FA692 Natural Language Processing for Financial Applications
Course Catalog Description
This course focuses on natural language processing (NLP) models and their applications to finance. Building on
fundamental machine learning theory and practice, the course covers advanced topics in natural language processing
for analyzing financial reports and news. Learning and building from financial data sets, the lectures will
introduce machine learning models in quantitative investing, portfolio management, algorithmic trading, risk
management, client-relationship management, and beyond. A final project on related topics is required.
Prerequisite: Students must have taken FA590 or comparable introduction to machine learning methods
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This is an advanced course in the FINTECH and Machine Learning concentration of the Financial Analytics program.
- Create and apply NLP models for analyzing financial reports and news.
- Evaluate performance of trained NLP models for sentiment analysis.
- Understand applicability of diverse NLP techniques.
- Dixon, M. F., Halperin, I., Bilokon, P. (2020). Machine Learning in Finance: From Theory to Practice. Germany: Springer International Publishing.
- Bird, S. (2006, July). NLTK: the natural language toolkit. In Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions (pp. 69-72).
- Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020). Comprehensive review of text-mining applications in finance. Financial Innovation, 6(1), 1-25.
- Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
|1||Review of Machine Learning||Dixon et al.: Chapter 1||Homework 1: Review of Python (assigned)|
|2||Introduction to NLP with Applications to Financial Data||Bird(2006)||Homework 1 (due)
Homework 2: Tokenization and preprocessing (assigned)
|3||Text Mining of Financial Reports and News||Gupta et al. (2020)||Homework 2 (due)
Homework 3: Application of text mining (assigned)
Project proposal due
|4||Sentiment Analysis of Financial Reports and News||Araci (2019)||Homework 3 (due)
Homework 4: Homework 3 (due)
Homework 4: Application of neural networks for time series (assigned)
|5||Topic Modeling in Finance||Blei et al. (2003)||Homework 5 (due)
Homework 5: Application of topic modeling(assigned)
|6||Applications to Finance/ Special topics||Dependent on topic area chosen||Homework 5 (due)|