Ateneo - UPO

The course examines the use of quantitative methods and Artificial Intelligence tools in finance and Fintech, with particular attention to investment decision support, risk management, the functioning of financial markets, and the financing of innovative firms.

The course first introduces the role of data, algorithms, and automation in financial services and develops the tools needed to represent risk and uncertainty through probabilistic models and Monte Carlo simulation. It then covers the main problems of portfolio optimization, risk preferences and utility theory, as well as dynamic asset allocation and rebalancing strategies.

A second part is devoted to quantitative applications in financial markets, including derivatives pricing and hedging, market microstructure, limit order book dynamics, algorithmic trading, optimal execution, and market making.

The course also addresses the design and evaluation of AI-driven financial systems, considering the relationship between prediction and decision, model validation, robustness, monitoring, interpretability, governance, and human oversight. The final part focuses on entrepreneurial finance and the main financing channels available to innovative firms, including venture capital, business angels, crowdfunding, valuation, ownership, dilution, and exit.

Quantitative applications and computational exercises are developed in Python.

The compulsory material for exam preparation is provided by the lecturer on the course DIR page. Any books, articles, or other sources suggested during the course are optional and intended only for further study.

The course aims to provide students with the conceptual and quantitative tools required to understand and analyze the role of data, models, and Artificial Intelligence in financial decision-making and in the main Fintech applications.

In particular, the course aims to develop the ability to represent financial problems characterized by risk and uncertainty, use quantitative models for resource allocation and financial valuation, understand the functioning of electronic markets and the main trading and execution algorithms, and critically assess the use of AI-based systems in finance.

A further objective is to provide an understanding of the main financing channels available to innovative firms and of the role that data, digital platforms, and Artificial Intelligence tools may play in screening, valuation, and financing processes.

The applied activities in Python are designed to develop the ability to translate theoretical models into simple numerical implementations, interpret their results, and assess their limitations and conditions of use.

Basic knowledge of mathematics, probability, and statistics is required, sufficient to understand random variables, probability distributions, expected value, variance, covariance, and correlation.
Introductory knowledge of Python programming is also required, particularly the use of variables, data structures, functions, and libraries for numerical computing and data visualization.
A preliminary understanding of the main concepts of finance, such as return, risk, portfolios, financial instruments, and the general functioning of financial markets, is useful but not essential.

The course combines lectures, quantitative examples, exercises, and computational activities in Python.

The information provided in the Syllabus concerning course content, teaching materials, teaching methods, and assessment applies both to attending and non-attending students.

DISABILITY AND SPECIFIC LEARNING DISORDERS
Students with disabilities, Specific Learning Disorders (SLD), or Special Educational Needs (SEN) may request dedicated services and specific support by contacting the University Staff for Student Careers and Services and consulting the dedicated University webpage.
After contacting the relevant University Staff, students with disabilities, SLD, or SEN may contact the course lecturer regarding the adaptation of examination arrangements and teaching-related aspects.

The exam consists of an oral examination aimed at assessing knowledge and understanding of the course topics, as well as the ability to apply the concepts and models studied to simple financial problems.
The examination may include both theoretical questions and applications, numerical examples, interpretation of results, or discussion of computational procedures covered during the course.
To pass the exam, students are required to demonstrate adequate knowledge of the fundamental concepts and the ability to apply them correctly in simple contexts. Higher grades will be awarded for deeper understanding, ability to connect different topics, autonomy in analysis, and clarity of exposition.

1. Fintech, Artificial Intelligence and financial decision-making
Digital transformation of financial services; role of data, algorithms, and automation; relationship between prediction, simulation, optimization, and decision-making; main AI applications in finance; introduction to Python.

2. Risk, uncertainty and Monte Carlo simulation
Random variables, distributions, returns, and scenarios. Introduction to stochastic processes and geometric Brownian motion. Monte Carlo simulation, risk measures, simulation error, validation, and sensitivity analysis.

3. Portfolio optimization
Portfolio return and risk, covariance, correlation, and diversification. Markowitz mean-variance model, minimum-variance portfolio, efficient frontier, and allocation constraints. Applications in Python.

4. Utility, risk preferences and investment decisions
Expected utility, certainty equivalent, risk premium, and measures of risk aversion. CARA and CRRA utility functions. Investment choices between risky and risk-free assets.

5. Dynamic asset allocation and rebalancing
Evolution of portfolio weights, buy-and-hold, target allocation, periodic rebalancing, and tolerance-band strategies. Turnover, transaction costs, and comparison of alternative strategies.

6. Derivatives pricing and hedging
Forward and option payoffs, no-arbitrage, replicating portfolios, and risk-neutral probabilities. Binomial model, Black-Scholes as a benchmark, delta hedging, and Monte Carlo pricing.

7. Market microstructure and algorithmic trading
Electronic markets, market and limit orders, limit order book, bid-ask spread, liquidity, slippage, and price impact. Implementation shortfall and main execution algorithms.

8. Optimal execution and market making
Trade-off among execution speed, market impact, and price risk. Order splitting, inventory risk, bid-ask quoting, reservation price, and the Avellaneda-Stoikov benchmark.

9. AI-driven financial systems
From predictive models to decision systems: data, decision rules, execution, validation, and monitoring. Robustness, calibration, drift, interpretability, model risk, governance, and human oversight.

10. Fintech and entrepreneurial finance
Funding gap, crowdfunding, business angels, and venture capital. Screening and selection, valuation, ownership, dilution, staged financing, and exit. Role of data and AI in evaluating innovative firms.

Quantitative applications and computational exercises are developed in Python.

Knowledge and understanding: by the end of the course, students will be able to understand the main concepts and quantitative models used in Fintech and finance, including risk and uncertainty, portfolio optimization, investment decisions, pricing and hedging, market microstructure, algorithmic trading, AI-driven financial systems, and entrepreneurial finance.

Applying knowledge and understanding: students will be able to apply the models and tools studied to simple financial problems, interpret numerical results, and use Python for simulations, quantitative analysis, and comparison of alternative strategies.

Making judgements: students will be able to critically assess the assumptions, results, and limitations of the models used and identify the most appropriate tool for the specific financial problem considered.

Communication skills: students will be able to describe and discuss clearly and correctly the main concepts, models, and quantitative results covered in the course, using appropriate technical language.

Learning skills: students will be able to independently explore topics in Fintech, quantitative finance, and Artificial Intelligence applications in finance using course materials, specialist texts, and other relevant sources.