Course Details
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Basic information
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Master Degree in Computer Science: 6 Ects (b)
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Degree(s):
Elective Master Degree in Computer Science curriculum ASSC
Elective Master Degree in Computer Science curriculum GSEEM
Elective Master Degree in Computer Science curriculum General
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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) macrofinance approach: flow of funds matrix; (b) microfinance 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

have a thorough and deep knowledge of capital budgeting / securities pricing models under certainty and portfolio selection and basic asset pricing models. Moreover, she/he must be knowledgeable with the general themes of finance.
 be able to use her/his programming skills in simple Excel spreadsheets and/or in high programming languages such as Gauss or MatLab, not only for financial models and algorithms dealt with at lesson but also for other similar problems.
 have acquired general skills in the field of algorithms and applied programming for financial modelling which enable him/her to make educated choices in a problem solving practice framework. The student should be able to retrieve financial data, compute main descriptive statistics, estimate parameters of main asset pricing models (Information procurement and analysis). The student should be able to apply integer and non integer linear programming and numerical non linear optimization algorithms in both capital budgeting and asset pricing (algorithm choice).
 be capable to give a presentation both in front of a general practitioners' audience and a more academic one about the models dealt within the course.
 have acquired a method of study both thanks to a wide knowledge of the main streams in which financial modelling is evolving, theoretical continued learning, and a confident practice with respect to the main high level programming languages, GAUSS and MatLab, which are continually evolving, best practice continued learning.
Prerequisites and Learning Activities
PreAssessment Formal prerequisites are stated in the first module webpage, namely Numerical Analysis, Calculus, Stochastic Calculus, Mathematical Stastistics. Actual prerequisites are not assessed at the beginning of the course and they are considered as a given when tuning the teaching approach of finance topics. 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
Formative Assessment The formative assessment of this course teaching and learning process is performed through class participation during lessons: students may be asked to answer questions about topics dealt with at lesson; students may ask instructor questions during lessons both about the very topic dealt with at lesson and about correlated topics they are particularly interested in. summary of previous week lessons: a student is randomly selected to sum up topics dealt with in the previous sessions, actually introducing extant session; short seminars: students are required to apply their skills in Calculus, Stochastic Calculus, Numerical Analysis and Mathematical Stastistics to specific problems in finance, proposing their own solutions previously prepared as homeworks. Summative Assessment The summative assessment of this course is performed through Written tests: administration: during the 60 hours semester module a mid term after roughly 30 hours and a final test at the end of the semester are given for students attending lessons; a comprehensive test is given in ordinary exam sessions for students not attending lessons and for attending students that do not pass mid term and final semester tests; aims and formative purposes students are evaluated with respect to three different dimensions of learning: Assessment of Testing method adopted Baseline theoretical knowledge provided through lessons and suggested reading list Open questions to be answered through short essays Problem solving involving simbolic calculus and stochastic calculus capabilities Questions about model building and algorithms tuning for specific formal problems Programming capabilities Small problems in class assignments to be programmed in a high level language, e.g. MatLab, Gauss, Ox, Scilab Evaluation criteria final numerical results achievement; style: in modelling – possibly new – solutions in a simbolic layout; in writing codes for extant models; in prose for short essays questions; Homeworks and take home projects: some compulsory homeworks are given on specific topics to let students delve into the subject at her/his own pace; some optional take home projects are suggested to students particularly interested in applying quantitative methods of their choice to finance problems. Oral exams: after achieving at least an average pass grade in written tests during the semester or, as an alternative, an equivalent valuation on a comprehensive written test in an ordinary exam session, students are required to take an oral exam made up of: questions about mistakes in written tests; one's choice topic question; Assessment breakdown Formative and Summative Assessment towards the definition of a final grade % weight on the final grade In class participation 5 Summary of previous week lessons 10 Short seminars (if given, else the weight is given to class participation) 5 In Class written tests 70 Home assignments (homeworks and take home projects) 5 Oral Exam 5
Textbooks
 Thomas E. Copeland, J. Fred Weston, and Kuldeep Shastri, Financial Theory and Corporate Policy. AddisonWesley 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:Course information last updated on: 15 aprile 2015, 15:33