Giuseppe Sette, President of Reflexivity, an AI-market research firm used by leading hedge funds and institutional investors, has compiled a list of the top questions Wall Street investors are asking on their AI platform right now. Reflexivity uses structured and unstructured data in their knowledge graph.
Reflexivity is owned by Stan Druckenmiller, SoftBank, General Catalyst, Thomas Peterffy, Greycroft and is partnered with Microsoft. Over the past month, Reflexivity’s AI fielded hundreds of queries from professional investors, hedge fund managers, and research analysts.
The prompts paint a remarkably clear picture of what the market is thinking. Forget the headlines, these are the questions that investors really care about.
1. Are we at a bottom yet?
In times of turmoil, pattern recognition becomes obsession. Investors asked AI to identify “capitulation” across time and asset classes, combing through decades of data to detect whether the present echoes past bottoms, 1987’s crash, the LTCM implosion, 2008’s credit crisis, or March 2020. Common elements? VIX sigma spikes, 5%+ SPX drawdowns paired with dollar weakness, and sentiment collapses.
One query asked: “When the S&P 500 drops 10% or more, which sectors lead the recovery in the next 3 months?” Another wanted to know if the VIX had “ever moved like this since the Asia Crisis of ’97.” For AI, it’s a test of historical memory. For humans, it’s an existential market question.
2. What happens after big moves?
If day one of a rally is hope, day two is analysis. Multiple queries focused on historical SPX returns following large single-day moves – especially up 6% or 10% or down multiple days in a row. The goal: determine if momentum persists or fades, and whether positioning should flip.
Some users even back-tested idiosyncratic rules: “What happens when SPY drops 5% from 50-day highs and Goldman Sachs calls it a buy?” Others asked about one-day or one-week forward returns based on VIX levels breaching standard deviation bands. The obsession is clear: in an increasingly nonlinear market, timing is everything.
3. What happens to my stock when macro hits?
The intersection of macro and micro remains fertile ground for AI inference. From the impact of rate hikes on Fannie Mae and Freddie Mac to correlations between coffee prices and coffee stocks, users looked to quantify ripple effects that are often anecdotal or qualitative.
Tariffs were a recurring motif. From Trump-era shocks to the current U.S.-China tensions, investors asked AI to dissect which industries are least exposed to cross-border frictions, frequently zeroing in on MedTech, consumer staples, and domestic-focused sectors. One query simply asked: “Which S&P 500 companies have the least China exposure?” Risk-averse capital, in search of sanctuary.
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4. Is this a signal – or just noise?
Investors want AI to cut through the fog of price action. A recurring set of queries focused on pattern recognition: “cup and handle” formations in Nasdaq stocks, ATR-based breakout strategies on 15-minute timeframes, or mean-reversion signals on a 90-day z-score basis. The rise of technical analysis, reborn through code.
Other questions leaned quantamental: Is high price-to-book a reliable signal? What’s the Sharpe ratio of momentum names during certain months? AI isn’t just summarizing models, it’s being asked to generate them, often on the fly, with user-specified assumptions and data windows.
5. What’s the right macro trade now?
TBT, TLT, HY OAS, and the dollar real (USDBRL) featured in macro queries that sought to build or validate tactical trades. Users didn’t just want correlations – they wanted causality. “What macro factors explain TBT volatility?” and “Is there a relative value trade between HY and the S&P?” were common frames.
Cross-asset thinking also emerged: Utilities vs Treasuries, SPX vs gold, dollar vs commodities. AI is becoming a kind of macro sounding board, where a trade idea is stress-tested through historical lenses before capital is deployed.
6. What quant strategies still work?
In a crowded market, repeatability is king. Dozens of queries asked AI to back-test rules: buy after three down days, exit on month-end, fade spikes in the VIX, long low-beta ETFs. A few users even asked for “a strategy that had consistent positive returns over the past five years.”
There’s an underlying question here: is anything still working? In a world awash with noise, investors want AI to rediscover the signal. Not just to mimic what quants already do – but to provide judgment, filtering, and even creativity.
Conclusion – the rise of AI as market confidant
What these queries reveal is not just what investors think, but how they think. AI is becoming a new kind of research assistant, one that never sleeps, can remember the 1987 crash as well as the dot.com bear market, and back-test your hunch before you’ve finished your coffee.
The line between strategist and system is blurring. And if these queries are any guide, the investors asking the smartest questions of AI may soon be the ones with the most resilient edge.
In this quiet shift, we may be witnessing not just the automation of research, but the emergence of a new language for market intuition itself.
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