课程信息

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完成时间大约为24 小时
英语(English)
字幕:英语(English)

您将学到的内容有

  • Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques

  • Write custom Python code to estimate risk and return parameters

  • Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios

  • Build custom utilities in Python to test and compare portfolio strategies

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。
完成时间大约为24 小时
英语(English)
字幕:英语(English)

提供方

北方高等商学院 徽标

北方高等商学院

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up92%(1,309 个评分)Info
1

1

完成时间为 5 小时

Analysing returns

完成时间为 5 小时
14 个视频 (总计 225 分钟), 5 个阅读材料, 1 个测验
14 个视频
Installing Anaconda3分钟
Fundamentals of Returns10分钟
Lab Session-Basics of returns29分钟
Measures of Risk and Reward9分钟
Lab Session-Risk Adjusted returns28分钟
Measuring Max Drawdown10分钟
Lab Session-Drawdown30分钟
Deviations from Normality9分钟
Lab Session-Building your own modules12分钟
Downside risk measures8分钟
Lab Session-Deviations from Normality30分钟
Estimating VaR10分钟
Lab Session-Semi Deviation, VAR and CVAR27分钟
5 个阅读材料
Material at your disposal5分钟
Material for the Lab Sessions10分钟
Module 1- Key points2分钟
INCORRECT STATEMENT IN “DEVIATION FROM NORMALITY” VIDEO10分钟
Before the Quiz10分钟
1 个练习
Module 1 Graded Quiz1小时
2

2

完成时间为 4 小时

An Introduction to Portfolio Optimization

完成时间为 4 小时
10 个视频 (总计 172 分钟), 1 个阅读材料, 1 个测验
10 个视频
Lab Session-Efficient frontier-Part 123分钟
Markowitz Optimization and the Efficient Frontier9分钟
Applying quadprog to draw the efficient Frontier11分钟
Lab Session-Asset Efficient Frontier-Part 220分钟
Lab Session-Applying Quadprog to Draw the Efficient Frontier38分钟
Fund Separation Theorem and the Capital Market Line7分钟
Lab Session-Locating the Max Sharpe Ratio Portfolio25分钟
Lack of robustness of Markowitz analysis5分钟
Lab Session-Plotting EW and GMV on the Efficient Frontier20分钟
1 个阅读材料
Module 2 - Key points2分钟
1 个练习
Module 2 Graded Quiz1小时
3

3

完成时间为 5 小时

Beyond Diversification

完成时间为 5 小时
15 个视频 (总计 236 分钟), 4 个阅读材料, 1 个测验
15 个视频
Lab session- Limits of Diversification-Part119分钟
Lab session-Limits of diversification-Part 222分钟
An introduction to CPPI - Part 17分钟
An introduction to CPPI - Part 210分钟
Lab session-CPPI and Drawdown Constraints-Part129分钟
Lab session-CPPI and Drawdown Constraints-Part228分钟
Simulating asset returns with random walks10分钟
Monte Carlo Simulation6分钟
Lab Session-Random Walks and Monte Carlo22分钟
Analyzing CPPI strategies11分钟
Lab Session-Installing IPYWIDGETS5分钟
Designing and calibrating CPPI strategies12分钟
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part119分钟
Lab session - interactive plots of monte Carlo Simulations of CPPI and GBM-Part221分钟
4 个阅读材料
Module 3 - Key points2分钟
ipywidgets installation - info5分钟
gbm function10分钟
Instruction prior to begin the module 3 graded quizz10分钟
1 个练习
Module 3 Graded Quiz45分钟
4

4

完成时间为 9 小时

Introduction to Asset-Liability Management

完成时间为 9 小时
12 个视频 (总计 327 分钟), 5 个阅读材料, 1 个测验
12 个视频
Lab Session-Present Values,liabilities and funding ratio22分钟
Liability hedging portfolios12分钟
Lab Session-CIR Model and cash vs ZC bonds1 小时 8 分
Liability-driven investing (LDI)10分钟
Lab Session-Liability driven investing51分钟
Choosing the policy portfolio14分钟
Lab Session-Monte Carlo simulation of coupon-bearing bonds using CIR33分钟
Beyond LDI11分钟
Lab Session-Naive risk budgeting between the PSP & GHP44分钟
Liability-friendly equity portfolios10分钟
Lab Session-Dynamic risk budgeting between PSP & LHP40分钟
5 个阅读材料
Module 4 - Key points2分钟
Dynamic Liability-Driven Investing Strategies: The Emergence Of A New Investment Paradigm For Pension Funds?1 小时 30 分
Liability-Driven-Investing1小时
Instruction prior to begin module 4 graded quizz2分钟
To be continued (1)5分钟
1 个练习
Module 4 Graded Quiz1小时

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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....
Investment Management with Python and Machine Learning

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