The course aims at providing the knowledge of machine learning (ML) tools for modeling financial data defined in high-dimensional spaces and characterised by non-linear relationships. In particular,
the objective of the considered methods is the automatic detection of patterns in the data by taking into account the specific peculiarities of financial data. The estimated models can then be
used by the analysts and investors to inform decisions on investment strategies under uncertain and risky conditions.
At the end of the course the student will gain the ability to:
a) understand and explain the main machine learning and deep learning techniques;
b) choose and apply the appropriate modeling tool, in the class of ML methods, for the analysis of financial data;
c) use the open-source software R for performing data analysis and visualization and implementing ML models;
d) assess the performance of the implemented predictive methods and interpret all the available results in a decision making perspective.
None
The course consists of theory lectures and R lab sessions.
The exam consists in:
- a test including open-ended and T/F questions concerning theoretical topics or short applications of the studied methods;
- exercises to be solved using the R software in order to evaluate the ability of the student in analysing data and interpreting outputs.
The two parts of the exam (theoretical and practical) are eachworth 50% of the total score, approximately.
- Introduction to AI and machine learning: supervised, unsupervised and reinforcement learning, deep-learning, regression and classification problems, the bias-variance trade-off.
- Illustration of financial applications with machine learning methods (e.g. price prediction, portfolio construction, risk analysis, credit ratings, outlier detection, algorithmic trading).
- Training, validation and testing, cross-validation back-testing, hyper-parameter tuning
- Ensemble methods: classification and regression trees, bagging, random forest, boosting.
- Neural networks: feedforward convolutional and recurrent neural network.
- Elements of reinforcement learning.
