This Jupyter Notebook accompanies the article titled "Working with Fundamental and Estimates Data - A DCF Example" on LSEG Developer Portal. This article will explore how you can use company fundamentals and estimates data to conduct a discounted cashflow (DCF) type intrinsic valuation for a company and its peers to provide a relative valuation overlay. We also use some unsupervised ML routines to generate classification groupings for our data.
Pre-requisites:
LSEG Workspace with access to LSEG Data Library for Python
Required Python Packages: lseg-data, pandas, numpy, sci-kit Learn, numpy-financial