AI in Finance

TL;DR for operators Fedspeak classification is not the same thing as sentiment analysis with better stationery. A sentence about “strong employment” can be dovish in one macro regime and hawkish in another. The paper behind this article tackles that problem by giving an LLM a structured reasoning scaffold: extract economic entities, map their relations, reason through monetary-policy transmission paths, then classify the stance as hawkish, dovish, or neutral.1 ...

TL;DR for operators The paper behind this article is not a victory lap for AI stock prediction. It is a more useful thing: a controlled comparison of how different optimisers behave when a MambaStock model tries to forecast one-week-ahead S&P 500 returns.1 The operational read is simple. If your priority is lowest forecast error in this setup, the safer family is still adaptive or momentum-based optimisation: RMSProp, Adam, Nesterov, and SGD with momentum. If your priority is fast experimentation across many hyperparameter settings, Lion deserves attention because it trains quickly and tolerates a broader region of settings. If your priority is Lion-like speed without quite so much convergence thrashing, Roaree is interesting: it smooths Lion’s hard sign update and improves Lion’s test error and training stability. ...