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Python and Machine Learning for Asset Management
  • Level Professional
  • المدة 16 ساعات hours
  • الطبع بواسطة EDHEC Business School
  • Offered by

عن

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions. The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis. You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept. At the end of this course, you will master the various machine learning techniques in investment management.

الوحدات

Section 1- Introduction

    • 4 Videos
    • 3 Readings
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4 Videos

  • Welcome to the Python Machine-Learning for Investment management course
  • Introduction to machine-learning
  • Financial applications
  • Supervised learning

3 Readings

  • Requirements
  • Material at your disposal
  • Machine Learning for Investment Decisions: A Brief Guided Tour

Section 2- Review of unsupervised learning

    • 5 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Module 1Graded Quiz

1 Discussions

  • Challenges ahead

1 Labs

  • Python lab sessions

5 Videos

  • First algorithms
  • Highlights of best practice
  • Unsupervised learning
  • Challenges ahead
  • Lab session optimal portfolio

2 Readings

  • References for module 1"Introducing the fundamentals of machine learning"
  • Lab session optimal portfolio

Section 1- Introduction to factor models and their use in portfolio construction and analysis

    • 4 Videos
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4 Videos

  • Introduction to module 2 - Basics of factor investing
  • Introducing Factor Models
  • Typology of factor models
  • Using factor models in portfolio construction and analysis

Section 2 - Robust estimation of factor models with machine learning techniques

    • 4 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Module 2 Graded Quiz

4 Videos

  • Penalty methods
  • Setting factor loadings and examples
  • Shrinkage concepts
  • Lab session - Jupiter notebook on Factor Models

1 Readings

  • References for module 2"Machine learning techniques for robust estimation of factor models"

Section 1- Measuring diversification benefits

    • 3 Videos
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3 Videos

  • Introduction to module 3 -Machine learning techniques for efficient portfolio diversification
  • Benefits of portfolio diversification
  • Portfolio diversification measures

Section 2 - Maximizing diversification benefits via machine learning

    • 5 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Module 3 Graded Quiz

1 Discussions

  • Selecting a portfolio of assets

5 Videos

  • Principle component analysis
  • Role of clustering
  • Graphical analysis
  • Selecting a portfolio of assets
  • Lab session: Graphical Network Analysis

3 Readings

  • Supplementary material PCA
  • References for the module "Machine learning techniques for efficient portfolio diversification"
  • Reference for the module "Selecting a portfolio of assets"

Section 1- Portfolio Decisions with Time-Varying Market Conditions

    • 2 Videos
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2 Videos

  • Introduction to economic regimes
  • Portfolio Decisions with Time-Varying Market Conditions

Section 2 - Robust estimation of regime switching models with machine learning techniques

    • 5 Videos
    • 4 Readings
    • 1 Assignment
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1 Assignment

  • Module 4 Graded Quiz

5 Videos

  • Trend filtering
  • A scenario based portfolio model
  • A two regime portfolio example
  • A multi regime model for a University Endowment
  • NEW Lab session- Jupyter notebook on regime-based investment model

4 Readings

  • Information on the "trend filtering" video
  • Information on "scenario based portfolio model" video
  • References for the module "Machine learning techniques for regime analysis"
  • Regime-aware asset allocation

Section 1-Introduction to classical methods and Machine-learning processes

    • 4 Videos
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4 Videos

  • Introduction to module 5
  • Traditional approaches
  • Machine-Learning Processes
  • Several Machine Learning Methods

Section 2 - Predicting credit contractions and crash regimes

    • 3 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Module 5 Graded Quiz

3 Videos

  • Predicting recessions
  • Challenges ahead
  • Lab session 5: Regime Prediction with Machine Learning

2 Readings

  • References for the module "Identifying recessions, crash regimes and features selection"
  • To be continued (3)

Auto Summary

Master machine learning for investment management with this comprehensive course designed by experts Lionel Martellini and John Mulvey. Ideal for finance professionals, the course covers fundamentals to advanced techniques, enhancing your skills in portfolio decisions and risk management. Featuring a structured learning process, videos, readings, quizzes, and Jupiter notebooks, it ensures a deep understanding of data science in asset management. Available on Coursera with Starter and Professional subscription options, the course spans 960 minutes of in-depth content.

Claudia Carrone
Instructors

Claudia Carrone

John Mulvey - Princeton University
Instructors

John Mulvey - Princeton University