The practice of investment management has been transformed in recent years by computational methods. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness.
이 강좌에 대하여
최근 조회 52,829회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 11시간 필요
영어
자막: 영어
배울 내용
Analyze style and factor exposures of portfolios
Implement robust estimates for the covariance matrix
Implement Black-Litterman portfolio construction analysis
Implement a variety of robust portfolio construction models
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 11시간 필요
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 3시간 필요
Style & Factors
완료하는 데 3시간 필요
9개 동영상 (총 114분), 3 개의 읽기 자료, 1 개의 테스트
9개의 동영상
Introduction to factor investing12m
Factor models and the CAPM9m
Multi-Factor models and Fama-French7m
Factor benchmarks and Style analysis8m
Shortcomings of cap-weighted indices11m
From cap-weighted benchmarks to smart-weighted benchmarks12m
Introduction to Lab sessions6m
Module 1 Lab Session - Foundations42m
3개의 읽기 자료
Requirements2m
Material at your disposal5m
Module 1- Key points2m
1개 연습문제
Module 1- Graded Quiz1시간
완료하는 데 2시간 필요
Robust estimates for the covariance matrix
완료하는 데 2시간 필요
7개 동영상 (총 70분), 1 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Estimating the Covariance Matrix with a Factor Model9m
Honey I Shrunk the Covariance Matrix!7m
Portfolio Construction with Time-Varying Risk Parameters8m
Exponentially weighted average8m
ARCH and GARCH Models9m
Module 2 Lab Session - Covariance Estimation13m
1개의 읽기 자료
Module 2-Key points2m
1개 연습문제
Module 2 - Graded quiz1시간
완료하는 데 3시간 필요
Robust estimates for expected returns
완료하는 데 3시간 필요
7개 동영상 (총 77분), 2 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Agnostic Priors on Expected Return Estimates6m
Using Factor Models to Estimate Expected Returns11m
Extracting Implied Expected Returns8m
Introducing Active Views6m
Black-Litterman Analysis10m
Module 3 Lab Session- Black Litterman23m
2개의 읽기 자료
Module 3-Key points2m
The Intuition Behind Black-Litterman Model Portfolios10m
1개 연습문제
Module 3 - Graded Quiz1시간
완료하는 데 3시간 필요
Portfolio Optimization in Practice
완료하는 데 3시간 필요
7개 동영상 (총 67분), 4 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Scientific Diversification11m
Measuring risk contributions6m
Simplified risk parity portfolios7m
Risk Parity Portfolios7m
Comparing Diversification Options8m
Module 4 Lab Session - Risk Contribution and Risk Parity15m
4개의 읽기 자료
Module 4-Key points2m
Survey: Alternative Equity Beta Investing10m
Dive into heuristic diversification10m
To be continued (2)10m
1개 연습문제
Module 4 - Graded quiz1시간
검토
ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON의 최상위 리뷰
MM 제공2020년 4월 13일
Loved how this course was presented. It built well off of the first course and provided labs that let me explore the content. I really enjoyed how Lionel and Vijay presented the material.
KA 제공2020년 1월 4일
I like the way instructors explained difficult topic and digest it to simple way. The coding side was also impressive. WIth novice background in Python, I would able to understand.
DB 제공2020년 5월 4일
This course teaches a great approach to portfolio construction. Crisp course content makes learning even more convenient. My sincere regards to the team behind this noble task,
MG 제공2020년 4월 11일
Really appreciated from both of the instructors, from thier very high level of theory and practical programming skills. Hoping to use these khowledge in pracsis some days.
Investment Management with Python and Machine Learning 특화 과정 정보
The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.
자주 묻는 질문
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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