At 3:40 p.m., a heavyweight tech stock in the Hong Kong market surged sharply on heavy volume intraday; five minutes later, all kinds of message groups were flooded with chatter, yet the evening recap push didn't arrive until
Without interpretation, data alone makes AI, at best, a sophisticated market terminal. What truly sets intelligent market analysis apart from traditional quantitative monitoring is the semantic understanding capability that large models bring: the questions it answers are not "what is rising," but "why it is rising, and whether the logic behind the rise can last." Is this sharp rally news-driven, or the result of a tug-of-war between funds? Is it the starting point of a main-theme rotation, or an impulsive release of market sentiment? Are stocks in the same sector rising in unison, or diverging? Answering these questions requires weaving fragmented information into a coherent market narrative—and that is precisely the task language models excel at, and the key step for AI trend tracking to move from "recognizing patterns" to "understanding logic."
Delivery Layer: From People Seeking Opinions to Opinions Finding People
Generative AI is also changing how
The rational way to use AI is to give it a clear role: real-time intelligence handles perception and questioning—"what is happening, and what are the possibilities"—while key judgments and position decisions must still be left to after-hours in-depth review and independent human thinking. One practice worth adopting is to establish an "interpretation archive" mechanism: record every attribution the AI makes during trading hours, verify it against the actual market movements of the following day, and continuously evaluate the hit rate of the model's explanations. Let intelligent market analysis evolve within a closed-loop review process, rather than "running wild" in its
