Computational Finance

Course Description

The financial industry is a major employer of computer science graduates, and this industry is undergoing tremendous changes vis-à-vis automation and AI-driven processes. In this course, we will study the basic tools and terminology of finance, and focus on computational and mathematical modeling techniques used in financial data analysis. In particular, we will study derivatives, pricing models, and financial time series.

Prerequisites

Programming

This course will involve a significant amount of programming. Students are expected to be comfortable with basic Python programming and with the principles of object-oriented programming. Specific use of relevant libraries such as NumPy, SciPy, scikit-learn, or PyTorch will be introduced as part of the course.

Mathematics

Familiarity with basic probability and statistics is required. Computational finance relies heavily on these subjects, and the more advanced topics in probability and statistics will be covered in this course as and when needed. Similarly, basic knowledge of calculus and linear algebra is also required. More advanced concepts (as and when needed) will be covered in class or in additional reference material.

Data Structures & Algorithms

Students are expected to have a solid understanding of basic data structures (e.g., linked lists, arrays, hash-maps) and algorithms (e.g., sorting, recursion, dynamic programming, and basic analysis of time- and space-complexity).

Reference Material

The material covered in this course will mostly be from the following books (you do not necessarily need the latest edition):

• Hull, John C. (10th ed.) (2017). Options, Futures, and Other Derivatives. Prentice Hall.

• Tsay, Ruey S. (3rd ed.) (2011). Analysis of Financial Time Series. John Wiley & Sons.

• Wilmott, P. (2nd ed.) (2007). Paul Wilmott Introduces Quantitative Finance. John Wiley & Sons.

We may also consult other material at times, and may also read a few research papers!

Communication Policy

Please follow the communication decorum below as strictly as possible for effective and efficient communication with the instructor and other members of the teaching staff:

• Do not use any email address for the instructor other than the one provided by the computer science department faculty page (the same email is also given in the PDF version of this syllabus).

• Do not use any email address to communicate with a TA other than the one provided on this course web site.

• Clearly state the course and the main concern in your email subject. For example, "CSE 390: homework 3 doubt".

• Class announcements will always be made on Brightspace. Please follow them carefully.

• We will use Piazza as our discussion platform (details will be provided to registered students via an announcement on Brightspace).

• For in-person discussions with the instructor, his office hours are the best venue. No appointments are needed to visit the instructor's office hours!

Attendance

Students are expected to attend every class, report for examinations, and submit graded coursework as scheduled. If a student is unable to report for any exam or complete major graded coursework as scheduled due to extenuating circumstances, the student must contact the instructor as soon as possible.  In such an extraordinary case, the student may be requested to provide documentation to support their absence and/or may be referred to the Student Support Team for assistance. In the instance of a missed lecture, the student is responsible for reviewing the lecture and staying up to date with the material covered in class. There are no make-up options for a missed exam (subject to the university policies on Student Participation in University-Sponsored Activities and Final Exams).

Grading Schema & Policy

• The Pass/No Credit (P/NC) option is not available for this course.

This course will have one midterm exam, one final exam, and four homework assignments. Two of these assignments will be theoretical and involve mathematical and conceptual problem-solving, while two others will be programming assignments. The grading schema is as follows:

1. Two theory assignments: 30% (15% each)

2. Two programming assignments: 30% (15% each)

3. Midterm exam: 20%

4. Final Exam: 20%

Note: In case the class size is small, we may reduce the weight of the exams, and introduce a seminar-style component where we can discuss recent advances in computational finance in the form of presenting research papers.

• In case of a grading error, the student must bring it to the instructor's notice within a 1-week period of the grade/score being posted on Brightspace.

• The midterm exam papers will be retained by the instructor for 2 weeks from the day of announcing the exam scores on Brightspace. Any inspection and reporting of a grading error will only be done during these 2 weeks for students who visit during the instructor's office hours.

  • Students visiting after this 2-week period can still retrieve their midterm exams, but no grade-related discussions shall take place any longer.

• For the final exam,  there will be a special office hour designated to resolve any queries or disputes related to the final exam score/grade. The exact venue for this office hour will be announced after the final exam. The final exams are not returned.

Final Grade

The final grade you receive in this class will reflect, as far as possible, the extent to which you have mastered the concepts and their applications. How much someone needs a grade, or how close they are to the next higher grade, will have no effect on the grade. As the instructor, I want everyone to do well in this course, and will make every reasonable effort to help you understand the material taught. However, the grades provided at the end of the semester are final, except for rare situations involving grade-entry errors. They will not be altered for any reason, so please do not ask me to do so. Any unethical request for grade change or a "bump" will be ignored, and may be reported to higher authorities.

Numeric scores (out of 100) will be reported on Brightspace. Letter grades will only be available directly on SOLAR, after being finalized. Letter grades are not for discussion and/or dispute, except in cases of obvious error in data-entry. In particular, the details such as the ones listed below will not be disclosed:

• How exactly is the grading done on a curve. E.g., "I have 78. Is that enough for B+?"

• Where the exact cutoffs are. E.g., "what is/was the cutoff for B+?"

• How far/near is the student to the next cutoff. E.g., "I got 83.5 but I got A-. How close was I to getting an A?"

• Questions implied through comparative and/or accusatory remarks. E.g., "My best friend got 76.2 and I got 76, but it's unfair that they got B+ while I got B."

Academic Integrity

You may discuss the homework assignments and recitation problems in this course with anyone you like. Each submission (including written material and coding), however, must be the student's own work, and only their own work. Any evidence of written homework submissions or source code being copied, shared, or transmitted in any way between students (this includes using source code downloaded from the Internet or written by others in previous semesters) will be regarded as evidence of academic dishonesty. Additionally, any evidence of sharing of information or using unauthorized information during an examination will also be regarded as evidence of academic dishonesty.

Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty are required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Technology & Management, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty, please refer to the academic judiciary website at :

• http://www.stonybrook.edu/commcms/academic_integrity/index.html

Student Accessibility Support Statement

If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact Student Accessibility Support Center, 128 ECC Building, (631) 632-6748, or at sasc@stonybrook.edu. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential. Students who require assistance during emergency evacuation are encouraged to discuss their needs with their professors and the Student Accessibility Support Center. For procedures and information go to the following website:

• https://ehs.stonybrook.edu/programs/fire-safety/emergency-evacuation/evacuation-guide-people-physical-disabilities

and search "Fire Safety and Evacuation and Disabilities".

Critical Incident Management

Access to the Piazza forum is not a mandatory component of this course. As such, disruptive behavior that compromises the safety of the learning environment, inhibits other students' ability to learn, or interrupts the ability of the teaching staff to constructively participate, will lead to the immediate removal of those responsible for such disruption.

Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of Judicial Affairs any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures.