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Analyzing Financial Data and Implementing Financial Models Using R 2015 ed. [Hardback]

  • Formāts: Hardback, 351 pages, height x width: 235x155 mm, weight: 6742 g, 1 Tables, black and white; 60 Illustrations, black and white; XVI, 351 p. 60 illus., 1 Hardback
  • Sērija : Springer Texts in Business and Economics
  • Izdošanas datums: 14-Apr-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319140744
  • ISBN-13: 9783319140742
Citas grāmatas par šo tēmu:
  • Formāts: Hardback, 351 pages, height x width: 235x155 mm, weight: 6742 g, 1 Tables, black and white; 60 Illustrations, black and white; XVI, 351 p. 60 illus., 1 Hardback
  • Sērija : Springer Texts in Business and Economics
  • Izdošanas datums: 14-Apr-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319140744
  • ISBN-13: 9783319140742
Citas grāmatas par šo tēmu:
This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement financial models. This text will show students how to obtain publicly available data, manipulate such data, implement the models, and generate typical output expected for a particular analysis.

This text aims to overcome several common obstacles in teaching financial modeling. First, most texts do not provide students with enough information to allow them to implement models from start to finish. In this book, we walk through each step in relatively more detail and show intermediate R output to help students make sure they are implementing the analyses correctly. Second, most books deal with sanitized or clean data that have been organized to suit a particular analysis. Consequently, many students do not know how to deal with real-world data or know how to apply simple data manipulation techniques to get the real-world data into a usable form. This book will expose students to the notion of data checking and make them aware of problems that exist when using real-world data. Third, most classes or texts use expensive commercial software or toolboxes. In this text, we use R to analyze financial data and implement models. R and the accompanying packages used in the text are freely available; therefore, any code or models we implement do not require any additional expenditure on the part of the student.

Demonstrating rigorous techniques applied to real-world data, this text covers a wide spectrum of timely and practical issues in financial modeling, including return and risk measurement, portfolio management, options pricing, and fixed income analysis.

Recenzijas

This book is aimed at students in finance and economics who are beginners to the R statistical programming language. We recommend the book for its intended audience, plus perhaps personal investors who want to experiment in R with portfolio optimization and simulation studies of likely ranges of securities. (Lauren Burr and Tom Burr, Technometrics, Vol. 58 (2), April, 2016)

