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.

Metaheuristics for optimization
- Particle Swarm Optimization
- 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 three 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 sennt no later than a pre-established deadline (the way of sending 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 using slides; in the second part one has to present, also using slides, 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 3 possible points, for a total from 0 to 12 points; 2) the oral examination is worth 0 to 18 points.
The sum of the points obtained from the homeworks and from the oral examination constitutes the final mark.

oral

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

Evaluation grid:
- 18–22: Incomplete and inaccurate completion of homework assignments; barely sufficient critical thinking skills applied to the understanding of applications, methods, models presented in the specialized literature; minimal or no ability in the development and implementation of applications, methods, and models; barely sufficient knowledge of the topics covered in class and in the assigned teaching materials.
- 23–26: Complete but not entirely accurate completion of homework assignments; fair critical thinking skills applied to the understanding of applications, methods, models presented in the specialized literature; modest ability in the development and implementation of applications, methods, and models; fair knowledge of the topics covered in class and in the assigned teaching materials.
- 27–30L: Complete and rigorous completion of homework assignments; excellent critical thinking skills applied to the understanding of applications, methods, models presented in the specialized literature; good ability in the development and implementation of applications, methods, and models; in-depth knowledge of the topics covered in class and in the assigned teaching materials.

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.

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

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