中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.
Using R for Regression and Machine Learning in Investment
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
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要了解的详细信息
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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
该课程共有2个模块
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
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.
涵盖的内容
5个视频10篇阅读材料1个作业
位教师
授课教师评分
4.6 (5个评价)
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