1 Prices 1(54)
1.1 Importing Daily Stock Price Data
2(1)
1.2 Importing Price Data from Yahoo Finance
2(10)
1.3 Checking the Data
12(4)
1.3.1 Plotting the Data
12(2)
1.3.2 Checking the Dimension
14(1)
1.3.3 Outputting Summary Statistics
15(1)
1.3.4 Checking the Ticker Symbol
15(1)
1.4 Basic Data Manipulation Techniques
16(12)
1.4.1 Keeping and Deleting One Row
16(1)
1.4.2 Keeping First and Last Rows
17(1)
1.4.3 Keeping Contiguous Rows
18(1)
1.4.4 Keeping First Three Rows and Last Row
19(1)
1.4.5 Keeping and Deleting One Column
20(1)
1.4.6 Keeping Non-Contiguous Columns
21(1)
1.4.7 Keeping Contiguous Columns
22(1)
1.4.8 Keeping Contiguous and Non-Contiguous Columns
22(1)
1.4.9 Subsetting Rows and Columns
23(1)
1.4.10 Subsetting Using Dates
23(2)
1.4.11 Converting Daily Prices to Weekly and Monthly Prices
25(3)
1.5 Comparing Capital Gains of Multiple Securities Over Time
28(13)
1.5.1 Alternative Presentation of Normalized Price Chart
37(4)
1.6 Technical Analysis Examples
41(11)
1.6.1 Trend: Simple Moving Average Crossover
41(3)
1.6.2 Volatility: Bollinger Bands
44(3)
1.6.3 Momentum: Relative Strength Index
47(5)
1.7 Further Reading
52(1)
References
53(2)
2 Individual Security Returns 55(24)
2.1 Price Returns
56(2)
2.2 Total Returns
58(3)
2.3 Logarithmic Total Returns
61(2)
2.4 Cumulating Multi-Day Returns
63(5)
2.4.1 Cumulating Arithmetic Returns
64(1)
2.4.2 Cumulating Logarithmic Returns
65(1)
2.4.3 Comparing Price Return and Total Return
66(2)
2.5 Weekly Returns
68(4)
2.6 Monthly Returns
72(1)
2.7 Comparing Performance of Multiple Securities: Total Returns
73(6)
3 Portfolio Returns 79(36)
3.1 Constructing Portfolio Returns (Long Way)
79(3)
3.2 Constructing Portfolio Returns (Matrix Algebra)
82(1)
3.3 Constructing Benchmark Portfolio Returns
83(30)
3.3.1 Equal-Weighted Portfolio
86(7)
3.3.2 Value-Weighted Portfolio
93(16)
3.3.3 Normalized EW and VW Portfolio Price Chart
109(1)
3.3.4 Saving Benchmark Portfolio Returns into a CSV File
110(3)
3.4 Further Reading
113(1)
Reference
113(2)
4 Risk 115(46)
4.1 Risk-Return Trade-Off
116(5)
4.2 Individual Security Risk
121(5)
4.3 Portfolio Risk
126(12)
4.3.1 Two Assets (Manual Approach)
127(4)
4.3.2 Two Assets (Matrix Algebra)
131(2)
4.3.3 Multiple Assets
133(5)
4.4 Value-at-Risk
138(8)
4.4.1 Gaussian VaR
138(2)
4.4.2 Historical VaR
140(6)
4.5 Expected Shortfall
146(4)
4.5.1 Gaussian ES
147(1)
4.5.2 Historical ES
147(2)
4.5.3 Comparing VaR and ES
149(1)
4.6 Alternative Risk Measures
150(8)
4.6.1 Parkinson
150(2)
4.6.2 Garman-Klass
152(1)
4.6.3 Rogers, Satchell, and Yoon
153(2)
4.6.4 Yang and Zhang
155(2)
4.6.5 Comparing the Risk Measures
157(1)
4.7 Further Reading
158(1)
References
158(3)
5 Factor Models 161(32)
5.1 CAPM
161(10)
5.2 Market Model
171(1)
5.3 Rolling Window Regressions
172(3)
5.4 Fama-French Three Factor Model
175(6)
5.5 Event Studies
181(9)
5.5.1 Example: Netflix July 2013 Earnings Announcement
183(7)
5.6 Further Reading
190(1)
References
191(2)
6 Risk-Adjusted Portfolio Performance Measures 193(16)
6.1 Portfolio and Benchmark Data
193(4)
6.2 Sharpe Ratio
197(2)
6.3 Roy's Safety First Ratio
199(1)
6.4 Treynor Ratio
200(2)
6.5 Sortino Ratio
202(3)
6.6 Information Ratio
205(1)
6.7 Combining Results
206(2)
6.8 Further Reading
208(1)
References
208(1)
7 Markowitz Mean-Variance Optimization 209(32)
7.1 Two Assets the "Long Way"
209(6)
7.2 Two-Assets Using Quadratic Programming
215(9)
7.3 Multiple Assets Using Quadratic Programming
224(9)
7.4 Effect of Allowing Short Selling
233(7)
7.5 Further Reading
240(1)
References
240(1)
8 Fixed Income 241(62)
8.1 Economic Analysis
242(13)
8.1.1 Real GDP
242(4)
8.1.2 Unemployment Rate
246(4)
8.1.3 Inflation Rate
250(5)
8.2 US Treasuries
255(23)
8.2.1 Shape of the US Treasury Yield Curve
255(8)
8.2.2 Slope of the US Treasury Yield Curve
263(4)
8.2.3 Real Yields on US Treasuries
267(3)
8.2.4 Expected Inflation Rates
270(4)
8.2.5 Mean Reversion
274(4)
8.3 Investment Grade Bond Spreads
278(8)
8.3.1 Time Series of Spreads
278(2)
8.3.2 Spreads and Real GDP Growth
280(6)
8.4 Bond ETFs
286(3)
8.5 Bond Valuation on Coupon Payment Dates
289(5)
8.5.1 Pricing Vanilla Bonds with Known Yield-to-Maturity
289(2)
8.5.2 Vanilla Bond Pricing Function
291(2)
8.5.3 Finding Bond Yield-to-Maturity with Known Price
293(1)
8.6 Duration and Convexity
294(4)
8.7 Bond Valuation on Non-Coupon Payment Dates
298(4)
8.8 Further Reading
302(1)
References
302(1)
9 Options 303(30)
9.1 Obtaining Options Chain Data
304(7)
9.2 Black-Scholes-Merton Options Pricing Model
311(4)
9.3 Black-Scholes-Merton OPM Function
315(1)
9.4 Put-Call Parity
316(1)
9.5 The Greeks
317(1)
9.6 Implied Volatility
318(1)
9.7 Gauging Market Risk
319(3)
9.8 Binomial OPM
322(8)
9.8.1 The Long Way
326(2)
9.8.2 Binomial Model Function
328(2)
9.9 Further Reading
330(1)
References
331(2)
Appendix A Getting Started with R 333(10)
Appendix B Constructing a Hypothetical Portfolio 343(6)
Index 349
Clifford S. Ang, CFA is a Vice President at Compass Lexecon in Chicago.  He specializes in valuation, corporate finance, and damages, and has worked on hundreds of engagements involving companies across a broad spectrum of industries.  Ang has held teaching appointments at DePaul University, the University of the Philippines, and Ateneo de Manila University, where he has taught courses in investments, investment management, corporate finance, and international finance.  He is a CFA Charterholder and holds an MS in Finance from the University of the Philippines.  Ang also holds a BSBA majoring in finance and accounting from Washington University in St. Louis, where he subsequently completed doctoral coursework in finance, economics, and econometrics.  He also presented at the 2012 R in Finance Conference a method to estimate the market value of illiquid debt.