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.
课程信息
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中级
完成时间大约为11 小时
英语（English）
字幕：英语（English）
您将学到的内容有
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
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为11 小时
英语（English）
字幕：英语（English）
提供方
北方高等商学院
Founded in 1906, EDHEC is now one of Europe’s top 15 business schools .
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Style & Factors
完成时间为 3 小时
9 个视频 （总计 114 分钟）, 3 个阅读材料, 1 个测验
9 个视频
Introduction to factor investing12分钟
Factor models and the CAPM9分钟
Multi-Factor models and Fama-French7分钟
Factor benchmarks and Style analysis8分钟
Shortcomings of cap-weighted indices11分钟
From cap-weighted benchmarks to smart-weighted benchmarks12分钟
Introduction to Lab sessions6分钟
Module 1 Lab Session - Foundations42分钟
3 个阅读材料
Requirements2分钟
Material at your disposal5分钟
Module 1- Key points2分钟
1 个练习
Module 1- Graded Quiz1小时
完成时间为 2 小时
Robust estimates for the covariance matrix
完成时间为 2 小时
7 个视频 （总计 70 分钟）, 1 个阅读材料, 1 个测验
7 个视频
Estimating the Covariance Matrix with a Factor Model9分钟
Honey I Shrunk the Covariance Matrix!7分钟
Portfolio Construction with Time-Varying Risk Parameters8分钟
Exponentially weighted average8分钟
ARCH and GARCH Models9分钟
Module 2 Lab Session - Covariance Estimation13分钟
1 个阅读材料
Module 2-Key points2分钟
1 个练习
Module 2 - Graded quiz1小时
完成时间为 3 小时
Robust estimates for expected returns
完成时间为 3 小时
7 个视频 （总计 77 分钟）, 2 个阅读材料, 1 个测验
7 个视频
Agnostic Priors on Expected Return Estimates6分钟
Using Factor Models to Estimate Expected Returns11分钟
Extracting Implied Expected Returns8分钟
Introducing Active Views6分钟
Black-Litterman Analysis10分钟
Module 3 Lab Session- Black Litterman23分钟
2 个阅读材料
Module 3-Key points2分钟
The Intuition Behind Black-Litterman Model Portfolios10分钟
1 个练习
Module 3 - Graded Quiz1小时
完成时间为 3 小时
Portfolio Optimization in Practice
完成时间为 3 小时
7 个视频 （总计 67 分钟）, 4 个阅读材料, 1 个测验
7 个视频
Scientific Diversification11分钟
Measuring risk contributions6分钟
Simplified risk parity portfolios7分钟
Risk Parity Portfolios7分钟
Comparing Diversification Options8分钟
Module 4 Lab Session - Risk Contribution and Risk Parity15分钟
4 个阅读材料
Module 4-Key points2分钟
Survey: Alternative Equity Beta Investing10分钟
Dive into heuristic diversification10分钟
To be continued (2)10分钟
1 个练习
Module 4 - Graded quiz1小时
审阅
来自ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON的热门评论
由 MM 提供Apr 13, 2020
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.
由 MM 提供Dec 2, 2019
The course is excellent, one of the best finance courses on coursera, but you should know in advance that you will not have any help from the staff, at least that was my experience.
由 KA 提供Jan 4, 2020
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.
由 DB 提供May 4, 2020
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,
关于 Investment Management with Python and Machine Learning 专项课程
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.
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