NLP Fintech Forum: Text Analytics and Financial Insights Exchange

The following links contain blogs written by students of Matthias Buehlmaier's courses on text analytics and natural language processing (NLP) in finance and fintech. The first course was taught in the academic year of 2017-2018 at HKU, and starting from 2021-2022 the blog posts have been centralized in this forum. The blog post contents do not necessarily reflect the opinions of the course instructor or HKU. The contents of all blog posts are for academic purposes only.

• MFIN7036 student blog from academic year 2025-2026
• FINA2350 student blog from academic year 2025-2026
• MFIN7036 student blog from academic year 2024-2025
• FINA4350 student blog from academic year 2024-2025
• MFIN7036 student blog from academic year 2023-2024
• FINA4350 student blog from academic year 2023-2024
• MFIN7036 student blog from academic year 2022-2023
• FINA4350 student blog from academic year 2022-2023
• MFIN7036 student blog from academic year 2021-2022
• FINA4350 student blog from academic year 2021-2022

Disclaimer: The views and opinions expressed in this blog are those of the individual student authors and do not necessarily reflect the official position of the University of Hong Kong, HKU Business School, or the course instructor. All content is produced for academic and educational purposes only. This blog documents student learning experiences in text analytics and NLP applied to finance. In particular:

• Not investment advice. References to trading strategies, portfolio performance, or backtesting results are purely illustrative academic exercises. They should not be construed as financial, investment, or trading advice. Past performance discussed in these posts is not indicative of future results. Readers should conduct their own research and consult qualified professionals before making any financial decisions.
• Data collection. Students worked with publicly available data sources, official APIs, and licensed datasets. Where posts describe challenges encountered during data collection (e.g., access restrictions or rate limiting), these are documented as learning experiences, not as instructions for circumventing website protections or terms of service.
• Third-party tools and services. References to specific software, APIs, platforms, or LLM providers are for documentation purposes only and do not constitute endorsement by HKU or the course instructor.
• Public figures, companies, and markets. Where posts analyze public statements by political figures, company earnings calls, or financial instruments, this is done strictly from a quantitative and market-impact perspective using established academic methods. Such analysis does not constitute political commentary, advocacy, endorsement, criticism, or recommendation of any entity, position, or security.
• Geopolitical references. Some projects involve detecting geopolitical events as market-moving factors. Any references to countries, conflicts, or political events are made solely in the context of financial data analysis.

While reasonable efforts have been made to ensure accuracy, the analyses presented may contain errors or limitations inherent to coursework.

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