Financial AI

In technical analysis, few concepts are as foundational as support levels — those invisible lines where prices tend to stop falling, bounce back, and spark new rallies. For decades, traders have relied on hand-drawn trendlines, Fibonacci ratios, and moving averages to guess where those turning points might be. But what if the real market structure is too complex, too dynamic, and too subtle for static rules? Enter DeepSupp, a new deep learning architecture that doesn’t guess support zones — it discovers them. By analyzing evolving market correlations through attention mechanisms and clustering latent embeddings, DeepSupp offers a glimpse into a future where support level detection is less of an art, and more of a science. ...

Can a model trained to think like a day trader ever truly understand long-term market moves? Most financial AI systems today seem stuck in the equivalent of high-frequency tunnel vision — obsessed with predicting tomorrow’s returns and blind to the richer patterns that shape actual investment outcomes. A new paper, NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction, proposes a more grounded solution. It redefines the task itself, the architecture behind the prediction, and how we should even build the graphs powering these systems. ...