Applications of AI for Securities Selection
With so much available information, today’s challenge is not accessing data, it is how to efficiently filter and act on the most relevant information — a perfect job for AI. We have recently discussed in detail how GenAI is a great approach for analyzing text and unstructured data, where Machine Learning is superior for gaining insights based on numerical data.
Today we’ll dig deeper into what that means for the specific tasks that portfolio managers deal with on a day-to-day basis.
GenAI tasks and use cases for portfolio managers
GenAI is especially helpful with language data, both spoken and written. GenAI is useful for any task that requires digesting reports, speeches, or publications, including:
- Analyze the business model and competitive landscape of a specific firm
- Summarizing earnings call transcripts
- Extract key takeaways from Fed meetings
- Capture insights from long reports
Machine Learning tasks and use cases for portfolio managers
Every portfolio managers knows that market behavior doesn’t always align with the most diligent fundamental analysis, so understanding the dynamics of prices is crucial to avoid opportunity costs and better inform portfolio decisions. The tasks where Machine Learning adds significant value include:
- Early identification of emerging stocks with strong risk-adjusted return potential
- Finding assets with potential that have not yet popped using traditional means
- Receiving alerts to better know when to act
For more information
Using Machine Learning to understand markets and make better investment decisions is our passion at Laplace Insights. If you’d like to learn more about how PSIMON, our purpose built machine learning solution, could work for you, please contact us.
