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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
Languages
Tools
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis FREE CHAPTER 2. Factor Models 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

Revealing connections


To start our investigation for shares with huge upside potential, we have to check the connections between individual ratios quantified a year ago and the total return of the next year. For the sake of this chapter, we picked the following ratios. We took the values from 1 year earlier so that we can contrast these with last year's TRS:

  • Cash/assets 1 year ago

  • Net fixed assets/total number of assets 1 year ago

  • Assets/1000 employees 1 year ago

  • Price/cash flow average of last 5 years 1 year ago

  • Price/cash flow 1 year ago

  • Operating income/net sales 1 year ago

  • Dividend payout ratio 1 year ago

  • Asset turnover 1 year ago

  • P/BV 1 year ago

  • Operating income/net sales 1 year ago

  • Revenue growth in the last 1 year 1 year ago

  • Long-term debt/capital 1 year ago

  • Debt/EBITDA 1 year ago

  • Market capitalization 1 year ago

  • P/E 1 year ago

Calculating Pearson's correlation coefficients may be a good start:

d_filt <- na.omit(d)[,setdiff(1:19, c(1,2,18))]
cor_mtx <- cor(d_filt)
round(cor_mtx, 3)

When looking...

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