QF103 Introduction to Portfolio Investing

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Course Catalog Description

Introduction

This course will cover the main topics of the analysis of time series to evaluate risk and return of the main products of capital markets (equity, fixed income, and derivatives). Students will work with historical databases, conduct their own analysis, and conduct tests based on the techniques reviewed during the class.
Campus Fall Spring Summer
On Campus X X
Web Campus

Instructors

Professor Email Office
Dragos Bozdog
dbozdog@stevens.edu Babbio 429A
Parisa Golbayani



More Information

Course Description

The significant amount of historical information available for most financial instruments requires a systematic and analytical approach to select an optimal portfolio. Time series analysis facilitates this process understanding, modeling and forecasting the behavior of financial assets. 
This course reviews the most important techniques used by investors, risk managers, and also by finance managers of non-financial service companies to analyze time series of their most relevant financial variables. Even though the methodologies reviewed during this course could also be applied to other domains such as marketing, the main emphasis of this class is on financial applications.

Course Outcomes

By the end of this course, the students will be able to:

  1. Understand the foundations of financial time series data, including high-frequency data
  2. Apply models and methods for analysis of financial time series (return and volatility)
  3. Recognize the value and also the limits of econometric methods in financial time series.






Grading

Grading Policies

Homeworks:The course will have a main project and three assignments of data analysis. The assignments must be submitted electronically through the course website. 
Each assignment has a value of 5 points. The solutions will be circulated after the due date so each student will have the opportunity to review his/her answers. 
Students may discuss lecture and textbook materials, and how to approach assignments; however each student must submit his/her own solution. Students cannot share ideas in any written form: code, pseudocode or solutions. Students cannot submit someone else's work found through internet or any other source, or a modification of that work, with or without that person's knowledge, regardless of the circumstances under which it was obtained, copied, or modified. Homeworks should be submitted through the class website before the deadline. 
For all the homeworks, students should send a report and an R program organized by questions. Please do not copy and paste large parts of the R program as part of the solutions. Create your own tables with the R output whenever it is possible or copy small sections of the R program and EXPLAIN the results. 
Late policy: 1 point lost for each day late. No assignments accepted after 3 days. 
If you dispute the grade received for an assignment, you must submit, in writing, your detailed and clearly stated argument for what you believe is incorrect and why. This must be submitted by the beginning of the next class after the assignment was returned. Requests for re-grade after the beginning of class will not be accepted. A written response will be provided by the next class indicating your final score. Be aware that requests of re-grade of a specific problem can result in a regrade of the entire assignment. This re-grade and written response is final; no additional re-grades or debate for that assignment. 
Project: The main objective of the project is to apply the theory of time series to analyze a financial problem using historical datasets. Students are expected to use most of the methodologies reviewed in class and present different forecasting scenarios with their financial recommendations. Each project must be developed by groups of three students and they should present a project proposal in the middle of the semester. 
Exams: The midterm and final exams will cover the first and second part of the class respectively. They are not cumulative. 
Software: R is the main software packages that will be used. 

4 Assignments are required
Examinations: BESS – BBG Certification
The assignments and their weights are as shown below:

  1. Assignments - 15%
  2. Team project - 25%
  3. Participation - 10%
  4. Midterm - 25%
  5. Final - 25%

TOTAL - 100%

Please note that assignments in this class may be submitted to www.turnitin.com, a web-based antiplagiarism system, for an evaluation of their originality.

 


Lecture Outline

Topic Reading
Week 1 Introduction to Financial Engineering Ch. 1 and 2
Week 2 Capital Markets Overview Ch. 3
Week 3 Corporate Finance & Valuation Ch. 3
Week 4 Equity Analysis Ch. 4
Week 5 Fixed Income Debt Securities Ch. 4
Week 6 Overview of Bonds Sectors & Instruments Ch. 4
Week 7 Valuation of Debt Securities Ch. 4
Week 8 Securitized Products
Week 9 Leveraged Loans & CLO's Ch. 5
Week 10 General Principles of Credit Analysis Ch. 5
Week 11 Foreign Exchange Ch. 6
Week 12 Poisson Processes and Jump Diffusion Ch. 11
Week 13 Exotic Options Ch. 7
Week 14 Review & Catch-up