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Applied-Machine-Learning-in-Empirical-Finance

PhD Research: Applied Machine Learning in Empirical Finance - University of Twente & Quoniam Asset Management collaboration

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Language HTML
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Open Issues 6
License Other
Created 2025-12-16
Last Updated 2026-04-01
Last Push 2026-04-01
Contributors 2
Default Branch main
Visibility private

Datasets

This repository includes 45 dataset(s):

Dataset Format Size

| data | | 0.0 KB |

| publications.json | .json | 158.89 KB |

| research_questions.json | .json | 31.83 KB |

| resources.json | .json | 20.72 KB |

| team.json | .json | 3.57 KB |

| selected_variables.xlsx | .xlsx | 8.53 KB |

| corpus_summary.json | .json | 11.66 KB |

| provenance.json | .json | 37.91 KB |

| corpus_summary.json | .json | 9.43 KB |

| provenance.json | .json | 58.3 KB |

| seed_centroid.json | .json | 18.07 KB |

| corpus_summary.json | .json | 2.79 KB |

| provenance.json | .json | 38.03 KB |

| seed_centroid.json | .json | 18.1 KB |

| corpus_summary.json | .json | 3.21 KB |

| provenance.json | .json | 18.76 KB |

| seed_centroid.json | .json | 18.05 KB |

| 02b_prefilter_stats.json | .json | 0.48 KB |

| 05_corpus_with_bibtex_keys.json | .json | 698.75 KB |

| corpus_summary.json | .json | 0.3 KB |

| decisions.json | .json | 5.93 KB |

| provenance.json | .json | 132.19 KB |

| run_metadata.json | .json | 0.24 KB |

| seed_centroid.json | .json | 18.06 KB |

| run_metadata.json | .json | 0.25 KB |

| run_metadata.json | .json | 0.25 KB |

| run_metadata.json | .json | 0.26 KB |

| 01_query_stats.json | .json | 0.09 KB |

| 02b_prefilter_stats.json | .json | 0.46 KB |

| 05_corpus_with_bibtex_keys.json | .json | 902.41 KB |

| corpus_summary.json | .json | 7.26 KB |

| provenance.json | .json | 183.95 KB |

| run_metadata.json | .json | 0.32 KB |

| seed_centroid.json | .json | 18.07 KB |

| 01_query_stats.json | .json | 0.09 KB |

| 02b_prefilter_stats.json | .json | 0.62 KB |

| 05_corpus_with_bibtex_keys.json | .json | 1804.68 KB |

| corpus_summary.json | .json | 28.54 KB |

| provenance.json | .json | 218.93 KB |

| run_metadata.json | .json | 0.32 KB |

| seed_centroid.json | .json | 18.07 KB |

| theme_centroids.json | .json | 117.57 KB |

| analysis_results.json | .json | 2938.8 KB |

| comparison_summary.csv | .csv | 1518.78 KB |

| sample_papers.json | .json | 29.77 KB |

Reproducibility

This repository includes reproducibility tools:

  • Python requirements.txt

  • Conda environment.yml

Status

  • Issues: Enabled
  • Wiki: Enabled
  • Pages: Enabled

README

Applied Machine Learning in Empirical Finance

License: MIT GitHub Pages

A collaborative PhD research project between University of Twente and Quoniam Asset Management, advancing the application of machine learning methods in portfolio optimization and risk management.

Project Overview

  • Start Date: December 2025
  • Duration: 3-4 years
  • Funding: Industry-funded by Quoniam Asset Management
  • License: MIT

Research Focus

Primary Themes: - ML for Portfolio Optimization - Risk Management & Forecasting

ML Methods: - Deep Learning (neural networks, transformers, LSTMs) - Reinforcement Learning - Ensemble Methods (random forests, gradient boosting) - Probabilistic ML (Bayesian methods, uncertainty quantification) - Statistical Learning Models

Asset Classes: - Equities, Fixed Income, Multi-Asset, Derivatives

Team

Name Role Affiliation
Joerg Osterrieder Primary Supervisor & Industry Liaison University of Twente
Xiaohong Huang Co-Supervisor University of Twente
Axel Gross-Klussmann Industry Supervisor Quoniam Asset Management
Dennis Hoffmann PhD Student Quoniam / University of Twente

