Investing.com -- As stock markets took a sharp plunge over the last month on concerns that Trump’s tariffs would derail global trade, retail investors worldwide were looking for stocks to buy on the dip.
That’s what the high-level trend data analysis from WarrenAI—Investing.com’s newly launched generative AI chatbot entirely directed to financial markets—depicts.
*You can now try Warren AI’s full version - available for everyone for free via this link.
During the period that spanned from mid-March to April 21, selected users who got to test the beta version asked WarrenAI roughly 30,000 queries, navigating the troubled market with an extra expert boost.
Amongst these queries, an astounding roughly 45% were oriented to the buy-side, with 25% being asset-specific analysis and forecast (such as ‘Is this a good time to buy Nvidia stock?’) and another 20% on stock picks (such as ‘Which stocks should I buy now?’).
Another popular trend was WarrenAI’s own portfolio management feature (for example, ’Create a growth-focused portfolio with a $100,000 budget.’) Those represented roughly 15% of the queries.
Although also significant, questions directly related to the macro events threatening the stock market (such as ’What’s the likely impact of Trump’s Tariffs on stock markets?’) represented only 15% of total searches.
The remaining main trends were:
- Forex & Currency Analysis (~10% of relevant queries)
Example: ’What’s the outlook for the EUR/USD today?’
- & Crypto Analysis (~5% of relevant queries)
Example: ’What is a good entry point for Bitcoin around current levels?’
(*The remaining ~10% of queries are classified as miscellaneous, meaning they were too broad to fit into any of the larger categories mentioned above.)
This data also coincides with JPMorgan’s research, which notes that retail investors poured around $21 billion into equity markets at the height of the selloff (between April 3 and April 16) - a number significantly larger than the normal average daily inflow.
During that period, institutional investors stayed mostly on the sidelines, as per Bank of America’s Global Fund Manager Survey and prime book data.
Here’s How WarrenAI Is Helping Our Users Outperform
While the jury remains out on whether retail or institutional investors made the right decision for the longer haul, those who bought the S&P 500 right after the ‘Liberation Day’ crash are already up a solid 6% as of this writing.
However, numbers are far more attractive for those who were able to direct their buying power to the right assets during the market dip.
That’s where WarrenAI has proved a real game-changer.
During the market crash, users got to experience what can only be described as a revolution for retail investing.
Here are some of the most recurring questions they sent during that period, followed by WarrenAI’s responses:
Source: WarrenAI
Source: WarrenAI
Source: WarrenAI
Source: WarrenAI
- “Give me a list of currency pairs to invest in today with low risk, specify buy/sell action and price range.”
Source: WarrenAI
Warren AI also offers several pre-designed prompts, which can help you get even further. Just check the bottom of the screen for shortcuts on everything WarrenAI can help you with:
Source: WarrenAI
If you haven’t yet tested WarrenAI, do it now here! Free users get 10 prompts, while InvestingPro members get 500 per month.
More Insight on How WarrenAI Works
In order to further help our users get the best out of WarrenAI, Investing.com’s CTO Yonathan Adest sat down with Jesse Cohen and Thomas Monteiro on a live webinar last April 23.
Not only did the team of experts discuss Warren’s main trends and what they indicate about the current market scenario, but they also gave a premium walkthrough on how WarrenAI works from behind the scenes, as well as how to get the best from it.
Check out the recording on X and on YouTube (below):
*For more info on how WarrenAI compares to ChatGPT, click here.
Disclaimer
When researching how people use financial AI models, protecting user privacy is paramount. For this project, we carefully analyzed anonymized WarrenAI user interactions while maintaining strict privacy standards throughout our research process.
Our analysis focused on identifying broad usage patterns without accessing or retaining any personally identifiable information. By examining aggregated query categories and interaction types, we were able to identify high-level trends in how users engage with financial AI.
We analyzed approximately thirty thousand anonymized queries from Investing.com users. These were filtered for investment and financial relevance, focusing on common themes like stock analysis, market research, and portfolio management. This approach allowed us to derive aggregate finance-related insights about the types of information users seek, how interaction patterns differ across user segments and the specific financial analysis tasks that users commonly request from AI systems.
All data was handled according to strict privacy protocols, ensuring that individual user information remained protected while still enabling valuable insights about broader usage patterns.