Course Details for A.Y. 2013/2014

The information on this page refer to the academic year 2013/2014. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2013/2014

Name:

Modelli e Algoritmi per la Finanza Aziendale mod I / Modelli e Algoritmi per la Finanza Aziendale mod I

Type:

Module

Main course:

Modelli E Algoritmi Per La Finanza Aziendale / Models and Algorithms for Financial Management

Basic information

Code:

Sector:

SECS-P09

Credits:

: Laurea Magistrale in Matematica 6 CFU (c)

Term:

1° semestre

Degree(s):

Laurea Magistrale in Matematica 2° anno curriculum Generale Opzionale

Language:

Italian

Teacher(s):

Giuseppe Alesii

Same as:

Models and Algorithms for Financial Management I

Course Objectives

Introduction to finance problems from a micro and macro economics perspective and from a financial accounting point of view. Applications of computer science and quantitative abilities to financial modelling

Course Content

Introduction to finance from three points of view: (a) macro-finance approach: flow of funds matrix; (b) micro-finance approach: Irving Fisher's Separation Theorem; (c) financial accounting approach: financing deficit with different sources
Fixed income securities valuation under the assumption of certainty: interest rates risk sensitivity: (a) Macaulay duration (b) term structure of interest rates, spot, forward and short rates; (c) Fisher Weil duration.
Shares valutation under the assumption of certainty, dividend discount model: (a) Myron Gordon's dividend growth model; (b) Modigliani Miller 1961, growth model; (c) fundamental indexes and dividend discount models, cross sectionals and longitudinal evidences;
Capital Budgeting, choosing real investments in an industrial firmunder the assumption of certainty: (a) criteria: i. Payback Period, ii. Internal Rate of Return, iii. Net Present Value, iv. Profitability Index, v. Economic Value Added (as an extension of Modigliani Miller 1961); (b) methods: i. capital rationing, uni e multiperiod cases, linear programming applied to multiperiod cases, geometric approach and Excel Solver Tool application; ii. optimal harvesting, Faustmann problem individual and repeated cycles problems solutions. (c) comparative statics and dynamic optimization: Richard Bellman's Dynamic Programming in a deterministic frameworkl: i. continuous and discrete control variables ii. application to the choice and optimal dynamic management of investment project for renewable and exhaustible resources.
Risky assets (non derivatives) valuation : (a) Markowitz’s portfolio selection, analytic solutions to the following portfolio selection problems: • efficient portfolios; • Minimum variance opportunity set; • global minimum variance portfolio; • tangency portfolio; • orthogonal portfolio; (b) Single index model, Market model; (c) Sharpe Lintner Mossin CAPM; • analytic derivation; • capital budgeting application: risk adjusted discount rate, Certainty equivalent approach; • financial leverage and its influence on hurdle rates, Hamada 1972; (d) multifactor models; • Ross APT; • Three Factor Model di Fama French; (e) which asset pricing is most suited for capital budgeting decisions: Jeremy Stein 1996.

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

Student must have a thorough and deep knowledge of some modelling approaches in finance. Moreover, she/he must be knowledgeable with the general themes of finance.
The student is enabled to use her/his programming skills to models and algorithms in finance not only with respect to those dealt with in the course but also to other problems.
Informed judgements and choices student's skills in the field of finance are built up thanks to a wide variety of models dealt with in the course and to an hands on approach which privileges practice over theory.
Students are under constant pressure to provide feedback to instructor. This is true both with respect to class participation and written exams and homeworks.
Since teaching is based mostly on methods and less on models by themselves, students are enabled to extend their knowledge of models and algorithms of finance well beyond those dealt with in the course.

Prerequisites and Learning Activities

See required courses. A good programming ability is required for the following applications: A) any spreadsheet, e.g. Excel, Calc; B) any matrix oriented language, e.g. MatLab, Gauss, Ox, Octave, Scilab. In the computer lab classes, Gauss will be used. Univariate and multivariate calculus is applied in most of the models. A solid background in probability theory is required.

Assessment Methods and Criteria

Two written exams during the 14 weeks course, mid term and final. The same exercises will be given during ordinary exams sessions during the year. Written test account for 90% of final valuation. A short oral exam is due to get the final grade by those students who got at least an average pass grade for the written tests.

Textbooks

Thomas E. Copeland, J. Fred Weston, and Kuldeep Shastri, Financial Theory and Corporate Policy , Addison-Wesley 2005. (4th Edition).
Luenberger, D, Investment Science , Oxford University Press. 1998.
Edwin J. Elton, Martin J. Gruber, Stephen J. Brown, William N. Goetzmann, Modern Portfolio Theory and Investment Analysis , Wiley. 2006.

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 21 marzo 2014, 14:21