使用Python和机器学习进行投资管理专项课程

Investment Management with Python and Machine Learning

This 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.

EDHEC Business School

Coursera

商业管理

简单(初级)

2 个月

本课程由Coursera和Linkshare共同提供
  • 英语
  • 890

课程概况

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.

你将学到什么

Write custom Python code and use existing Python libraries to build and analyse efficient portfolio strategies.

Write custom Python code and use existing Python libraries to estimate risk and return parameters, and build better diversified portfolios.

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets

Gain an understanding of advanced data analytics methodologies, and quantitative modelling applied to alternative data in investment decisions

包含课程

课程1
Introduction to Portfolio Construction and Analysis with Python

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. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. 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. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods.

课程2
Advanced Portfolio Construction and Analysis with Python

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. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods. If you follow along and implement all the lab exercises, you will complete the course with a powerful toolkit that you will be able to use to perform your own analysis and build your own implementations and perhaps even use your newly acquired knowledge to improve on current methods.

课程3
Python and Machine Learning for Asset Management

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.

课程4
Python and Machine-Learning for Asset Management with Alternative Data sets

Over-utilization of market and accounting data over the last few decades has lead to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as 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. As a result, participants will learn new data and research techniques applied to the financial markets while strengthening data science and python skills. This course covers a broad array of alternative data types including consumption based datasets, corporate filings, social media, and various datasets scraped directly from the web. The course teaches tools such as analytics, text mining, sentiment analysis, and data visualization required to process the data in order to gain insight for financial market applications.

预备知识

This digital Specialization program is meant to be self-contained and no prior knowledge or Python or portfolio analysis is assumed or required. On the other hand, learners are expected to show a good dose of enthusiasm for, and interest in, the subject of data science applied to investment management.

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