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Source document· June 29, 2026

Prediction Markets Emerge in Japan, With Shopping Vouchers

View original at finance.yahoo.com
Prediction Markets Emerge in Japan, With Shopping Vouchers (Bloomberg) -- Even as prediction platforms like Polymarket and Kalshi surged into the global mainstream in recent years, Japan — like its Asian neighbors China and South Korea — remained a no-go zone due to strict anti-gambling regulations…
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  • He compulsively opens the prediction apps whenever he has a spare moment or when there is news that might move the odds

    60% confidence
  • Japan, like China and South Korea, remained a no-go zone for prediction platforms due to strict anti-gambling regulations, but new startups are finding ways to skirt the rules

    60% confidence
  • Prediction market apps are addictive; he has earned more than 10,000 yen a month in points on Miraima and similar apps

    60% confidence
  • Miraima has amassed close to 1 million monthly users since its debut in November, helped by interest in recent sporting events like the World Cup

    60% confidence
  • Miraima is seven months old and is Japan's largest prediction market platform, spearheaded by Gen Z entrepreneurs

    60% confidence
  • Japan's nascent prediction market platforms are closely modeled on Polymarket and Kalshi in that they enable users to bet on outcomes of live events, but unlike those platforms no money changes hands — the entire system is built on non-monetary virtual tokens

    60% confidence
  • Japan's pachinko industry is worth approximately $100 billion and employs an indirect payout system that prediction market apps emulate as a legal workaround

    60% confidence
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Satellite-Terrestrial Network Integration Acceleration
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ING Group
Both facts record the same metric (shares_outstanding) for ING Group at the identical observation date (2025-12-31). FACT A states 2,902,437,688 shares; FACT B states 2,902 million shares (2,902,000,000). The difference is 437,688 shares (~0.015%). This is a genuine value conflict, though the discrepancy appears to result from FACT B rounding to the nearest million while FACT A provides the precise count.
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