Optimization models play an increasingly important role in financial decisions. This course introduces the student to financial optimization models and methods. We will specifically discuss linear, integer, quadratic, and general nonlinear programming. If time permits, we will also cover dynamic and stochastic programming. The main theoretical features of these optimization methods will be studied as well as a variety of algorithms used in practice. Prerequisite(s): Multivariate calculus and linear algebra. Course Note(s): Due to overlap in subject matter in EN.625.615 and EN.625.616, students may not receive credit towards the MS or post-master’s certificate for both EN.625.615 and EN.625.616.
The course materials are divided into modules which can be accessed by clicking Course Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, starting on WEDNESDAYS. Exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.
To develop and use optimization models in the analysis of problems in finance.
Recommended
Cornuejols, Gerard, Javier Peña, and Reha Tütüncü. (2018). Optimization Methods in Finance (Second Edition). New York, NY: Cambridge University Press.
ISBN-13: 978-1107056749
Note: The first edition of this text (ISBN-13: 978-052186170) is satisfactory from a reading perspective. The second edition has more problems, a few additional topics, and some reorganization. I will make an effort to provide information for both editions.
Textbook information for this course is available online through the appropriate bookstore website: For online courses, search the MBS website.
You will need access to a recent version of MATLAB. A license is provided at no cost to you, through JHU.
Visit the JHU IT Services Portal. Log in with your JHED ID and type “Matlab” in the search bar. Click on “Matlab for Students” in the search results and follow the instructions provided.
It is expected that each module will take approximately 8–11 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the text and outside readings as applicable (approximately 2–3 hours per week), listening to the audio annotated slide presentations (approximately 2–3 hours per week), and writing assignments (approximately 4–5 hours per week).
This course will consist of the following basic student requirements:
All modules, except Modules 7, 13, and 14, will have instructor provided discussion questions. You are responsible for providing an answer to ONE question each week. You should not answer a question already answered by another student unless your approach is significantly different from what has already been provided. In addition, you are required to respond to your classmates' questions about your solution and provide any additional resources that you believe will be helpful. You are encouraged to collaborate (within the JHU guidelines on academic integrity) on these assignments but you should write up your own final solution.
All modules, except Modules 7 and 14, will contain a graded problem set. These assignments will be based on the theory, algorithms, and/or applications discussed in the associated module. Each assignment will have approximately 5 problems of varying difficulty. You are always encouraged to collaborate (within the JHU guidelines on academic integrity) on assignments. This year, you are allowed to work in a group up to size 3 on homework assignments. If you choose to work in a group, elect one of the group members to be responsible for submitting the assignment for the entire group.
REMINDER: Direct copying of someone else’s work or computer code is considered to be cheating and will result in a grade of zero on the assignment and a possible F in the course.
Usually you will be required to complete a problem “by hand.” This means that you should not use any software or electronic device (save a simple calculator) to do the work. (However there is no reason you cannot check your work with commercially available software, shareware, freeware, etc.) Some assignments will have a computing (Matlab) component that you will also complete individually.
Graded assignments will be returned weekly, providing frequent feedback. Problem sets will be graded out of 100 points. The allocation of those points to specific problems may vary from assignment to assignment depending on the degree of difficulty.
All assignments are due according to the dates in the Calendar. No late problem sets will be accepted. The two lowest homework grades will be dropped (provided that such grades are not the results of violations of academic integrity).
You are allowed 1 week to request a regrade on a homework assignment.
There will be two exams given, one in Module 7 and the other in Module 14. Each of these exams will be timed and open book, with NO COLLABORATION ALLOWED. The midterm exam (Module 7) will focus on the course material taught in Modules 1-6. The final exam (Module 14) will focus on the course material taught in Modules 8-13. While not cumulative, the final exam typically assumes knowledge of material covered in the first six modules.
Both of these exams will provide you the opportunity to demonstrate your ability to determine the required techniques and apply them to solve an optimization problem outside of the framework of a specific lecture/individual topic. Each of these exams will be worth 30% of your final grade.
All examinations are due according to the dates in the Calendar. Late submissions will not be accepted. You are allowed 1 week to request a regrade on an exam.
Partial credit may be given. For each individual homework and/or exam item that is graded the following (approximate) rubric will be used:
We generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.
A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.
A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.
EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10). 100-98 = A+
97-94 = A
93-90 = A−
89-87 = B+
86-83 = B
82-80 = B−
79-77 = C+
76-73 = C
72-70 = C−
69-67 = D+
66-63 = D
Final grades will be determined by the following weighting:
Item | % of Grade |
Discussions | 10% |
Assignments | 30% |
Exams (Midterm + Final) | 60% (30% + 30%) |
There are some specific ethics guidelines for this course:
Deadlines for Adding, Dropping and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.
Academic Misconduct Policy
All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.
This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.
Students with Disabilities - Accommodations and Accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.
For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.
Student Conduct Code
The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.
For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/
Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
Course Auditing
When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.