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Using R for Regression and Machine Learning in Investment

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kurs ist nicht verfügbar in Deutsch (Deutschland)

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Using R for Regression and Machine Learning in Investment

Dozent: Youngju Nielsen

Bei enthalten

Mehr erfahren

2 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

2 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: Statistical Analysis
Kategorie: Portfolio Risk
Kategorie: Investment Management
Kategorie: Predictive Analytics
Kategorie: Machine Learning Methods
Kategorie: Statistical Programming
Kategorie: Investments
Kategorie: Risk Analysis
Kategorie: Machine Learning Algorithms
Kategorie: Risk Modeling
Kategorie: Financial Analysis
Kategorie: Machine Learning
Kategorie: Statistical Methods
Kategorie: Applied Machine Learning
Kategorie: Predictive Modeling
Kategorie: Advanced Analytics
Kategorie: Risk Management
Kategorie: Regression Analysis

Werkzeuge, die Sie lernen werden

Kategorie: R (Software)
Kategorie: R Programming

Wichtige Details

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Bewertungen

1 Aufgabe

Unterrichtet in Englisch

91%

of learners achieved a positive career outcome

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In diesem Kurs gibt es 2 Module

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.

Moduldetails

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.

Das ist alles enthalten

5 Videos9 Lektüren

5 VideosInsgesamt 82 Minuten

L1. Brief History on Investing, Machine Learning and Alternative Data14 Minuten
L2. Ingredients for Maching Learning Based Investment15 Minuten
L3. Big Picture of Alorithm-Driven Investment18 Minuten
L4. Understanding the Characteristics of Factors20 Minuten
L5. Understanding Machine Learning Concepts16 Minuten

9 LektürenInsgesamt 340 Minuten

Learning Content60 Minuten
References10 Minuten
Learning Content60 Minuten
References10 Minuten
Learning Content60 Minuten
Learning Content60 Minuten
References10 Minuten
Learning Content60 Minuten
References10 Minuten

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.