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Teaching/FF6126

FF6126 · Postgraduate

Machine Learning in Finance

MSc in Finance, Nanyang Business School

Trimester 3Back to module tabs

Course materials and assessment information for registered students are provided through NTULearn. External resource links may require NTU access.

Overview

In order to appreciate the future changes on the finance industry that will be caused by machine learning, it is necessary to understand the key components of machine learning in finance. This is not a math-heavy module and we try to focus on the application of the machine learning methods

Learning Objectives and Outcomes

This course covers essential machine learning techniques in finance. The emphasis is placed on the financial applications and how can they transform the finance industry. This course will cover supervised learning and unsupervised learning. This course will also train the students’ soft skills through the group project on realistic data analysis problem.

Assessment Scheme

  • [5%] Class Participation
  • [20%] Assignments
  • [30%] Quiz or Take-home Individual Project
  • [45%] Group Project

Topics

  1. [4hrs] Introduction of Machine Learning and Types of Data and Errors: Data Pre-processing and Bias-Variance Trade-off
  2. [3hrs] Supervised Learning I: KNN and Resampling
  3. [3hrs] Supervised Learning II: Regression and Regularization
  4. [2hrs] Supervised Learning III: Logistic Regression
  5. [6hrs] Supervised Learning IV: Decision Trees and Ensemble Methods
  6. [2hrs] Supervised Learning V: Support Vector Machines
  7. [6hrs] Deep Learning: Feedforward Neural Networks
  8. [2hrs] Unsupervised Learning I: Clustering Analysis
  9. [1hr] Unsupervised Learning II: Dimension Reduction
  10. [3hrs] Unsupervised Learning III: Association Analysis

References

  1. [Textbook] J. C. Hull. (2021) Machine Learning in Business: An Introduction to the World of Data Science (3rd Ed.). Independently Published.
  2. [Textbook] G. James, D. Witten, T. Hastie, and R. Tibshirani. (2021) An Introduction to Statistical Learning - with Applications in R (2nd Ed.). Springer.
  3. T. Hastie, R. Tibshirani, and J. Friedman. (2009) The Elements of Statistical Learning - Data Mining, Inference, and Prediction (2nd Ed.). Springer.
  4. P.-N. Tan, M. Steinbach, and V. Kumar. (2005) Introduction to Data Mining (1st Ed.). Pearson.
  5. B. Ripley. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
  6. I. Goodfellow, Y. Bengio, and A. Courville. (2016) Deep Learning. MIT Press.
  7. M. L. de Prado. (2018) Advances in Financial Machine Learning. Wiley.
Remark: The second and third books (about statistical learning) can be downloaded for free from their websites. The fourth book provides some sample chapters downloadable at its website. The sixth book (about deep learning) is viewable online.