Efficient access to qualitative data: a case of MD&A analysis from 10-K with Python via SEC’s API

ABSTRACT

A distinction between quantitative and qualitative data has been regarded as an inviolable separation for a long time, resulting in a significant restriction in setting research designs and methods. In this study, we propose a way to pull this stereotype down and to open more choices for upcoming studies: using Python with 10-K filings via the U.S. Securities and Exchange Commission’s application programming interface, we show how to broaden the source of data available for research. In particular, we focus on management’s discussion and analysis (MD&A) of 10-K filing. This part has not been fully incorporated due to considerable requirements for an access – substantial time and effort in case of hand collecting. The new perspective approach described in this paper provides significant implications for business practice as well as research in relation to the higher level of utilization of existing data than before.

KEYWORDS:

• Quantitative data
• qualitative data
• data mining
• Python

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ A full guide of the SEC’s API is available at https://www.sec.gov/edgar/sec-api-documentation.

² An excel file used for this simulation is named ‘10-K_Company CIK.csv’, a comma separated value format file.

³ Search keywords used in this application are “covid”, “covid-19”, “corona”, “virus”, and “pandemic”.

⁴ Selenium library is a web application testing Python library for automated framework.

⁵ HTML is an acronym of “Hypertext Markup Language” and widely used for web page construction.