Stock Forecasting

When financial AI meets the optimizer arms race, the stakes are measured in both milliseconds and market moves. The recent From Rattle to Roar study tests this premise with MambaStock — a selective state-space model — trained to forecast S&P 500 weekly returns. The twist: pitting eight widely-used optimizers against a new family called Roaree, designed to capture Lion’s speed while taming its instability. Why Optimizers Matter More in Finance Than You Think In financial forecasting, milliseconds can mean the difference between execution and regret. This makes optimizer choice not just a theoretical concern but a practical lever for profitability. The study reinforces that adaptive-rate, momentum-based methods (Adam, RMSProp, Nesterov) deliver the lowest test errors for noisy, small-magnitude financial returns. Vanilla SGD struggles in this regime; AdamW’s decoupled weight decay over-regularizes, slowing convergence in already weak-signal environments. ...

In the world of financial AI, where speed meets complexity, most systems are either too slow to adapt or too brittle to interpret the nuanced messiness of real-world finance. Enter FinKario, a new system that combines event-enhanced financial knowledge graphs with a graph-aware retrieval strategy — and outperforms both specialized financial LLMs and institutional strategies in real-world backtests. The Retail Investor’s Dilemma While retail traders drown in information overload, professional research reports contain rich insights — but they’re long, unstructured, and hard to parse. Most LLM-based tools don’t fully exploit these reports. They either extract static attributes (e.g., stock ticker, sector, valuation) or respond to isolated queries without contextual awareness. ...