Stock Market

When financial markets move in unison, the patterns are rarely random. Beneath the noise of daily price changes, certain groups of stocks form tightly knit clusters—connected not just by strong correlations, but by relationships that remain structurally stable over time. The recent Finding Core Balanced Modules in Statistically Validated Stock Networks paper formalizes this idea through the Largest Strong-Correlation Balanced Module (LSCBM) framework. Why Traditional Stock Networks Fall Short Most stock network studies use a simple recipe: calculate correlations, set a threshold (say 0.7), and keep only edges above it. This approach is quick—but flawed: ...

In an age where TikTok traders and YouTube gurus claim market mastery, a new benchmark dataset asks a deceptively simple question: Can AI tell when someone really believes in their own stock pick? The answer, it turns out, reveals not just a performance gap between finfluencers and index funds, but also a yawning chasm between today’s multimodal AI models and human judgment. Conviction Is More Than a Call to Action The paper “VideoConviction” introduces a unique multimodal benchmark composed of 288 YouTube videos from 22 financial influencers, or “finfluencers,” spanning over six years of market cycles. From these, researchers extracted 687 stock recommendation segments, annotating each with: ...