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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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Toc

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

Ranking of variables


After fitting a regression/predictive model, we need to understand what the relative ranking of significant attributes is on a comparative scale. This is explained by Beta parameter estimates. Beta, or standardized coefficients, are the slopes we get if all the variables are on the same scale, which is done by converting them to z-scores before doing the predictive modeling (regression). Beta coefficients allow a comparison of the approximate relative importance of the predictors and hence the variables can be ranked, which neither the unstandardized coefficients nor the Pvalues can. Scaling, or standardizing, the data vectors can be done using the scale() function. Once the scaled variables are created, the regression is redone using them. The resulting coefficients are the beta coefficients.

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