Source: Ruma
Latest JPYStableCoin insights
The XRP Ledger community warned about counterfeit tokens using the name 'JPYSC' from SBI. SBI launched the JPYSC token on June 24, but fake tokens are circulating on the XRPL. Users should verify token authenticity to avoid scams.
SBI Holdings, a Ripple partner, and Startale Group launched JPYSC, Japan's first trust-type yen stablecoin on Ethereum, providing a regulated digital yen option for the Japanese market.
A warning has been issued regarding scam $JPYSC tokens on the XRP Ledger. The legitimate $JPYSC token has not been publicly announced for the XRPL under a known issuer address; any similar ticker on the network is fraudulent. Users are advised to be cautious of scammers using this ticker.
SBI Group is supporting Startale Group and the YJPYSC stablecoin, issued by SBI Shinsei Bank, for global payment use. Startale and Netstars signed a memorandum of understanding to promote payments using JPYSC, expanding the stablecoin's adoption in international transactions.
Frequently asked questions
What is JPYStableCoin's Fear & Greed Index?
JPYStableCoin's Ruma Fear & Greed Index is currently 0 out of 100, which is Extreme Fear. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.
Is JPYStableCoin bullish or bearish right now?
Ruma scores JPYStableCoin's social sentiment as bullish, bearish, or mixed based on LLM analysis of the crypto social conversation. Sentiment reflects market mood, not financial advice.
How does Ruma measure JPYStableCoin sentiment?
Ruma reads every relevant social post about JPYStableCoin across crypto Twitter/X and other sources and scores it with large language models — capturing bullish/bearish tone, emotion, and who is speaking (from retail to smart money) — rather than counting keywords.