Martin Waibel

Martin Waibel

Ph.D. candidate in Finance

Stockholm School of Economics

Welcome to my website!

I am a Ph.D. student at the Finance Department of the Stockholm School of Economics. My research focuses on how regulation impacts financial intermediation with an emphasis on implications for financial stability and microstructure implications.

I am on the academic job market 2023-2024 and will be available for virtual interviews in conjunction with the EJME and the AFA/ASSA Meeting.

NEWS:

  • 01/24: I presented my job market paper at the AFA PhD Student Poster Session 2024

  • 09/23: I received the “Best Doctoral Student Paper Award” at the 2023 German Finance Association (DGF) Annual Meeting for my job market paper.

Interests
  • Financial Intermediation
  • Empirical Asset Pricing
  • Broker-Dealers
  • Nonbank Financial Institutions
  • Machine Learning
Education

  • PhD in Finance, 2019 - current

    Stockholm School of Economics

  • MSc in Economics, 2019

    University of Bonn

  • BSc in Economics, 2016

    University of Mannheim

Working Papers

Managing Regulatory Pressure: Bank Regulation and its Impact on Corporate Bond Intermediation
(Job Market Paper)
with Andreas C. Rapp
Paper Internet Appendix
Managing Regulatory Pressure: Bank Regulation and its Impact on Corporate Bond Intermediation
Intermediary Balance Sheet Constraints, Bond Mutual Funds’ Strategies, and Bond Returns
with Mariassunta Giannetti, Chotibhak (Pab) Jotikasthira, and Andreas C. Rapp
Paper
Intermediary Balance Sheet Constraints, Bond Mutual Funds' Strategies, and Bond Returns
Exposure to Local Housing Markets and Bank Performance
with Valentin Schubert
Paper
Exposure to Local Housing Markets and Bank Performance

Publications

Window Dressing of Regulatory Metrics: Evidence from Repo Markets
(forthcoming Journal of Financial Intermediation)
with Claudio Bassi, Markus Behn, and Michael Grill
Paper
Window Dressing of Regulatory Metrics: Evidence from Repo Markets

Work in Progress

Machine Learning Approach to Option Returns
with Adrien d’Avernas, Tobias Sichert, Christian Schlag, and Chunjie Wang

Teaching

Graduate Courses

Undergraduate Courses

  • Financial Markets and Institutions - University of Bonn                                  2017, 2018

  • Econometrics - University of Bonn                                                                                  2018

    Code

    Machine Learning Toolbox in Asset Pricing
    ML_toolbox is an open-source framework written in Python for the flexible replication of the results documented in Gu et al. (2020), Empirical Asset Pricing via Machine Learning. The toolbox provides the user with a flexible and fully automated process for i) sourcing the required raw data, ii) cleaning and processing the data, iii) creating the 94 features used in Gu et al. (2020), and iv) replicate key results of different machine learning models. Note: The Github repository is currently overhauled and hence in private mode. If you are interested in getting access please do reach out via Email.
    with Adrien d'Avernas and Chunjie Wang
    Github Repository
    Cleaning Academic TRACE in Python (PyCleanTrace)
    PyCleanTrace is the Python implementation of the cleaning steps in Dick-Nielsen and Poulsen (2019), “How to Clean Academic TRACE Data”. The code provides a fully integrated solution to read in the Academic Trace data, implement the Dick-Nielsen & Poulsen cleaning steps and generate specific key variables. The main goal is to provide an open-source alternative to the SAS code provided in Dick-Nielsen and Poulsen (2019).
    Github Repository

    Contact