Dhruv Bhutani / Android Authority
As much as I dislike AI, it’s hard to disagree that it has its use cases. As a purely scientific tool, it’s a great way to sweep through troves of documents, research reports, news, and distill trends. While that makes it particularly useful for research, there’s one more sector that makes heavy use of it. The finance sector, particularly high-frequency trading, has been using AI and machine learning for a while now. Custom AI solutions are also used for predictive analytics and portfolio automation. However, those tools depend on highly specialized in-house AI tools. What I’ve been wondering is — can a general-purpose AI like Google Gemini be optimised to perform similar functions?
I was curious to know if an AI model could make sense of my investments, identify risks, and maybe even suggest strategies that were worth testing.
A few months ago, I decided to run a small experiment. Instead of blindly following every piece of advice from my portfolio advisor, I wanted to see what would happen if I brought Google’s Gemini AI into the mix. My idea was pretty straightforward. Take the same account statements I usually share with my advisor, feed them into Gemini, and ask it to give me an analysis. I also fed it mutual fund fact sheets and portfolio disclosures and asked it to run deep research surveys into the sectors and funds I was investing in. I was curious to know if an AI model could make sense of my investments, identify risks, and maybe even suggest strategies that were worth testing. And I’d put some money behind it as well. What happened next was rather interesting.
Would you trust portfolio advice from an LLM? 18 votes Yes, if it was a dedicated model built ground-up from finance. 22 % Yes, I use ChatGPT and Gemini for advice. 33 % No, I trust my wealth manager. 11 % No, I manage my investments based on my own research. 33 %
Putting Gemini to work
Dhruv Bhutani / Android Authority
Using AI for investment advice largely works the same way as speaking to a financial advisor, and it starts with setting goals. Asking Gemini to just optimize my portfolio would invariably spit out vague advice because it has no grounding in what I expect from it or my risk appetite. So I spelled out what optimizing a portfolio meant for me. In my case, I wanted a strategy that leaned aggressively, prioritizing higher long-term returns over absolute safety. This would invariably result in an equity-heavy portfolio, but I was fine with that. I also defined my benchmark that the portfolio should aim to beat the Nifty 50 over a ten-year horizon while avoiding unnecessary duplication in the choice of sectors. I was fine with short-term volatility along the way if it meant better overall returns. Defining those goals upfront gave Gemini a direction to work with. It’s an excellent strategy to have when working with an LLM for any task, and particularly so if you’re working with financial data.
Next, I gave Gemini access to my holding statements. Based on my experience, Gemini didn’t fare too well with Excel documents, and feeding it raw data worked out much better; it was able to make sense of all the data in seconds. However, your mileage may vary. Regardless, I would highly recommend labelling essentials like the fund name, code, current value, invested amount, start date, returns so far, and more in a structured format. This makes it trivial for the LLM to understand your current holding statements and avoids unnecessary to-and-fro in cleaning up bad parsing of data.
There's a fair amount of hand holding needed to combine your data with additional insights.
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