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

Support vector machine


Support vector machine is another supervised learning algorithm that can be used for classification and regression. It is able to classify data linearly and nonlinearly using kernel methods. Each data point in the training dataset is labeled, as it is supervised learning, and mapped to the input feature space, and the aim is to classify every point of new data to one of the classes. A data point is an N dimension number, as N is the number of features, and the problem is to separate this data using N-1 dimensional hyperplane and this is considered to be a linear classifier. There might be many classifiers which segregate the data; however, the optimal classifier is one which has the maximum margin between classes. The maximum margin hyperplane is one which has the maximum distance from the closest point in each size and the corresponding classifier is called the maximum margin classifier. Package e1071 has all functionalities related to the support vector machine so...