{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/c4b2bd521cf1448f8113c494fe93fc5b\" frameborder=\"0\" width=\"1838\" height=\"1378\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1378,"width":1838,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1378,"thumbnail_width":1838,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/c4b2bd521cf1448f8113c494fe93fc5b-f0d880c90b83f45f.gif","duration":488.754,"title":"Dynamic Comps Engine: A Fairness Opinion for Company Valuation 📊","description":"In this video, I walk you through my Dynamic Comps Engine, a Python code that utilizes Financial Modeling Prep and Yahoo Finance APIs to gather real-time market data for creating dynamic peer groups for companies. This tool is designed to assist in comparables analysis, providing a fairness opinion on company valuations, such as indicating that Apple may be worth 36% less than its current trading price. I also highlight the importance of selecting the right financial multiples and how our engine scores companies based on their similarities. I encourage you to consider how this model can enhance your analysis and decision-making processes. Thank you for watching!"}