Using R for Regression and Machine Learning in Investment

Using R for Regression and Machine Learning in Investment

位教师：Youngju Nielsen

访问权限由 Coursera Learning Team 提供

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Understanding the basic common concept of machine learning
Familiarizing with most commonly used methodology, regression
Distinguishing in-sample and out-of-sample results and leading to well-performing models in a real-life

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

1 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有2个模块

In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.

The course is designed with the assumption that most students already have a little bit of knowledge in financial economics and R programming. Students are expected to have heard about stocks and bonds and balance sheets, earnings, etc., and know the introductory statistics level, such as mean, median, distribution, regression, etc. Students are also expected to know of the instructors' 1st course, 'Fundamental of data-driven investment.' The instructor will explain the detail of R programming. It will be an excellent course for you to improve your programming skills but you must have basic knowledge in R. If you are very good at R programming, it will provide you with an excellent opportunity to practice again with finance and investment examples.

Understand the characteristics of predictive models and various data in investment The instructor will give you the big picture of the algorithm-driven investment decision-making process. After you understand that, we will review the regression concept and connect it with the core concepts of machine learning methodologies.

涵盖的内容

5个视频9篇阅读材料

5个视频总计82分钟

L1. Brief History on Investing, Machine Learning and Alternative Data 14分钟
L2. Ingredients for Maching Learning Based Investment 15分钟
L3. Big Picture of Alorithm-Driven Investment 18分钟
L4. Understanding the Characteristics of Factors 20分钟
L5. Understanding Machine Learning Concepts 16分钟

9篇阅读材料总计340分钟

Learning Content 60分钟
References 10分钟
Learning Content 60分钟
References 10分钟
Learning Content 60分钟
Learning Content 60分钟
References 10分钟
Learning Content 60分钟
References 10分钟

Use regression methodology for various investment analysis purpose and improve models by using ridge, lasso, and logistic regression. First of all, you will learn how you can gauge investment strategy using backtesting. You learned the first component of investment strategy, returns, in the first week. You will expand your study to assessing investment risks. To understand stocks' risks, you will calculate covariance and correlation matrix using historical time-series stock return data. You will extend this to market factor and three-factor models to understand the risk you are facing with your investment. Finally, you will calculate factor exposure using a 3-factor model from week 2 and separate common factor risk and idiosyncratic risk of the stock.