Generative artificial intelligence (GenAI) produces new content and insights rather than only returning a fixed prediction. In financial research, it can summarize earnings reports and news, analyze market sentiment, and simulate scenarios for stress-testing investment strategies. section 1.3.2
Use GenAI as a research and insight-generation tool:
- Summarize large collections of company information, including earnings reports and news articles. section 1.3.2
- Ask detailed questions about management style or other company characteristics, then combine answers into an investment scorecard. section 8.0
- Generate possible insights and help identify candidate features for narrower machine-learning models. section 8.2.2
- Integrate large language models from OpenAI, Google, or Hugging Face through Python and configured model parameters. section 8.0
From generic advice to structured research
Instead of asking an LLM to recommend three stocks, ask about a specific company’s management style and other investment-relevant details. Record the responses in a scorecard and apply additional due diligence before making a decision. section 8.0
GenAI is broader and more creative than discriminative machine learning, but broader capability does not remove uncertainty. LLM responses may hallucinate or rely on outdated information, and neither GenAI nor traditional ML reliably eliminates risk in volatile markets. Narrow, well-defined uses are more defensible than promises of universal forecasting. section 8.0 section 8.2.4
GenAI and traditional ML can work together: GenAI explores information and suggests useful features, while a focused ML model evaluates historical data for a defined outcome. Choosing the tool according to the task matters more than treating either approach as universally suitable. section 8.2.2 section 8.1.5
