Guide · 6 min read
AI stock analysis, explained without the hype
"AI stock analysis" covers a wide range of things: language models summarising filings, statistical models ranking momentum, and rules-based screens that fire when a condition is met. None of them predict the future. What they do well is read far more market data than a person can in a morning, and tell you where something changed.
What the data actually is
Useful analysis starts with inputs you can name: daily and intraday price bars, volume and relative volume, moving averages such as the 20/50/200 EMA, ATR for volatility, options open interest and implied volatility, earnings dates, and recent news or social mentions. If a tool won't tell you which inputs it uses, treat its output as entertainment.
Rules first, AI second
The reliable pattern is: rules narrow the universe, AI ranks and explains what survives. A rule is auditable — "price above the 200 EMA, relative volume over 2, inside a prior resistance zone". An AI ranking on top of that adds context (sector behaviour, news tone, recent similar setups). Reverse the order and you get a number nobody can defend.
Which AI is best for stock analysis?
There is no single winner, because strategies aren't ranked on one scale. General chat models are good at summarising a filing or an earnings call and bad at pricing options. Quant scores are good at relative ranking and blind to a headline that just broke. Signal tools like Economove sit in between: rules-based screens with an AI ranker and the rules exposed. Pick the one whose inputs match the trades you actually place — see the side-by-side comparison.
How to sanity-check any signal in 60 seconds
- Can you see the rules that fired? If not, skip it.
- Is there an invalidation level — where the idea is simply wrong?
- Does the position size fit your account, not the tool's demo account?
- Is earnings or a macro print inside your holding window?
- Is the quoted option price a real print, or a model estimate?
What accuracy claims really mean
A published win rate says more about the sample window than the method. Economove deliberately publishes no accuracy figure: a signal is an educational indication that a rule triggered — a potential disruption in the market — not a probability of profit. No strategy is inherently better than another; the fit to your risk and timeframe is what matters.
See the rules, not just a score
Economove shows every rule that fired, the invalidation level, and the risk on each idea.