Earnings revision momentum is one of the most studied and persistent signals in quantitative finance. When independent analysts across different banks and brokerages revise their earnings forecasts in the same direction, stock prices often follow — but not immediately. Revisions arrive over days and weeks, and markets can take time to fully price in the cumulative signal.
This notebook turns that concept into a practical, configurable weekly screen. Using I/B/E/S estimate data and market price/volume data from the LSEG ecosystem, it scores analyst revision activity, measures recent momentum, and identifies stocks where those two views diverge.
The workflow covers:
- Retrieving analyst estimate revision data across a broad equity universe
- Measuring short- and medium-term price momentum from historical data
- Standardizing and combining revision and momentum signals into a divergence score
- Ranking names where analyst sentiment and market pricing disagree most
- Adding context with sector breakdowns, volume confirmation, and a final weekly output table
The output is not a trade recommendation. It is a focused watchlist that helps narrow a large universe to the names most worth deeper investigation in Workspace.
Details and concepts are further explained in the Finding Stocks Where Analysts and the Market Disagree article published on the LSEG Developer Community portal.
The source code presented in this project has been written by LSEG only for the purpose of illustrating the concepts of creating example scenarios using the LSEG Data Library for Python.
Note: To ask questions and benefit from the learning material, I recommend you to register on the LSEG Developer Community
To execute the workbook, refer to the following:
License(s):
- An LSEG Workspace desktop license that has API access
- Tested with Python 3.12.14
- Packages: lseg-data pandas plotly
- LSEG Data Library for Python installation: 'pip install lseg-data'
The package includes a single Jupyter Notebook demonstrating features of the service. Depending where you plan to access the content from, you will need provide the proper credentials:
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Desktop Access
The notebook has been set up and tested to access data within the LSEG Workspace desktop application.
- Nick Zincone - Release 1.0. Initial version