FIMA Research Lab

Finance ∙ Intelligence ∙ Math ∙ Analytics
A global academic research initiative led by Prof. Huei-Wen Teng

🌟 Innovate with Us

FIMA Research Lab is an independent research initiative founded in October 2010 by Prof. Huei-Wen Teng, a full professor at National Yang Ming Chiao Tung University, Taiwan. We are passionate about driving data-driven innovations in FinTech, financial engineering, statistical modeling, and digital asset regulation. As a dynamic platform for international collaboration, academic publishing, and mentoring in AI-powered finance, we also bring abundant consulting expertise to real-world challenges.

We are eager to partner with industry leaders, startups, and organizations to co-create practical solutions that unlock the intrinsic value of data through interdisciplinary knowledge in math, statistics, and finance. If you’re ready to explore transformative opportunities together, we welcome your contact—let’s collaborate!

🧭 Ongoing research projects

Project Status: 🧩 In preparation 📄 Working paper 🚀 Submitted 🔧 Revision in progress

Trading

• 🔧 Why Feature Selection Still Matters in Bitcoin Return Forecasting (with Yen-Ting and YC )
  
• 🧩 Forecasting Realized Volatility with Multi-Agent Architectures (with Elvis)
  
• 🧩 A Hybrid Deep Learning–Association Rule Model for Forecasting S&P 500 Sector Returns (with Ben)
  

Portfolio management

• 🔧 Which risk do crypto index investments have? (with Meng-Jou and Matúš)
• 🔧 Cardinality-Constrained Portfolio Optimization for Cryptocurrency-Index-Tracking Exchange-Traded Funds Using a Black-Litterman Model with Machine Learning (with Chuen-Chun, Wei-Cheng, and Yen-Ming)
  

Explainable AI & FinTech

• 🔧 AI Act Compliance in Credit Scoring: Reconciling Interpretability and Accuracy via Quadratic Terms and powershap (with Hung-Ying, Heng-Shiu, Mark, YC)
• 📄 Integrating Explainable AI with Polynomial Analytics to Enhance Credit Scoring Model Compliance (with Paul, Yenchang, Ming-Hsuan)
  
• 🧩 Comprehensive Financial Statement Data and Feature Selection in U.S. Corporate Bankruptcy Prediction (with Mabel)
  
• 🧩 A SHAP-Weighted RFM Framework for Balancing Accuracy and Interpret-ability in Credit Card Fraud Detection (with Chrissy)
  
• 🧩 An Empirical Study of RFM and Domain-Driven Feature Engineering for Credit Card Fraud Detection (with Andy, Alvin, Chrissy)
  

Financial market risk management

• 🚀 Financial Risk Meters in Taiwan’s High-Cap Sectors (with Siang-Li and Wolfgang)
  
• 📄 Mapping Risk Contagion through Graphical Models and Early Warning Signals in Taiwan’s Stock Market (with Jason, Frank, Alex, Kuang-Yao)
  

Financial options pricing and hedging

• 🔧 Financial analytics of inverse BTC options in a stochastic volatility world (with Wolfgang)
  
• 🧩 Bitcoin Option Greeks in the Stochastic Volatility World (with Allen and Tom)
  
• 📄 Enhancing Financial Resilience in Taiwan’s Green Energy Sector: ESG-Focused Contract Redesign and Risk Mitigation (wit Howard and Elvis)
  

🚀 Join Our Lab

If you’re driven, curious, and eager to make your research count — we’d love to work with you. In our lab, you will be trained to write a high-quality master’s thesis with the potential to become a journal publication (preferably in SCIE or SSCI journals). You can find our lab members whose master’s theses or projects led to published papers — a great way to see what’s possible when you commit to excellence.

We emphasize research that is transparent, reproducible, and contributes meaningfully to the literature and society. And, we’re looking for students who demonstrate:

• Passion and curiosity for learning
• Strong execution and resilience
• Leadership and teamwork spirit
• Good presentation and communication skills
• Solid foundations in math, statistics, machine learning, coding, and finance

The research of our lab spans several key areas, with Lab Members actively collaborating on ongoing projects across these domains.

1. FinTech with machine learning and AI: Our lab explores machine learning and AI, emphasizing unsupervised learning, supervised learning, and large language models (LLMs), with applications in FinTech
2. Financial engineering with statistical models: We specialize in time series and graphical models, with applications in financial market risk management, and pricing and hedging financial derivatives.

Interested in joining? Let’s make an impact together!

• 📧 Email me your (1) CV, (2) transcripts, and (3) a short statement of purpose (150 words max).
• Enrolling in my Machine Learning & FinTech course is a great way to build relevant skills for our research — it’s highly recommended but not required.
• If you are from a different department, we encourage you to review the suggested course plan to help build a solid foundation in finance.