Checking date: 31/05/2022

Course: 2023/2024

AI en Finance
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: PARRADO HERNANDEZ, EMILIO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
- Basic statistics - Programming, ideally in python - Basic machine learning, not mandatory
- To understand basic concepts about financial markets, financial products and the different agents and institutions that operate in the financial markets - Identify different scenarios of application of AI techniques in problems related with finance - Understand and work in typical use cases of the application of AI techniques in financial markets
Skills and learning outcomes
Description of contents: programme
1. Introduction to financial products, markets and institutions 1.1 Financial markets and institutions 1.2 Principal markets and indices 1.3 Financial services for retail costumers, corporate clients and investment banks 1.4 Fixed Income 1.5 Equity 1.6 Foreign Exchange 1.7 Derivatives 1.8 Regulation 2. Market structure 2.1. Macrostructure 2.1.1 Auctions 2.1.2 Order books, types of orders, OTC trading 2.1.3 Market making 2.1.4 Low latency trading and high frequency trading 2.2 Microstructure 2.2.1 Limited order books 2.2.2 Empirical analysis of financial data: returns, price correlations, volatility, intraday liquidity 2.2.3 Market Impact 3. Algorithmic trading 3.1 Introduction to trading 3.2 Data sources 3.3 Trading strategies 3.4 Backtesting 4. Roboadvisors 4.1 Introduction to roboadvisors 4.2 Roboadvisors platforms 4.3 Regulation 5. Sentiment analysis 5.1 Introduction to Natural Language Processing 5.2 Applications of sentiment analysis in finance 6. Analysis of Market Regimes with artificial intelligence 6.1 Introduction to machine learning for time series 6.2 Hidden Markov Models 6.3 Characterization of market regimes with Hidden Markov Models 7. Portfolio management 7.1 Introduction to investing portfolios 7.2 Portfolio configuration 7.3 Portfolio management with machine learning
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • Marcos López de Prado. Advances in Financial Machine Learning. John Wiley & Sons Inc. 2018
  • Robert Kissell. The Science of Algorithmic Trading and Portfolio Management: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques. Academic Press. 2013

The course syllabus may change due academic events or other reasons.