A rough and historically atypical November for U.S. stocks

As the U.S. stock market paused for Thanksgiving, the outlook for November appears grim, with major indexes likely to finish the month in negative territory. Current assessments suggest that, with just hours left of trading, the S&P 500 is down 0.4%, the Dow Jones Industrial Average has slipped 0.29%, and the Nasdaq Composite has endured a significant drop of 2.15%. These figures indicate a stark underperformance, particularly among technology stocks. Unless an unexpected surge in stock prices occurs during Friday's abbreviated trading session, this decline may signal the end of a six-month winning streak for both the S&P 500 and the Dow Jones, while the Nasdaq's streak could be halted after seven months. Historically, November has been a strong month for the S&P 500, which has averaged a gain of 1.8% since 1950. Moreover, post-presidential election years typically see the index rise by around 1.6%. However, this year's performance deviates from these historical patterns, leaving analysts uncertain about future market trends. On a broader scale, U.S. futures remained relatively stable on Thursday evening as the market observed the Thanksgiving holiday. Across the Atlantic, Europe's Stoxx 600 index saw a modest rebound, increasing by 0.14% after earlier losses. In related news, Alibaba has launched its Quark AI Glasses, priced competitively at 1,899 yuan (about $268) and 3,799 yuan, positioning itself against Meta's Ray-Ban Display glasses. Additionally, Apple is under scrutiny from India's Competition Commission over its in-app purchase policies, facing potential fines that could reach up to $38 billion. Meanwhile, Russian President Vladimir Putin expressed a willingness for serious peace discussions, noting that the U.S.-led framework could serve as a foundation for future agreements. Lastly, analysts from Bank of America predict limited growth for the S&P 500 in 2026, citing a slowdown in supporting factors. In the realm of AI, experts suggest Europe may not lead in the construction of large-scale AI facilities but could carve a niche in smaller, cloud-focused operations, which could offer long-term value despite existing constraints.