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, satellite, social, news & media)
Natural Language Processing
Systematic Trading
Large Language Models
Fine – tuning of AI Models
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
Real time coding sessions
Teamwork
Interactive trading session
To successfully complete the course, students must attend at least 60% of classes.
Assessment Method
1 case study for the first qualitative AI part, not mandatory, evaluated up to 3 points.
1 case study for the second quantitative AI part, not mandatory, evaluated up to 3 points.
Final project regarding the creation of a systematic trading strategy integrating qualitative and quantitative AI approaches, evaluated up to 15 points.
Oral exam evaluated up to 15 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
Collection and Analysis of Alternative Data
Identifying and sourcing relevant alternative data (e.g.,satellite and geospatial data, ESG data)
Initial preprocessing and data cleaning techniques
Week 2
From Natural Language Processing to Large Language Models
Architecture of Transformer models
Self – attention mechanism and fine tuning
Week 3
Training Large Language Models
Fine – tuning of LLM
Iterative testing and refinement of LLM
Week 4
Text-based Data Processing with LLMs
Techniques for extracting financial insights from alternative data (e.g.,satellite and geospatial data, ESG data)
Analyzing news articles and reports for market impact using LLMs
Week 5
Retrieval Augmented Generation (RAG)
Optimization of an LLM with RAG
RAG to fine – tune analysis of alternative data
Week 6
Enhancing Trading Signals with Generative Models
Generating synthetic financial time series data for model training
Techniques for augmenting trading signals using generated data
Week 7
Creation of a concrete trading strategy
Learning and application real techniques deployed in one of the most important UK based Hedge Fund
Insights from the Head of Capital Markets at this Hedge Fund
Week 8
Real Fuzzy Logic
Introduction to Fuzzy Logic and Membership functions
Week 9
Fuzzy Neural Networks
Metaheuristic optimization models
Adaptive Fuzzy Neural Networks
Week 10
Complex Fuzzy Logic
Complex Numbers
Introduction to Complex Fuzzy Logic
Week 11
Complex Fuzzy Neural Networks
Complex Neuro Fuzzy Logic
ANCFIS & Hybrid Learning
Week 12
Systematic Trading strategy
Use Fuzzy Logic and Neural Networks to predict financial time series