One of Kalshi's most prominent traders tells WIRED he's swearing off Spotify-related markets until the issue is resolved. Top Kalshi trader Caleb Davies usually speaks to the press about how prediction markets help him rake in money. The Minneapolis-based IT worker estimates he's made $1.2 million overall across different prediction platforms, with $414,000 in winnings from Kalshi's culture markets alone. He especially enjoys wagering on music charts, because he carefully analyzes Spotify data to pick winners. "Every single morning, I'm going in, downloading the data, and updating my projections," he tells WIRED.

Traders work on the floor of the NYSE in New York Traders work on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., June 22, 2026. Traders work on the floor of the NYSE in New York Traders work on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., June 22, 2026. The markets are souring on artificial intelligence, but is this the bubble being burst? Today, we're discussing a rocky week for the AI industry's finances and how California's proposed billionaire's tax is changing the political posture of the state's governor. AI is facing a financial stress test, but the bubble hasn't popped After the share prices of Alphabet, Samsung, and SK Hynix dropped, a global stock selloff caused markets worldwide to slump.

Market microstructure has long classified trading activity by its informational role: an informed trader demands liquidity by trading in the direction of private information, while a market maker supplies liquidity by absorbing that order flow and earning the spread in compensation Kyle (1985); Glosten and Milgrom (1985). This classification is typically recovered from the data the classifier requires: signed order flow, quote revisions, or the sequential-trade structure of the market. The classification is harder to apply to an algorithmic strategy whose internal logic is unobservable. However, the signals or optimization problems generating the decisions of a typical quantitative fund are not visible, even though the trades and reported positions may be available. This paper shows that the liquidity role of such a strategy (consumer or provider) can be recovered from realized portfolio costs and trade decisions alone, without observing quotes, order flow, or any other microstructure-specific signal.

We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence. Calibration targets the pseudo-outcome ψDRt; under estimator consistency, this yields asymptotically conservative CATE containment (Corollary 6). Temporal block cross-fitting preserves switch-coefficient mixing bounds and the DML product-bias rate up to an explicit coupling remainder.

This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury market and bonds issued by the European Central Bank (ECB). To enhance predictive performance, macroeconomic variables are incorporated. The findings for both markets are separately analyzed and compared. To this end, we propose a robust model evaluation framework combining statistical accuracy metrics - such as RMSE, MAE, and directional accuracy - with the economic relevance of a quantitative bond trading strategy. Results show that NNs consistently outperform traditional models in both forecasting accuracy and portfolio performance. For the U.S., the most effective approach is a direct-forecasting NN that incorporates DNS factors to reduce the dimensionality of zero-rate data and an Autoencoder (AE) to extract macroeconomic features, while for Europe, the optimal model is a factor-based NN using PCA-derived zero-rate factors without the integration of macroeconomic variables. Overall, the paper demonstrates how combining traditional modeling approaches with modern ML techniques and evaluation can improve yield curve forecasts and support applications in fixed-income portfolio construction.

Google Finance finally has a standalone Android app, with an iOS version on the way. This gives people access to real-time market data, a live financial news feed and the platform's AI research tool. The company says more features from the web experience will arrive for the mobile app in the coming months. The web experience is also receiving some major upgrades, as an AI-heavy redesign exits beta. There's an upgraded portfolio feature that consolidates all investments in a single dashboard, complete with performance data and insights on asset allocation.

People visit the Delta Electronics booth during the annual Computex trade show in Taipei, Taiwan, on June 3, 2026. Thailand's stock market is having the best year among Southeast Asian peers, as investors discover an unlikely source of exposure to the global artificial-intelligence boom. Much of that gain has come from Delta Electronics (Thailand). The maker of power systems for AI data centers has surged more than 80% this year and became Thailand's first $100 billion company, large enough to be worth more than the next four largest Thai stocks combined. While the country lacks the semiconductor champions of Taiwan or South Korea, investors are increasingly recognizing its role in supplying the infrastructure behind AI. "Thailand isn't a pure AI market, but its exposure to data centers, electronics, power systems and digital infrastructure gives investors a new way to view Thai equities beyond the traditional tourism, banks and domestic consumption cycle," Bloomberg Intelligence Strategist Sufianti said in a note. Delta's rise is the clearest evidence of that shift.

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit. Qualcomm will acquire the Silicon Valley chip startup Modular for nearly $4 billion. The companies announced the acquisition on Wednesday; Qualcomm said it expects to issue up to 19.2 million shares of common stock in the deal, which works out to just under $4 billion based on the company's last closing share price. The deal, which includes $300 million for Modular employees, comes nine months after the chip startup raised $250 million at a $1.6 billion valuation . It's expected to close in the second half of this year.

'AI cannot and will never render us obsolete' Cory Doctorow at home in Los Angeles. 'AI cannot and will never render us obsolete' Cory Doctorow at home in Los Angeles. The writer who coined the word'enshittification' tells us why AI will never deliver what it promises - and why it still appeals so much to those in power A "centaur", in automation theory, is a person assisted by a machine, and a "reverse centaur", hero of Cory Doctorow's new book, The Reverse Centaur's Guide to Life After AI, is a "human who is conscripted into acting as an assistant a machine". Every warehouse worker who ever had to urinate in a water bottle because they couldn't otherwise meet the fulfilment targets set by an algorithm is a reverse centaur. Reaching into the future, everyone who has to sit in a self-driving truck to make sure it doesn't crash, presumably on minimum rather than truck-driver wages, is a reverse centaur; as is every lawyer no longer on lawyer's money checking Gemini's command of precedent, every indie band scraping a living doing covers of AI-generated hits, and so on. That, anyway, is the promise: AI is coming for your job, and it is coming for your kids' jobs, and there is no point fighting it because the future's already here.

AI boom sees investors shift from Japan's value to growth stocks Some see Japanese equities as an attractive way to diversify away from U.S. stocks while still benefiting from the global artificial intelligence rally. Japanese equities, long regarded by global investors as a value market, are beginning to attract growth funds, as artificial intelligence-linked firms power to the top of market-cap rankings, beating out the manufacturers and telecoms giants that dominated for decades. "We have been raising our exposure to Japan based on the growth prospects of Japanese companies" under a strategy of investing in innovative firms globally, said Kei Takizawa, senior investment strategist at AllianceBernstein Japan. The nation's firms are playing an increasingly critical role in building AI infrastructure, he added. Investors had historically classified Japan's equities as low-growth value stocks due to the country's sluggish economic growth and declining population.