課程信息
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中級

完成時間大約為16 小時

建議:4 weeks, from 3 to 4 hours per week...

英語(English)

字幕:英語(English)

您將學到的內容有

  • Check

    Analyze style and factor exposures of portfolios

  • Check

    Implement robust estimates for the covariance matrix

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    Implement Black-Litterman portfolio construction analysis

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    Implement a variety of robust portfolio construction models

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為16 小時

建議:4 weeks, from 3 to 4 hours per week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 3 小時

Style & Factors

9 個視頻 (總計 114 分鐘), 3 個閱讀材料, 1 個測驗
9 個視頻
Introduction to factor investing12分鐘
Factor models and the CAPM9分鐘
Multi-Factor models and Fama-French7分鐘
Factor benchmarks and Style analysis8分鐘
Shortcomings of cap-weighted indices11分鐘
From cap-weighted benchmarks to smart-weighted benchmarks12分鐘
Introduction to Lab sessions6分鐘
Module 1 Lab Session - Foundations42分鐘
3 個閱讀材料
Requirements2分鐘
Material at your disposal5分鐘
Module 1- Key points2分鐘
1 個練習
Module 1- Graded Quiz1小時
2
完成時間為 2 小時

Robust estimates for the covariance matrix

7 個視頻 (總計 70 分鐘), 1 個閱讀材料, 1 個測驗
7 個視頻
Estimating the Covariance Matrix with a Factor Model9分鐘
Honey I Shrunk the Covariance Matrix!7分鐘
Portfolio Construction with Time-Varying Risk Parameters8分鐘
Exponentially weighted average8分鐘
ARCH and GARCH Models9分鐘
Module 2 Lab Session - Covariance Estimation13分鐘
1 個閱讀材料
Module 2-Key points2分鐘
1 個練習
Module 2 - Graded quiz1小時
3
完成時間為 3 小時

Robust estimates for expected returns

7 個視頻 (總計 77 分鐘), 2 個閱讀材料, 1 個測驗
7 個視頻
Agnostic Priors on Expected Return Estimates6分鐘
Using Factor Models to Estimate Expected Returns11分鐘
Extracting Implied Expected Returns8分鐘
Introducing Active Views6分鐘
Black-Litterman Analysis10分鐘
Module 3 Lab Session- Black Litterman23分鐘
2 個閱讀材料
Module 3-Key points2分鐘
The Intuition Behind Black-Litterman Model Portfolios10分鐘
1 個練習
Module 3 - Graded Quiz1小時
4
完成時間為 3 小時

Portfolio Optimization in Practice

7 個視頻 (總計 67 分鐘), 3 個閱讀材料, 1 個測驗
7 個視頻
Scientific Diversification11分鐘
Measuring risk contributions6分鐘
Simplified risk parity portfolios7分鐘
Risk Parity Portfolios7分鐘
Comparing Diversification Options8分鐘
Module 4 Lab Session - Risk Contribution and Risk Parity15分鐘
3 個閱讀材料
Module 4-Key points2分鐘
Survey: Alternative Equity Beta Investing10分鐘
Dive into heuristic diversification10分鐘
1 個練習
Module 4 - Graded quiz1小時
4.9
1 個審閱Chevron Right

來自Advanced Portfolio Construction and Analysis with Python的熱門評論

創建者 KRNov 6th 2019

Very demanding, especially the tests. Extremely interesting lectures and to the point.

講師

Avatar

Lionel Martellini, PhD

EDHEC-Risk Institute, Director
Finance
Avatar

Vijay Vaidyanathan, PhD

Optimal Asset Management Inc.
CEO

關於 EDHEC Business School

Founded in 1906, EDHEC is now one of Europe’s top 15 business schools . Based in Lille, Nice, Paris, London and Singapore, and counting over 90 nationalities on its campuses, EDHEC is a fully international school directly connected to the business world. With over 40,000 graduates in 120 countries, it trains committed managers capable of dealing with the challenges of a fast-evolving world. Harnessing its core values of excellence, innovation and entrepreneurial spirit, EDHEC has developed a strategic model founded on research of true practical use to society, businesses and students, and which is particularly evident in the work of EDHEC-Risk Institute and Scientific Beta. The School functions as a genuine laboratory of ideas and plays a pioneering role in the field of digital education via EDHEC Online, the first fully online degree-level training platform. These various components make EDHEC a centre of knowledge, experience and diversity, geared to preparing new generations of managers to excel in a world subject to transformational change. EDHEC in figures: 8,600 students in academic education, 19 degree programmes ranging from bachelor to PhD level, 184 professors and researchers, 11 specialist research centres. ...

關於 Investment Management with Python and Machine Learning 專項課程

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....
Investment Management with Python and Machine Learning

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