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Data Science

Tidy Finance: Foundations for Reproducible Research

Master key tools for reproducible empirical finance research with hands-on coding in R or Python.

17.5h (5 days)

€1,399

Face-to-face

English

Program date: June 29-July 3, 2026

Early bird deadline: April 15, 2026

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Data Science

Tidy Finance: Foundations for Reproducible Research

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Who is this Course for?

Tidy Finance: Foundations for Reproducible Research

Course overview

This summer school is designed for early-stage PhD students and recent graduates who want to build a solid foundation in transparent, reproducible, and scalable empirical research. Participants will learn modern workflows in Python that can be applied across disciplines, with a focus on applications in empirical asset pricing—the most influential area of financial economics.

Combining lectures with hands-on tutorials, the course guides students through the entire research pipeline: structuring projects for long-term reproducibility, managing and cleaning financial data, implementing econometric models, and evaluating the implications of preprocessing and methodological choices.

The program emphasizes principles and practices that help young researchers establish good habits early in their careers. We begin with core data, coding, and workflow skills before advancing to techniques such as methodological variation and machine learning methods, directly relevant to asset pricing and applied finance.

Teaching Faculty

Who is this course for?

The course is designed to accommodate participants with varying levels of experience.

Early-stage PhD students and recent graduates who want to build a solid foundation in transparent, reproducible, and scalable empirical research

Learning outcomes

Participants of this course will:

Learn essential tools and techniques for reproducible empirical research in finance
Develop clear communication skills for presenting research findings and collaborating on code
Learn efficient collaboration techniques, emphasizing best practices for version control and tidy coding
Gain hands-on experience through empirical finance applications, allowing participants to iteratively develop their own research projects
Cover foundational and advanced techniques that are transferable across programming languages like R or Python
Learn key concepts such as tidy data principles and reproducible communication to support robust research practices
Be able to apply their skills to real-world financial data and research challenges through practical sessions

Key topics for this Tidy Finance course

Here is an outline of the topics that will be discussed during the course.

Day 1: Foundations

Lectures:
- Reproducibility and replicability in empirical finance (e.g., Harvey et al., 2016; Jensen et al., 2023; Pérignon et al., 2024)
- Structuring research projects: Environments, version control, and data science workflows
Tutorial:
- Practicing setting-up projects, environments, and version control with Git and GitHub

Day 2: Tidy Data

Lectures:
- Data management in empirical finance: From raw to analysis-ready datasets (Scheuch et al., 2024)
- Cleaning, transforming, and storing both open-source and proprietary data using modern tools (e.g., Parquet, DuckDB)
Tutorial:
- Wrangling messy stock return and accounting data into reusable datasets

Day 3: Tidy Code

Lectures:
- Writing functions, modular code, and pipelines for scalable research (Scheuch et al., 2024)
- Principles of readable and maintainable code across projects
Tutorial:
- Practicing function, modular code and parallelization by estimating time-varying factor models

Day 4: Methodological Variation

Lectures:
- Common decision nodes in empirical asset pricing (e.g., Menkveld et al., 2024; Walter et al., 2024)
- Estimating results across multiple portfolio sort specifications simultaneously
Tutorial:
- Evaluating the value premium with methodological variation

Day 5: Machine Learning Workflow

Lectures
- Overview of recent machine learning in asset pricing (e.g., Giglio et al., 2022)
- Implementing the machine learning workflow (pre-process, build, fit, and tune)
Tutorial:
- Applying machine learning models to asset pricing problems

List of References

The course slides and code will be distributed with the course pack. The main references are:

Books / Articles

Harvey, C R., Liu, Y., & Zhu, H. (2016). …and the cross-section of expected returns, Review of Financial Studies 29, 5–68. https://doi.org/10.1093/rfs/hhv059.
Jensen, T. I., Kelly, B. T., & Pedersen, L. H. (2023). Is There a Replication Crisis in Finance? Journal of Finance, 78(5), 2465–2518. https://doi.org/10.1111/jofi.13249.
Giglio, S., Kelly, B., & Xiu, D. (2022). Factor models, machine learning, and asset pricing. Annual Review of Financial Economics, 14, 337–368. https://doi.org/10.1146/annurev-financial-101521-104735.
Menkveld, A. J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Neusüß, S., Razen, M., Weitzel, U., & Abad-Díaz, D. et al. (2024). Nonstandard errors. The Journal of Finance, 79(3), 2339–2390. https://doi.org/10.1111/jofi.13337.
Pérignon, C., Akmansoy, O., Hurlin, C., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Menkveld, A. J., Razen, M., & Weitzel, U. (2024). Computational Reproducibility in Finance: Evidence from 1,000 Tests. The Review of Financial Studies, 37(11), 3558–3593. http://doi.org/10.1093/rfs/hhae029.
Scheuch, C., Voigt, S., & Weiss, P. (2023). Tidy Finance with R. Chapman and Hall/CRC. https://doi.org/10.1201/b23237.
Scheuch, C., Voigt, S., Weiss, P., & Frey, C. (2024). Tidy Finance with Python. Chapman and Hall/CRC. https://doi.org/10.1201/9781032684307.
Walter, D., Weber, R., & Weiss, P. (2024). Methodological uncertainty in portfolio sorts .Working Paper. http://dx.doi.org/10.2139/ssrn.4164117.

Software / Hardware

Students will utilize coding programs Python or R throughout the course

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

It is the participant’s responsibility to ensure they meet the Admissions Criteria.

Program date: June 29-July 3, 2026

Early bird deadline: April 15, 2026

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Data Science for Tidy Finance: Foundations for Reproducible Research

Ability to write and run Python code, work with data structures, and use common libraries (such as pandas and numpy). Prior exposure to Git/GitHub is helpful but not required.
Undergraduate-level background in finance, economics, or a related field, with a basic understanding of asset pricing concepts (including risk–return tradeoffs, factor models, and portfolio sorts).
Familiarity with core statistical concepts, including probability distributions, regression analysis, and hypothesis testing.

Schedule

Here is your schedule for this edition of BSE Data Science for Finance Summer School, Data Science for Empirical Finance course.

Time

29

mon

30

tue

1

wed

2

thu

3

fri

09:00 - 11:00

Lecture

14:30 - 16:00

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Please consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Fees

Multiple course discounts are available, see more information about available discounts. Fees for courses in other Summer School programs may vary.

Course

Data Science for Empirical Finance

Large Language Models in Finance

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

1,399€

Reduced Fee*

799€

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

Need more information? Check out our most commonly asked questions.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Unfortunately, accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts oor request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

Yes, a social dinner is held once a week for all participants, and it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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