About me

I am Reeyarn Li, currently a post-doc researcher at Paderborn University, Germany. Between July and December 2025, I was also a visiting scholar at the University of North Carolina at Chapel Hill.

• CV

Published papers (and dataset)

Journal articles

• Huang, A., K.W. Hui, & R.Z. Li (2019). "Federal Judge Ideology: A New Measure of Ex Ante Litigation Risk" Journal of Accounting Research 57(2): 431-489. Available at: https://onlinelibrary.wiley.com/doi/full/10.1111/1475-679X.12260

  • See blog post
  • Data set: to be updated...(ideally there should be a github repo posting the python code that updates the data...; or please just email me or Allen)

• Franke, B., A. Huang, R.Z. Li, & H. Wang (2023). "Securities Law Precedents, Legal Liability, and Financial Reporting Quality" Review of Finance, 28(2), 413-445. Available at: https://academic.oup.com/rof/article-abstract/28/2/413/7285632

• Degen, D., J. Kengelbach, R.Z. Li, F. Pietrogrande, & S. Sievers "Beer, Cars & Fundamentals: Predicting German M&A activity." Corporate Finance, Forthcoming.

• Hofmann, P., R.Z. Li, & S. Sievers (2025). "Vierjährige Erfahrungen mit der ESEF-Berichterstattung in Deutschland: Herausforderungen und Chancen" (Four-Year Experience with ESEF Reporting in Germany: Chal-lenges and Opportunities), KOR. Available at: https://wiwi.uni-paderborn.de/fileadmin-wiwi/dep2ls9/Dateien_Sievers/KOR_2025_07-08_251-258.pdf

  • Data set at github (or please contact me)

Working papers

• Kim, J., Li, R., & Petacchi, R. (2025). "Measuring the Informativeness of Audit Reports: A Machine Learning Approach"
• Landsman, W., Li, R., & Sievers, S. (2025). "Investor Inattention to Earnings Metadata." (Job Market Paper)

Working-in-progress

• Li, R. (2025). "The Economic Role of Non-GAAP Earnings." Available at SSRN with Abstract ID: 5926002

• Kaiser, S., Li, R., & Sievers, S. (2025). "Centralized Electronic Reporting and Disclosure Informativeness: Evidence from a Top-Down Regulatory Change in Germany" EAA 2025 Annual Congress.

  • slides

• Franke, B., Kaiser, S., Li, R., Schlackl, F. & Sievers, S. (2025). "Changes in Firms’ Business Models and Accrual Estimation" EAA 2025 Annual Congress.

• As is seen on my github, I'm working on something with XBRL and deep learning.

Open-source

EasyRL (github.com/reeyarn/EasyRL)

A lightweight, memory-efficient, and (hopefully) fast Python library for extracting specific information from XBRL filings and taxonomies — without loading the entire DTS (Discovery Tree). Forget strict XBRL validation and complex object models. Extract only what you need into simple Python structures (e.g., dict, list, pandas.DataFrame)

Open-ESEF (github.com/reeyarn/openesef)

• Open-ESEF is a Python-based open-source project for handling XBRL filings that follow ESEF, the European Single Electronic Format.

• ESEF is a standard for electronic financial reporting developed by the European Securities and Markets Authority (ESMA).

• This project contains open-source code from two repos:

  • XBRL-Model project.
  • SEC EDGAR Financial Reports project.

• Project is under development.

  • Development completeness: 75%.
  • See Example: https://reeyarn.li/blog/apple_2020.html