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ISCTE-IUL  >  Education  >  ISCTE-IUL

Project Evaluation Methods (1 º Sem 2016/2017)

Code: 02533
Acronym: 02533
Level: 1st Cycle
Basic: No
Teaching Language(s): Portuguese, English
Friendly languages:
Be English-friendly or any other language-friendly means that UC is taught in a language but can either of the following conditions:
1. There are support materials in English / other language;
2. There are exercises, tests and exams in English / other language;
3. There is a possibility to present written or oral work in English / other language.
1 6.0 0.0 h/sem 36.0 h/sem 0.0 h/sem 0.0 h/sem 0.0 h/sem 0.0 h/sem 1.0 h/sem 37.0 h/sem 113.0 h/sem 0.0 h/sem 150.0 h/sem
Since year 2016/2017
Pre-requisites Basic mathematical and statistical concepts.
Objectives This course intends to promote the learning of quantitative methods applied in project evaluation when there is a high level of uncertainty over future rewards. Other objective is to present, for each method, several case studies of assessment.
Program 1. Introduction
1.1. Types of investment projects;
1.2. Quantitative methods applied in project evaluation.
2. Monte Carlo Simulation (MCS)
2.1. Description of methodology;
2.2. Implementation of a MCS model;
2.2.1. Random number generation;
2.2.2. Random observations generation;
2.2.3. Model validation;
2.3. Variance-reducing techniques;
2.3.1. Antithetic Variable Technique;
2.3.2. Example;
2.4. Case studies using specific software.
3. Dynamic Programming (DP)
3.1. Introduction;
3.2. Characteristics of DP problems;
3.3. Deterministic DP;
3.4. Probabilistic DP;
3.5. Implementation of a DP model
3.6. Case studies using specific software.
4. Binomial Tree Approaches
4.1. Introduction;
4.2. Usual procedure in binomial tree;
4.3. Binomial alternative schemes;
4.4. Case studies using specific software.
Evaluation Method 1. Evaluation throughout the Term (ET): two group courseworks (Weights(W): 25% and 25%); one test (W: 50%); minimum attendance: 80% of classes taught.
2. Final Exam: for students who have not opted for the ET or students who have given up of ET; if grade(G) is between 7.5 and 9.5, the student can perform an oral exam(OE).
Approval in the Course: Final grade(FG) greater than or equal to 10 (over 20). In case of G higher than 16.5, OE, if considered necessary; otherwise, the FG will be 16.
Teaching Method Each student should acquire analytical, information gathering, written and oral communication skills, according to the established learning outcomes for this unit.
Learning methodologies will be used:
1. Expositional, to the presentation of the theoretical reference frames.
2. Participative, with analysis and resolution of application exercises.
3. Active, with the realization of group works.
4. Experimental laboratory, with development and operation of computer models.
5. Self-study.
Basic Bibliographic 1. Hillier, Frederick S. e Lieberman, Gerald J.; Introduction to Operations Research, 10th ed., McGraw-Hill, 2015.

2. Winston, Wayne L., Operations Research: Applications and Algorithms 3rd ed., Duxbury Press, 1994.

3. Huynh, H. T., Lai, V. S. and Soumare, I.; Stochastic  Simulation and Applications in Finance with Matlab Programs, Wiley, 2009.

4. Damodaran, Aswath; Investment Valuation: Tools and Techniques for determining the value of any asset, Wiley Finance, 2012.

5. Elementos de apoio fornecidos pelo docente da Unidade Curricular. / Lectures notes provided by the lecturer of Course.
Complementar Bibliographic 1. Dixit, A. K. and Pindyck, R. S., Investment under Uncertainty, Princeton University, New Jersey, 1994.

2. Hull, J. C., Options, Futures and other Derivatives Securities, fourth edition, Prentice-Hall International, Inc, 2000.

3. Trigeorgis, L., Real Options: Managerial Flexibility and Strategy in Resource Allocation, MIT Press, London, 2000.

4. Dayananda, D., Irons, R., Harrison, S., Herbohn, J. and Rowland, P.; Capital Budgeting: Financial Appraisal of Investment Projects, Cambridge University Press, 2002.

5.  Bellman, R. E., Dynamic Programming, Courier Dover Publications, 2003.