Repository Structure

Applied-Machine-Learning-in-Empirical-Finance/
├── .claude/                        # Shared Claude Code config
│   ├── CLAUDE.md                   # Project conventions (both users)
│   ├── commands/                   # Custom slash commands
│   └── hooks/                     # Git & CI hooks
├── shared/                         # Shared resources
│   ├── reference_checker/         # BibTeX verification against CrossRef/OpenAlex
│   ├── data_sources.md            # External data source documentation
│   ├── research_proposal.tex      # PhD research proposal
│   └── templates/                 # Reusable templates
│       ├── beamer/                # Beamer presentation template
│       └── project/               # Scaffold for new papers/projects
├── systematic_literature_review/   # Systematic literature review pipeline
│   ├── scripts/                   # Numbered pipeline steps (01–07)
│   ├── configs/                   # YAML search configurations
│   ├── runs/                      # Pipeline output per review
│   ├── src/                       # Pipeline source code
│   ├── README.md                  # Usage guide
│   └── DEVELOPMENT.md             # Developer docs
├── qam_projects/                   # Quoniam industry projects (confidential)
│   └── strategy_specific_models/  # ML models for investment strategies
├── docs/                           # GitHub Pages website
│   ├── index.html                 # Landing page
│   ├── team.html                  # Team members & bios
│   ├── publications.html          # Publication browser
│   ├── research.html              # Research overview & gaps
│   ├── resources.html             # Tools & resources
│   ├── news.html                  # News & updates
│   ├── css/, js/, data/, assets/  # Website resources
│   └── scripts/                   # Python data collection scripts
│       ├── fetch_team_info.py     # Fetch ORCID IDs from OpenAlex
│       ├── fetch_openalex.py      # Fetch publications
│       ├── analyze_research_gaps.py # Identify research gaps
│       ├── verify_publications.py # Verify publication authors
│       ├── check_links.py         # Validate website links
│       ├── download_team_photos.py # Download team member photos
│       └── fetch_logos.py         # Fetch partner logos
├── environment.yml                 # Conda environment spec
├── README.md
├── CONTRIBUTING.md
└── LICENSE

Getting Started

View the Website

Visit: https://digital-ai-finance.github.io/Applied-Machine-Learning-in-Empirical-Finance/

Update Data

To refresh publication and team data from OpenAlex:

# Set up environment
conda env create -f environment.yml
conda activate applied-ml-finance

# Or install script dependencies only
pip install -r docs/scripts/requirements.txt

# Fetch team information
python docs/scripts/fetch_team_info.py

# Fetch publications
python docs/scripts/fetch_openalex.py

# Analyze research gaps
python docs/scripts/analyze_research_gaps.py

Verify References

Check BibTeX entries against CrossRef/OpenAlex and TeX citation consistency:

# Full check: citation consistency + API verification
python -m shared.reference_checker paper_1/paper.tex

# Consistency only (no API calls)
python -m shared.reference_checker paper_1/paper.tex --consistency-only

# Bib-only: verify entries against APIs
python -m shared.reference_checker paper_1/references.bib

See shared/reference_checker/README.md for all options.

Project Management

We use GitHub's built-in tools for tracking progress and collaboration:

  • Issues — Task tracking with direct links to code, commits, and branches
  • Projects (Kanban Board) — Overview of PhD progress and milestones
  • Wiki — Comprehensive project documentation: setup guides, pipeline docs, conventions, and architecture

Research Questions

Key open questions driving this research:

Portfolio Optimization

  • How can deep reinforcement learning handle multi-asset portfolios with realistic constraints?
  • What is the optimal way to incorporate transaction costs into ML-based portfolio optimization?
  • Can transformer architectures capture cross-asset dependencies effectively?

Risk Management

  • How can we develop uncertainty-aware deep learning models for VaR/ES estimation?
  • What architectures work best for real-time risk monitoring with streaming data?
  • How should ML risk models be validated to meet regulatory requirements?

Methodology

  • What pre-training strategies work for financial time series foundation models?
  • How can causal inference be integrated with ML predictions for portfolio decisions?
  • What transfer learning approaches work across financial markets?

Partners

University of Twente

BMS Financial Engineering - Academic research partner providing theoretical foundations and research methodology.

Quoniam Asset Management

Frankfurt-based quantitative asset manager providing industry context, practical use cases, and data access.

Research Networks

  • COST Action Fintech and AI in Finance
  • MSCA Digital Finance

Publications

Team publications are automatically fetched from OpenAlex and displayed on the website. The data includes:

  • 160+ publications from team members
  • 80+ ML+Finance relevant papers
  • 1,000+ total citations

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

  • Academic: University of Twente, BMS Financial Engineering
  • Industry: Quoniam Asset Management, Frankfurt

Acknowledgments

  • OpenAlex for open academic publication data
  • Digital-AI-Finance organization for hosting
  • COST Action CA19130 Fintech and AI in Finance
  • MSCA Digital Finance network

(c) Joerg Osterrieder 2025-2026