An Intro to AI-Powered Investing: How Individual Investors Can Leverage LLMs
Artificial intelligence can enhance investment research and decision-making, but it should be used carefully.
Imagine having a personal analyst working 24/7—scanning financial news, crunching numbers and identifying key investing opportunities before they become mainstream. Artificial intelligence (AI) is no longer exclusive to institutional investors. Today, individual investors have access to powerful AI tools that can analyze financial reports, track market sentiment and assist in building a well-informed investing strategy.
With AI-powered investing, you can:
Analyze financial statements more efficiently.
Compare investment options.
Identify market trends before they become mainstream.
Avoid emotional decision-making by leveraging data-driven insights.
AI can be a valuable tool for long-term investors looking to refine their strategies—whether that’s managing a retirement portfolio, optimizing dividend income or making sense of economic trends. The key is understanding how to use it effectively. This is the first in a new series of articles exploring AI-powered investing. We start with a broad overview of AI’s strengths and limitations when using it to aid your investment analysis.
How AI Is Changing the Investing Landscape
The rise of AI in finance is already making a measurable impact. Consider the following data from CoinLaw:
AI-powered tools assist 30% of investors in making trading and investment decisions.
Robo-advisers, such as Betterment and Wealthfront, have grown their assets under management (AUM) to $1.4 trillion, a 15% year-over-year increase.
In 2023, AI-driven trading bots were used by 15% of investors to optimize their portfolios.
These developments signal a shift: AI is leveling the playing field, providing individual investors with sophisticated analytical capabilities once reserved for Wall Street firms.
What Are LLMs?
A large language model (LLM) is a type of AI trained on vast amounts of text-based data to interpret and generate human-like responses. In investing, LLMs can process financial reports, news articles, analyst research and earnings transcripts in seconds—helping investors quickly extract relevant insights.
Some of the most commonly used AI-powered investing tools are listed in Table 1.
Table 1. Popular LLMs for Investing
How AI Can Help Individual Investors
LLMs serve as valuable research assistants for investors, helping analyze financial documents, summarize market trends and even generate investment theses. They are designed to process large amounts of text-based data, making them useful for:
Summarizing financial reports: They can quickly extract key insights from Form 10-Ks, earnings calls and analyst reports.
Generating investment theses: LLMs structure qualitative data to formulate a clear, research-backed view of a stock.
Comparing stocks: They are able to conduct side-by-side comparisons of fundamental metrics, risks and qualitative factors.
Extracting sentiment: LLMs are good at analyzing how positive or negative management commentary is over time.
Understanding AI’s Limitations
While AI offers powerful advantages, it is not without risks. Investors should be aware of these limitations.
Prompts Matter: The feedback LLMs give you is highly dependent on the commands or questions, called “prompts,” that you give them. If a prompt is not worded correctly, AI may give you an answer that is either incomplete or excludes a fact that could alter your opinion about a stock.
No Real-Time Stock Data: Most LLMs do not provide up-to-the-minute pricing. Always check official financial platforms for real-time information.
No Trading Execution: AI can analyze investments, but it cannot place trades or manage portfolios.
Risk of AI Hallucinations: Sometimes AI generates misleading or incorrect financial data and information, which can lead to poor investing decisions if taken at face value.
How to Minimize AI Risks
There are steps investors can take to maximize AI’s usefulness while avoiding its pitfalls.
Cross-check AI insights with official sources like the U.S. Securities and Exchange Commission (SEC), Bloomberg and reputable analysts.
Use multiple AI tools to compare responses and detect inconsistencies.
Phrase prompts carefully to reduce ambiguity and prevent AI from generating speculative information. Treat these models as if you are speaking to a new, untrained employee. Give it follow-up prompts to ensure it returns the key information you seek.
Avoid asking AI for real-time stock prices since most LLMs do not access live market data.
How to Use AI for Investment Analysis
Using AI for investment analysis requires both using the right AI and giving it the right prompts. Here are the key steps to follow.
Choose the Right AI Tool
Different LLMs specialize in different areas. Investors should select a tool based on their needs.
ChatGPT is good for in-depth research and report analysis.
Gemini works for tracking market sentiment and providing macroeconomic insights.
Claude should be used for portfolio risk assessment and long-term strategy evaluation.
Structure Prompts for Better Insights
A prompt is the input or question you provide to an LLM to receive a response. AI responds best to specific, well-structured questions. The following are examples of some basic prompt structures.
Direct Query: “Summarize Tesla’s latest earnings report.”
Comparison Request: “Compare Apple and Microsoft’s financial statements for growth potential.”
Trend Analysis: “What are the latest macroeconomic trends affecting the stock market?”
Sentiment Analysis: “Analyze the sentiment of recent analyst reports on Nvidia.”
You can also use more advanced prompting techniques.
Specifying a Format: “Summarize Nvidia’s earnings in bullet points.”
Setting Parameters: “Analyze Amazon’s stock performance over the last five years, focusing on revenue growth and P/E ratios.”
Refining Responses: “Give me a simpler explanation of what an earnings beat means for a stock’s price movement.”
It is crucial to craft precise and structured prompts to get the best investment insights from AI. Follow these tips for high-quality, high-impact prompts.
Tip #1—Be specific and provide clear instructions: Instead of asking, “Is Apple a good investment?”; say, “Summarize Apple’s latest earnings call and compare its revenue growth to Microsoft.”
Tip #2—Use context to improve the AI’s understanding. Providing background information can help refine responses. Example: “Assume I’m a long-term investor focusing on dividend growth. How does Johnson & Johnson compare to Procter & Gamble?”
Tip #3—Break down complex questions into steps. If the analysis requires multiple steps, structure your request sequentially. Here is an example.
“Step 1: List Tesla’s revenue growth over the past five years.
Step 2: Compare it to Ford’s over the same period.”
Tip #4—Request data to be presented in a structured format. Tables, bullet points or structured summaries improve clarity and usability. Example: “Summarize Nvidia’s latest earnings call in bullet points.”
Tip #5—Guide the AI’s role for better insights. Tell the AI how you would like it to approach the response. Example: “That summary was too broad. Can you narrow it down to focus on revenue growth and competitive risks?”
Verify and Cross-Check Information
LLMs can generate valuable insights, but they are not perfect. Investors should always validate AI-generated insights with real-world data sources such as SEC filings, earnings reports and financial databases.
Integrate AI Into a Broader Investing Strategy
AI should complement, not replace, fundamental and technical analysis. Investors can use AI-driven insights alongside traditional investment research to build a well-rounded strategy.
The Future of AI for Individual Investors
The AI revolution isn’t just coming—it’s already here. AI-powered tools are giving individual investors more control, deeper insights and greater efficiency in managing their portfolios.
Here some key takeaways that will help you make better use of AI:
AI can enhance investment research and decision-making, but it should be used carefully.
LLMs can summarize reports, analyze sentiment and compare investment options to streamline portfolio management.
Investors should verify AI-generated insights with real-world data and avoid relying on AI for real-time stock prices or execution.
AI doesn’t replace financial advisers or personal judgment, but it can serve as a powerful research tool.
As this series continues, we’ll explore how to:
Use AI for fundamental analysis and stock valuation.
Apply AI to portfolio optimization and asset allocation.
Recognize and avoid common AI-driven investing mistakes.
Individual investors can gain a competitive edge by strategically adopting AI while maintaining a disciplined investing approach.