AI in investing: a research assistant, not an oracle
Where AI can help organise information—and where verification still matters.
Begin with a defined task
AI can help summarise long documents, organise questions and compare the language of company disclosures. A useful request might be to identify the assumptions behind a forecast, then locate the original source. That is a different task from asking a chatbot which asset will rise tomorrow.
Go back to the source
Models can produce confident mistakes, invented references and outdated information. Check numbers against the original report and verify the date, currency, units and scope. A persuasive explanation is not evidence that a fact is correct or that a trade has an edge.
ASIC’s Moneysmart cautions against relying on general-purpose AI alone for money decisions. Such tools may not understand your circumstances or risk tolerance. Avoid entering private account details, credentials or sensitive documents into tools without understanding how they handle that information.
Test the process, not the pitch
For systematic research, define what information was available at the time. Keep a record of inputs, model changes and decisions. Otherwise, hindsight can make a process look more reliable than it would have been in use. Evaluate the costs of data and execution alongside any measured benefit.
Our division of responsibility
Liquidity Labs keeps strategy decisions and research context separate. The original TradingView strategy determines entries and exits. Scanner research helps organise the market view; a score is not a probability of winning and does not create, veto or resize a trade.
Use technology to make research more reviewable. Keep the investment decision grounded in verified information, your circumstances and appropriate professional advice where needed.
Explore signals and the full research workspace.
See pricing