I was asked by a researcher to write a script to extract the headings, sources, dates and word counts of a search of articles from the Factiva news platform into a table. She would use the table for further study. Since Factiva searches include a summary of these fields in a web browser, the content can… Continue reading Extracting meta data from Factiva to CSV via Python
The problem Researchers frequently need to retrieve data on thousands of companies from several sources and combine the results. Each business database that lists securities (companies, bonds, options, etc.) uses their own identifier code. For example, Datastream uses DS Code, Compustat uses GVKEY. They record the security name (and some kind of ticker symbol), but this is… Continue reading Comparing security identifiers for business databases
The problem Markit CDS is a source for credit default swap data for quoted companies, private companies and governments. Academic researchers with access to a resource like this usually need to cross reference the securities’ fundamentals or financial data. The financial data is offered from Markit for additional cost but is already available from a university’s other subscriptions.… Continue reading How I made Markit CDS play nice with other databases
A technical prototype I developed for the Business Data Service has been used as the driving force behind a new and exciting research project post, bringing together partners from outside The University of Manchester Library. What is the basic premise? To develop a collection of tools to bring together commercially available databases from separate suppliers…
Phil Reed Data 