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Mastering R for Quantitative Finance

Mastering R for Quantitative Finance

By : Gabler
4 (11)
Buy this Book
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Mastering R for Quantitative Finance

Mastering R for Quantitative Finance

4 (11)
By: Gabler
Buy this Book

Overview of this book

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Time Series Analysis
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1. Time Series Analysis
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Multivariate time series analysis
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Volatility modeling
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Summary
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References and reading list
2
2. Factor Models
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2. Factor Models
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Arbitrage pricing theory
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Modeling in R
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Summary
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References
3
3. Forecasting Volume
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3. Forecasting Volume
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Motivation
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The intensity of trading
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The volume forecasting model
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Implementation in R
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Summary
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References
4
4. Big Data – Advanced Analytics
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4. Big Data – Advanced Analytics
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Getting data from open sources
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Introduction to big data analysis in R
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K-means clustering on big data
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Big data linear regression analysis
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Summary
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References
5
5. FX Derivatives
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5. FX Derivatives
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Terminology and notations
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Currency options
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Exchange options
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Quanto options
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Summary
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References
6
6. Interest Rate Derivatives and Models
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6. Interest Rate Derivatives and Models
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The Black model
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The Vasicek model
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The Cox-Ingersoll-Ross model
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Parameter estimation of interest rate models
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Using the SMFI5 package
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Summary
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References
7
7. Exotic Options
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7. Exotic Options
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A general pricing approach
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The role of dynamic hedging
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How R can help a lot
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A glance beyond vanillas
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Greeks – the link back to the vanilla world
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Pricing the Double-no-touch option
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Another way to price the Double-no-touch option
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The life of a Double-no-touch option – a simulation
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Exotic options embedded in structured products
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Summary
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References
8
8. Optimal Hedging
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8. Optimal Hedging
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Hedging of derivatives
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Hedging in the presence of transaction costs
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Further extensions
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Summary
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References
9
9. Fundamental Analysis
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9. Fundamental Analysis
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The basics of fundamental analysis
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Collecting data
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Revealing connections
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Including multiple variables
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Separating investment targets
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Setting classification rules
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Backtesting
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Industry-specific investment
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Summary
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References
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10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
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10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
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Market efficiency
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Technical analysis
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Neural networks
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Logoptimal portfolios
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Summary
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References
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11. Asset and Liability Management
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11. Asset and Liability Management
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Data preparation
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Interest rate risk measurement
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Liquidity risk measurement
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Modeling non-maturity deposits
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Summary
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References
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12. Capital Adequacy
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12. Capital Adequacy
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Principles of the Basel Accords
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Risk measures
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Risk categories
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Summary
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References
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13. Systemic Risks
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13. Systemic Risks
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Systemic risk in a nutshell
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The dataset used in our examples
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Core-periphery decomposition
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The simulation method
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Possible interpretations and suggestions
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Summary
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References
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Index
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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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