Book Image

Learning Quantitative Finance with R

By : Dr. Param Jeet, PRASHANT VATS
Book Image

Learning Quantitative Finance with R

By: Dr. Param Jeet, PRASHANT VATS

Overview of this book

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.
Table of Contents (16 chapters)
Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Random forest


Random forest is one of the best tree-based methods. Random forest is an ensemble of decision trees and each decision tree has certain weights associated with it. A decision of the random forest is decided like voting, as the majority of decision tree outcomes decide the outcome of the random forest. So we start using the randomForest package and this can be installed and loaded using the following commands:

>install.packages("randomForest")
>library(randomForest)

We can also use the following command to know more about this randomForest package, including version, date of release, URL, set of functions implemented in this package, and much more:

>library(help=randomForest)

Random forest works best for any type of problem and handles classification, regression, and unsupervised problems quite well. Depending upon the type of labeled variable, it will implement relevant decision trees; for example, it uses classification for factor target variables, regression for numeric...