Introduction to Quantitative Investment, 201310
This course introduces students to quantitative investment. A “quant” portfolio manager or a trader usually starts with an intuition or a vague trading idea. Using mathematics, s/he turns the intuition into a mathematical trading model for analysis, back testing and refinement. When the quantitative investment model proves to be likely profitable after passing rigorous statistical tests, the portfolio manager implements the model on a computer system for automatic execution. In short, quantitative investment is the process where ideas are turned into mathematical models and then coded into computer programs for systematic trading. It is a science where mathematics and computer science meet. In this course, students study investment strategies from the popular academic literature and learn the fundamental mathematics and IT aspects of this emerging field. After satisfactorily completing this course, the students will have an overview of the necessary quantitative, computing, and programming skills in quantitative investment.
There are a total of 8 lectures, each running for 3 hours.
Dr. Haksun Li is the CEO of NM FinTech LTD., a quantitative trading research and analytic consulting company, which serves brokerage houses and funds all over the world, multinational corporations, very high net worth individuals and gambling groups. Prior to this, Dr. Li was a quantitative trader/quantitative analyst with multiple investment banks. He has worked in New York, London, Tokyo, Singapore and Hong Kong. Dr. Li has a B.S. and M.S. in Pure and Financial Mathematics from the University of Chicago, an M.S. and a Ph.D. in Computer Science & Engineering from the University of Michigan, Ann Arbor. Dr. Haksun Li is/was an adjunct professor with multiple universities. He taught at the National University of Singapore (Mathematics), Nanyang Technological University (Business School), FuDan University (Economics), as well as Hong Kong University of Science and Technology (Mathematics).
- Some experience in trading is preferred but not essential
- University level mathematics and statistics
- Programming experience