RMSC 3101

Special Topics in Risk Management (2019 Spring)

Class Information

Instructor

Teaching Assistant

Description

This is an advanced course aiming at introducing current issues and special topics in risk management. Students are required to present books and current articles in the related topics assigned by the instructor.

Textbooks

    • (Required) Chan, Ernie. (2013) Algorithmic trading: winning strategies and their rationale. Wiley. (Free online access via CUHK library)
    • (Reference) Prado, Marcos L ́opez de. (2018) Advances in Financial Machine Learning. Wiley.

Readings

Learning outcomes:

    1. Soft skills for job interviews
      • prepare for interviews (e.g., application letters, follow-up emails, CV, dress-code, etc); and
      • manage to answer non-technical questions during job interviews.
    2. Machine learning (ML), big data, and deep learning
      • familiarize with the concepts of ML, big data, and deep learning (excluding the implementation and theories); and
      • understand the scope of problems they can handle.
    3. Algorithmic Trading (algo trading)
      • familiarize with the basic algo trading techniques (excluding the implementation and theories); and
      • appreciate and criticize the use of algo trading methodologies.


Assessment

There are four assessing components in this course. They mimic the procedure of common job interview.

      1. Job Application (e1 out of 100): Each student writes an application letter for a pre-specified job, and send it to the lecturer and cc the TA at least 24 hours prior to their interview 1. Requirements:
          • write an email in a proper format;
          • include relevant information about applying the assigned job; and
          • attach an one-page resume in the email.
      2. Interview 1 (i1 out of 100): Each student attends a 3-min interview for a pre-specified job. Requirements:
          • answer non-technical and slightly technical questions (that are commonly asked in a first round interview); and
          • choose a proper dress-code for the interview.
      3. Interview 2 (i2 out of 100): Students are divided into 8 groups. Each group needs to read one chapter of the textbook or an article (about 30 pages), and deliver a 15-minute presentation to a client who knows very brief about risk management and algo trading. Requirements:
          • concisely and clearly present ONE to TWO new concept(s) about the assigned readings;
          • convince the client the importance of the presented concept(s); and
          • choose a proper dress-code for the interview.
      4. Follow-up email (e2 out of 100): Each student needs to write a follow-up email (about 200 words), and send it to the lecturer and cc the TA by the end of their interview 2. Requirements:
          • write an email in a proper format;
          • follow-up the interview 2, e.g., re-answer some questions asked by the interviews; and
          • convince the interviews why they should recruit you.

Grading

The final score (out of 100) is given by {10(e1 +e2)+15(i1 +i2)+50max(i1,i2)}/100, where e1, i1, i2, e2 are the scores of the assessing components specified above.

Attendance may be taken in one to two randomly selected class(es). If you are present in all selected class(es), 5 bonus points will be directly added to the final score. Besides, 5 bonus points (directly added to the final score) will be given to students who actively participate in this course.