METHODS FOR THE MANAGEMENT OF PERSONAL PORTFOLIOS

The course is among the free choice ones of the master's degree program in Economia e Finanza. It aims to provide knowledge on decision-making criteria, quantitative tools, operational techniques and models for the analysis of investments in financial markets and for the building of personal risky portfolios. It also aims to present bio-inspired optimization methods, artificial intelligence and machine learning techniques for complex portfolio management.

1. Knowledge and understanding:
1.1. understand the quantitative tools and the mathematical methods necessary for the specification of a risky investment problem;
1.1. know the models for the selection and management of personal financial portfolios;
1.3. know the different types of risk measures and constraints on the characteristics of the portfolio.

2. Ability to apply knowledge and understanding:
2.1. formalizing a risky investment problem by specifying the risk measure and the system of constraints on the characteristics of the portfolio;
2.2. applying the quantitative tools and the mathematical methods necessary for selecting and managing personal financial portfolios;
2.3. implementing quantitative tools and mathematical methods through the use of software.

3. Judgement skill:
3.1. interpreting the results coming from the solution of a risky investment problem;
3.2. understanding virtues and drawbacks of the models for selecting and managing personal financial portfolios;
3.3. pondering on the measurement of risk on the basis of an analytical-financial method.

Keeping fresh in mind: several variable functions; matrix algebra; elements of optimization; elements of statistics.

he Modern Portfolio Theory (MPT)
- Diversification
- Classic portfolio selection models
- Limits of the MPT

Advanced portfolio selection models
- Risk-adjusted risk measures
- Coherent risk measures
- Non-standard constraints.

Bio-inspired optimization methods and artificial intelligence techniques
- Particle Swarm Optimization
- Artificial Neural Networks
- Machine Learning for MPT

Static portfolio revision
- The model of Smith
- The model of Stone and Hill

MATLAB ™ elements for portfolio selection and management
- Introduction to MATLAB™
- Free and constrained optimization with MATLAB™

- Luenberger D.G. (2013) Investment Science. Oxford University Press. [Chapters 6 (without the subsections "Solution of the Markowitz Problem" and "Solution Method".), 11 (without the subsections "Risk Aversion Coefficient" and "Certainty Equivalent")]
- Other teaching material will be indicated by the teacher during the course.

The exam consists in four homeworks and in an oral examination.
The homeworks: 1) must be carried out in groups; 2) are valid for the whole academic year and not beyond; 3) their carrying out must be sent by e-mail no later than a pre-established deadline (the e-mail address and the deadline will be indicated during the course).
Regarding the oral exam, it is divided into two parts: in the first part one has to critically present a research article; in the second part one has to present the results coming from the application to real data of a portfolio selection/management model.
Regarding the evaluation: 1) each homework is worth 0 to 5 possible points, for a total from 0 to 20 points; 2) the oral examination is worth 0 to 10 points.
The sum of the points obtained from the homeworks and from the oral examination constitutes the final mark.

The course is articulated into:
a) lectures in class;
b) applications of the studied tools by the use of software;
c) individual study.
Students are strongly encouraged to actively attend classes.

Italian

Site of the course on the e-learning platform Moodle.

oral

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development