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

Feature selection


Feature selection is one of the toughest parts of financial model building. Feature selection can be done statistically or by having domain knowledge. Here we are going to discuss only a few of the statistical feature selection methods in the financial space.

Removing irrelevant features

Data may contain highly correlated features and the model does better if we do not have highly correlated features in the model. The Caret R package gives the method for finding a correlation matrix between the features, which is shown by the following example.

A few lines of data used for correlation analysis and multiple regression analysis are displayed here by executing the following code:

>DataMR = read.csv("C:/Users/prashant.vats/Desktop/Projects/BOOK R/DataForMultipleRegression.csv") 
>head(DataMR) 

StockYPrice

StockX1Price

StockX2Price

StockX3Price

StockX4Price

1

80.13

72.86

93.1

63.7

83.1

2

79.57

72.88

90.2

63.5

82

3

79.93

71.72

99

64...