Instructional goals
•Learn how to use alternative data like satellite and geospatial data, web data and news/media with LLM and Generative AI to improve investment decisions
•Explore Machine Learning and Decision-Making methods with a focus on investment applications.
•Implement a live trading strategy following the most valuable Hedge Fund techniques
•Build a systematic trading strategy with both a qualitative and a quantitative AI approach
Prerequisites
The students should know basis of Python language, even if it is not essential. No previous knowledge of finance is required.
Intended learning outcomes
Qualitative AI:
Understand the principles of Generative AI and LLM, with a focus on investments
Explore advanced data synthesis techniques
Analyze alternative data with Generative AI Models
Apply Generative AI in classification and prediction of financial investments
Quantitative AI:
Integrate Fuzzy Logic and Neural Network to predict financial time series
Analyze and predict real financial data with AI
Develop a Quantitative AI systematic trading strategy
Course Contents
Use of alternative data (web, geospatial, consumption, flights, news & media)
Natural Language Processing
Systematic Trading
Large Language Models in Finance
Quantum Annealing
Real & Complex Fuzzy Logic
Adaptive Fuzzy Neural Networks
Prediction of financial phenomena
Reference Books
The course materials will be based on collection of top scientific articles and book reports. Articles and other materials (cases) will be available through the Luiss Learn platform.
Teaching Methods
Interactive lectures
Real time coding sessions
Teamwork
Project work
Assessment Method
Mid-term exam, evaluated up to 11 points
Final project regarding the creation of a systematic trading strategy integrating qualitative and quantitative AI approaches, evaluated up to 12 points.
Final exam evaluated up to 10 points.
Each vote greater than 30 will be 30L.
Thesis assignment criteria
The thesis will be assigned (upon specific request to the instructor) to students who demonstrate a serious and motivated interest to the course subjects.
Week 1
Introduction to the course. Systematic trading.
Practical Lab Session with Python.
Week 2
From Natural Language Processing to Large Language Models, financial use cases.
Practical Lab Session with Python.
Week 3
Collection processing and Analysis of Alternative Data for financial usage.
Practical Lab Session with Python
Week 4
Bloomberg terminal overview.
Practical Lab Session with the terminal
Week 5
Fuzzy Logic.
Practical Lab Session with Python
Week 6
Neural Networks, Pre-trained Transformers in Finance.
Practical Lab Session with Python
Week 7
Learning and application real techniques deployed in one of the most important UK based Hedge Fund.
Overview of the Hedge Fund industry.
Insights from the Head of Capital Markets at this Hedge Fund
Week 8
Quantum Annealing Introduction to spin networks.
Classical/quantum Ising models.
Week 9
Applications.Mapping and embedding of an optimization problem onto a spin network.
Practical example and application on financial services.
Week 10
Integration of Qualitative and Quantitative AI, ensemble models.
Practical Lab Session with Python
Week 11
Genetic Algorithms.
Practical Lab Session
Week 12
Recap and Q&A
Practical Lab Session with Python