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

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  • Joo Hyung Lee
  • Seung Jae Lee

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.

Suggested Citation

  • Joo Hyung Lee & Seung Jae Lee, 2023. "Efficient access to qualitative data: a case of MD&A analysis from 10-K with Python via SEC’s API," Applied Economics Letters, Taylor & Francis Journals, vol. 30(21), pages 3021-3025, December.
    • Handle: RePEc:taf:apeclt:v:30:y:2023:i:21:p:3021-3025
    DOI: 10.1080/13504851.2022.2118219
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