Advance Your Research Through Practice

The PhD and Postdoc Workshop on October 27, 2025, held as part of the AI in Finance Conference, is designed for early-career researchers interested in empirical finance, machine learning, data-driven research, and programming in Matlab. This one-day workshop offers hands-on experience with WRDS data, replication of top-tier finance papers applying machine learning techniques, and interactive feedback from senior faculty.

Whether you are planning your dissertation, developing an empirical paper, or aiming to deepen your technical toolkit, this workshop delivers essential skills to succeed in modern research in financial economics.

Workshop Highlights

• WRDS Data Overview: Gain a comprehensive understanding of key datasets (CRSP, Compustat, IBES) and how they are used in academic finance research.
• Retrieving WRDS Data via SQL & Matlab: Learn how to access and manipulate financial data from WRDS using SQL queries embedded in Matlab scripts. Participants will retrieve real-world data and apply it in a replicable research workflow.
• AI & Machine Learning Essentials: Build conceptual understanding of how AI methods translate into empirical finance. Drawing on frameworks established in recent work such as Gu et al. (2020), we demonstrate how machine learning methods—like elastic net and random forest—can be effectively applied to forecast stock returns and evaluate economic relevance beyond traditional regression models.
• Paper Replication in Empirical Asset Pricing: Learn to replicate influential papers in empirical asset pricing—ranging from foundational tests (e.g., Black et al., 1972; Fama and MacBeth, 1973) to cutting-edge machine learning applications in predictability and asset pricing research (e.g., Gu et al., 2020; Avramov et al., 2023).

All codes, materials, and slides will be made available to participants afterwards.

Who Should Apply

• PhD students and early-career researchers in finance, economics, or data science
• Strong interest in empirical work and data
• Basic asset pricing knowledge such as the CAPM and the Fama and French (1993) model
• Basic knowledge in programming with Matlab
• No prior experience with or access to WRDS required. Access to WRDS is provided by Concordia University.

Application Procedure

To apply for the PhD and Postdoc Workshop at the AI in Finance 2025 Conference, please submit your CV including following information:

• A list of published papers and working papers (if available)
• A brief description of your knowledge in empirical asset pricing
• A brief summary of your programming skills

Instructors

Both instructors have extensive experience with statistical software tools, including Matlab/Octave/Freemat, R, Stata, Python, and Excel/VBA.  

Dr. Christian Fieberg (Hochschule Bremen and Concordia University) is Professor of Data Science at Hochschule Bremen (Bremen, Germany) and an Affiliate Professor at Concordia University (Montreal, Canada). His research centers on the analysis of large-scale datasets and the application of advanced methods from statistics, econometrics, optimization, operations research, simulation, and machine learning. 

Dr. Gerrit Liedtke (University of Bremen) is a Postdoctoral Researcher at the Chair of Finance at the University of Bremen. His research focuses on empirical asset pricing for equities, bonds, options, currencies, and cryptocurrencies using traditional econometrics and machine learning. 

References

• Avramov, D., Cheng, S., & Metzker, L. (2023). Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability. Management Science, 69(5), 2587–2619.
• Black, F., Jensen, M. C., & Scholes, M. (1972). The Capital Asset Pricing Model: Some Empirical Tests. In Studies in the Theory of Capital Markets, edited by M.C. Jensen, Praeger Publishers.
• Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
• Fama, E. F., & MacBeth, J. D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.
• Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, 33(5), 2223–2273.

Workshop Fee 

No Fees apply.