Here's a useful approach for market research:

You can leverage AI to quickly uncover historical patterns. Try asking your AI assistant: "What does the historical performance of the US stock market look like during January across different years? If these patterns hold, which stocks might be worth considering right now?"

The beauty of this method? You get a comprehensive analysis in seconds. The AI pulls historical data, identifies seasonal trends, and suggests potential opportunities based on cyclical patterns.

But don't stop there. Follow up with deeper questions like: "If I factor in current economic conditions and sector rotation, how would that change the analysis?" Or: "Which asset classes typically outperform during similar market conditions?"

This layered approach helps you move beyond surface-level information. You're not just getting investment ideas—you're understanding the reasoning behind them. It's especially useful when you're trying to spot whether current market movements align with historical precedent or represent something genuinely different.

The key is asking specificity questions that combine historical context with current data. That's when AI becomes a real research tool rather than just a suggestion engine.