printer friendly pageCME 240: Statistical and Machine Learning Approaches to Problems in Investment Management (MS&E 445)
This course will approach a variety of problems in investment management, using statistical and machine learning tools to model forecasting problems in the evolution of security prices. Through a combination of lectures and projects, we will investigate pricing and risk models ranging from individual securities up through asset classes. Occasional guest lecturers will present problems they currently face in their day to day work. Prerequisites: Basic background in Probability (e.g.:
CME 106) and Mathematical Finance (e.g.:
MATH 238), and some facility programming in R and/or Python.
Last offered: Spring 2019
MATH 238: Mathematical Finance (STATS 250)
Stochastic models of financial markets. Forward and futures contracts. European options and equivalent martingale measures. Hedging strategies and management of risk. Term structure models and interest rate derivatives. Optimal stopping and American options. Corequisites:
MATH 236 and 227 or equivalent.nnNOTE: Undergraduates require instructor permission to enroll. Undergraduates interested in taking the course should contact the instructor for permission, providing information about relevant background such as performance in prior coursework, reading, etc.
Terms: Win
| Units: 3
Instructors:
Papanicolaou, G. (PI)
;
Guijarro Ordonez, J. (TA)
MS&E 445: Statistical and Machine Learning Approaches to Problems in Investment Management (CME 240)
This course will approach a variety of problems in investment management, using statistical and machine learning tools to model forecasting problems in the evolution of security prices. Through a combination of lectures and projects, we will investigate pricing and risk models ranging from individual securities up through asset classes. Occasional guest lecturers will present problems they currently face in their day to day work. Prerequisites: Basic background in Probability (e.g.:
CME 106) and Mathematical Finance (e.g.:
MATH 238), and some facility programming in R and/or Python.
Last offered: Spring 2019
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