How AI Is Used in Market Analysis
AI is doing genuinely useful work in market analysis, and almost none of it is prediction. Here is what it actually does, explained without the jargon.
If you have read anything about artificial intelligence and financial markets in the last couple of years, you have probably come away with the impression that AI is being used to forecast prices. That is the version that sells subscriptions. It is not, for the most part, the version that is actually in use.
What AI is genuinely good at is handling volume. A single trading day across global markets produces more price data, company announcements, economic releases and commentary than any person could read in a month. AI does not need to be clever to be useful there. It needs to be fast, consistent and tireless.
1. Summarising and grouping information
The most common use is the least glamorous: taking a large amount of material and reducing it to something a human can read in two minutes. That might mean condensing forty company announcements into a paragraph each, or grouping a day of price movement by sector so that you can see whether a fall was broad or concentrated in one place.
Example. Suppose the Australian share market closes down 1.2%. On its own that number tells you almost nothing. Grouped by sector, it might show that materials fell 3% on a weaker iron ore price while healthcare and utilities were flat. That is a completely different story from a broad, uniform decline, and it took a machine two seconds to organise.
2. Classifying news and sentiment
Language models are reasonably good at reading a headline and deciding what it is about — a rate decision, a profit downgrade, a regulatory action, a merger. They are also used to score whether the tone of coverage is positive, negative or neutral.
This is useful for triage: it helps surface the twenty items worth reading out of two thousand. It is much weaker as a signal. Sentiment scoring struggles with sarcasm, with the difference between a rumour and a confirmation, and with news that is bad for one company and excellent for its competitor. Treat it as a filing system rather than an opinion.
3. Pattern recognition in historical data
Machine learning can identify statistical relationships across long histories — how an asset has typically behaved after a particular kind of move, or how two markets have tended to move together. This is real analysis and it has real value in understanding what has happened.
The trap is treating a historical relationship as a rule. Relationships that held for a decade can break in a fortnight when the underlying conditions change, and the periods when they break are precisely the periods that matter most to your money. A model has no way of knowing that this time is different, because that information does not exist in the past.
4. Translating technical language
One of the quietly valuable uses of AI is explanation. Ask what a bid-ask spread is, why overnight financing is charged on a leveraged position, or what a franking credit does, and you get a clear answer at whatever level of detail you asked for, without anyone sighing at you. For people who are interested in markets but were never taught the vocabulary, this alone removes a real barrier.
5. Monitoring and alerting
Machines are better than people at watching something for eight hours without losing attention. AI-assisted monitoring can flag unusual volume, a sharp move against a recent range, or the appearance of a particular kind of announcement. Note that flagging is not deciding: an alert tells you something changed, not what you should do about it.
Where the honest limits are
AI in market analysis is a reading and organising technology. It reduces the cost of understanding what has happened and what is happening. It does not reduce uncertainty about what happens next, because that uncertainty is not caused by a shortage of processing power — it is caused by the fact that future prices depend on decisions that have not been made yet.
Anyone quoting you an accuracy percentage for market predictions should be asked three questions: measured how, over what period, and audited by whom. In our experience the conversation rarely survives the third question.
At Murray Capholm we use AI for the first four things on this list. We do not use it to predict prices, we do not run an automated trading system, and we do not have access to anybody’s positions. The FAQ sets out exactly where the line sits.
In summary
- AI in market analysis is mostly about summarising, classifying, pattern-finding and explaining — not forecasting.
- Grouping data changes what a headline number means, which is where most of the practical value sits.
- Sentiment scoring is a triage tool, not a signal.
- Historical relationships can break, and they tend to break when it matters most.
- Uncertainty about future prices is not a computing problem, so more computing power does not solve it.
Curious how this looks in practice? See how Murray Capholm works, or register your interest and we will walk you through it in plain English. Registration is free and commits you to nothing.