Module Code - Title:

FI6024 - MACHINE LEARNING FOR FINANCE

Year Last Offered:

2025/6

Hours Per Week:

Lecture

2

Lab

0

Tutorial

1

Other

0

Private

7

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

This module is a core module for the MSc in Machine Learning For Finance. The aim of this course is to provide candidates with a practitioner-oriented education in machine learning algorithms that are used in the capital markets and asset management sectors of the international financial services sector.

Syllabus:

Machine Learning algorithms and methodologies increasingly use machine learning models that can not only analyze large volumes of data but also continue to improve themselves. Recent studies show that hedge funds utilizing ML outperformed those managed by more traditional quants and generalized hedge funds. This module will cover a number of supervised and unsupervised techniques such as Naive Bayes, PCA, Hidden Markov Models, KNN's and Support Vector Machines (SVMs). All algorithms will be applied to financial data utilizing with practical applied examples.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

At the end of this module students will be able to: 1. Create a software programme to calculate the price of an exotic stock option using Monte Carlo pricing methods 2. Illustrate how Bayes Theory and Naïve Bayes techniques can be used to identify pairs trading opportunities. 3. Describe How Decision Trees may be used in asset allocation for optimal portfolio selection. 4. Create a software programme to implement a Random Forest algorithm to access portfolio risk 5. Illustrate the use of Support Vector Machines in determining both intra- and inter-day index movements.

Affective (Attitudes and Values)

At the end of this module students will be able to: 1. Demonstrate an appreciation of specialised modelling techniques. 2. Acknowledge the model assumptions and limitations.

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

The module will be delivered using online lectures, virtual labs and tutorials.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Dr. Param Jeet, Prashant Vats (2017) Learning Quantitative Finance with R , Packt Publishing Ltd

Michael Paluszek, Stephanie Thomas (2016) MATLAB Machine Learning , Apress

Other Relevant Texts:

Citi (2017) Searching for Alpha: Big Data. Navigating New Alternative Datasets ,

Kolanovic, M. and Krishnamachari, R.T. (2017) Big Data and AI Strategies Machine Learning and Alternative Data Approach to Investing , JP Morgan

Programme(s) in which this Module is Offered:

Semester(s) Module is Offered:

Autumn

Module Leader:

Eamon.Leonard@ul.ie