EDHEC Investment Management with Python and Machine Learning

About this Coursera Specialization offered by EDHEC Business School

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.

Instructors

  Vijay Vaidyanathan, PhD  - Optimal Asset Management Inc.   EDHEC Business School
  Lionel Martellini, PhD   - EDHEC-Risk Institute, Director, EDHEC Business School
  John Mulvey              - Princeton University Professor in the Operations Research and Financial Engineering Department 
  Gideon Ozik              - Founder and managing partner of MKT MediaStats, EDHEC Business School
  Sean McOwen              - Quantitative Analyst Finance

Course 1 Introduction to Portfolio Construction and Analysis with Python

Taught by: Vijay Vaidyanathan and Lionel Martellini

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, 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. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction.

  WEEK 1  Analysing returns
  WEEK 2  An Introduction to Portfolio Optimization
  WEEK 3  Beyond Diversification
  WEEK 4  Introduction to Asset-Liability Management

Course 2 Advanced Portfolio Construction and Analysis with Python

Taught by: Vijay Vaidyanathan and Lionel Martellini

  WEEK 1  Style & Factors
  WEEK 2  Robust estimates for the covariance matrix
  WEEK 3  Robust estimates for expected returns
  WEEK 4  Portfolio Optimization in Practice
  

Course 3 Python and Machine Learning for Asset Management

Taught by: John Mulvey and Lionel Martellini

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.

  WEEK 1  Introducing the fundamentals of machine learning
  WEEK 2  Machine learning techniques for robust estimation of factor models
  WEEK 3  Machine learning techniques for efficient portfolio diversification
  WEEK 4  Machine learning techniques for regime analysis 
  WEEK 5  Identifying recessions, crash regimes and feature selection

Course 4 Python and Machine-Learning for Asset Management with Alternative Data Sets

Taught by: Gideon Ozik and Sean McOwen

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills.

  WEEK 1  Consumption
  WEEK 2  Textual Analysis for Financial Applications
  WEEK 3  Processing Corporate Filings
  WEEK 4  Using Media-Derived Data

Offered by

EDHEC Business School

Ecole des Hautes Etudes Commerciales du Nord

Ranked #5 Masters in Finance by Financial Times 2022

 http://www.edhec.com
 https://rankings.ft.com/schools/333/edhec-business-school/rankings/2874/masters-in-finance-pre-experience-2022/ranking-data

Operating from campuses in Lille, Nice, Paris, London and Singapore, EDHEC is one of the top 10 European business schools. Fully international and directly connected to the business world, EDHEC is a school for business, rather than a business school, where excellence in teaching and research focuses on innovation to stimulate entrepreneurship and creativity. EDHEC functions as a genuine laboratory of ideas and produces innovative solutions valued by businesses. The School’s teaching philosophy, inspired by its award-winning research activities, focuses on “learning by doing”.