INVESTMENT APPRAISAL

Course Description

This course is devoted to the techniques of cost-benefit analysis of investment projects. The course covers both the analytical techniques as well as their practical applications in decision-making. The course topics will cover financial modeling of investment projects, alternative investment criteria, the role of discounting the time value of money, pre-feasibility and feasibility studies in project appraisal, sensitivity analysis and maintaining consistency between real and nominal prices, inflation rates, exchange rates and interest rates. It will also cover how to determine the optimal scale and timing of investments and how to construct income statements and balance sheets from cash flow projections.

INVESTMENT APPRAISAL

Evaluation Tools (Active Term)

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Reference Books & Course Materials

1. 01 Glenn P. Jenkins, Chun – Yan Kuo, and Arnold C. Harberger, Cost-Benefit Analysis for Investment Decisions, First ed. Kindle Direct Publishing 2019.
2. 02 Belli, P., et.al, Economic Analysis of Investment Operations: Analytical Tools and Practical Applications, WBI Development Studies, World Bank Institute, World Bank, 2001

Learning Outcomes

1. L01 To construct a pre-feasibility study SOLO 4
2. L02 To analyze investment projects to determine whether they should be implemented using modern investment decision-making criteria SOLO 4
3. L03 To apply methods of financial modeling for investment projects SOLO 3
4. L04 To integrate financial, economical, and stakeholder analysis SOLO 4
5. L05 To compare alternative investment criteria SOLO 4
6. L06 To assess implementation of public and private invesment projects SOLO 5

Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

Po-Lo Matrix