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Learning Quantitative Finance with R

You're reading from   Learning Quantitative Finance with R Implement machine learning, time-series analysis, algorithmic trading and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Length 284 pages
Edition 1st Edition
Languages
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Authors (2):
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PRASHANT VATS PRASHANT VATS
Author Profile Icon PRASHANT VATS
PRASHANT VATS
Dr. Param Jeet Dr. Param Jeet
Author Profile Icon Dr. Param Jeet
Dr. Param Jeet
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Table of Contents (10) Chapters Close

Preface 1. Introduction to R FREE CHAPTER 2. Statistical Modeling 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

Portfolio risk


With the use of R language, we can manage portfolios better by mitigating the risk and portfolio optimization. To avoid the risk associated with portfolio analysis, diversification of the portfolio is required, with the selection of optimum weights for the portfolio's constituents.

Let us try to find the optimal weight of the portfolio whose stocks are IBM and FB, and using the CAPM. First, let us get the relevant data by executing the following code:

>GPSCMonthlyUrl<-'http://ichart.yahoo.com/table.csv?s=%5EGSPC&a=00&b=1&c=2015&d=00&e=1&f=2017&g=m' 
>GPSCMonthlyData <- read.csv(GPSCMonthlyUrl) 
>IBMMonthlyUrl<-'http://ichart.yahoo.com/table.csv?s=IBM&a=00&b=1&c=2015&d=00&e=1&f=2017&g=m' 
>IBMMonthlyData <- read.csv(IBMMonthlyUrl) 
>FBMonthlyUrl<-'http://ichart.yahoo.com/table.csv?s=FB&a=00&b=1&c=2015&d=00&e=1&f=2017&g=m' 
>FBMonthlyData <- read.csv(FBMonthlyUrl...
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