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E-raamat: Essentials of Excel VBA, Python, and R: Volume I: Financial Statistics and Portfolio Analysis

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 02-Jan-2023
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783031142369
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Essentials of Excel VBA, Python, and R: Volume I: Financial Statistics and Portfolio AnalysisSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 02-Jan-2023
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783031142369
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This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry.

This first volume is designed for advanced courses in financial statistics, investment analysis and portfolio management. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the second volume for dedicated content on financial derivatives, risk management, and machine learning.
1 Introduction
1(18)
1.1 Introduction
1(1)
1.2 Microsoft Excel 2019 Versus Microsoft Excel 365
2(1)
1.3 Power Query
2(1)
1.4 Microsoft Excel and Power Query
3(1)
1.5 Microsoft Excel 64-Bit Versus Microsoft Excel 32-Bit
3(2)
1.5.1 Microsoft Excel 64-Bit and Power Query
5(1)
1.6 Statistical Environment of Microsoft Excel 365
5(3)
1.7 Python Programming Language
8(2)
1.7.1 Python Libraries for Statistics
9(1)
1.7.2 Python Development Environment
9(1)
1.8 R Programming Language
10(1)
1.9 Web Scraping for Market and Financial Data
11(3)
1.9.1 Microsoft Excel Power Query
11(3)
1.10 Case Study, Google Study, and Active Study Approach
14(1)
1.11 Structure of the Book
15(1)
Bibliography
15(4)
Part I Financial Statistics
2 Data Collection, Presentation, and Yahoo! Finance
19(62)
2.1 Introduction
19(1)
2.2 Data Presentation
19(1)
2.3 Yahoo! Finance
19(1)
2.4 Market Indexes
20(1)
2.4.1 Dow Jones Industrial Average
20(1)
2.4.2 S&P 500
20(1)
2.4.3 NASDAQ
20(1)
2.5 JSON Data Format
21(4)
2.5.1 Quandl Data Provider
21(4)
2.6 Ticker Attributes
25(19)
2.6.1 Yahoo! Finance API
25(19)
2.7 Historical Data
44(23)
2.7.1 Yahoo! Finance API
44(1)
2.7.2 Epoch Time
45(1)
2.7.3 Power Query
46(19)
2.7.4 Python
65(2)
2.8 Charting Historical Data
67(11)
2.8.1 Microsoft Excel 365 Chart Wizard
67(6)
2.8.2 Power Query M Code
73(5)
2.9 Using Python to Graph Johnson & Johnson's Historical Prices
78(2)
2.10 Summary
80(1)
Bibliography
80(1)
3 Histograms, Rate of Returns, and Financial Statements
81(52)
3.1 Introduction
81(1)
3.2 Rate of Return
81(23)
3.2.1 Power Query
81(17)
3.2.2 Dynamic Power Query
98(4)
3.2.3 Python
102(2)
3.3 Histograms
104(7)
3.3.1 Sturge's Rule
104(1)
3.3.2 Microsoft Excel
105(6)
3.4 Using Python to Create Johnson & Johnson's Rate of Return Histogram
111(3)
3.4.1 Code
111(2)
3.4.2 Output
113(1)
3.5 Financial Statements
114(18)
3.5.1 Power Query
114(15)
3.5.2 Python
129(3)
3.6 Summary
132(1)
Bibliography
132(1)
4 Numerical Summary Measures on Rate of Returns of Stocks and Market Indexes
133(36)
4.1 Introduction
133(10)
4.1.1 Summary Measures Excel Workbook
133(1)
4.1.2 Summary Worksheet
133(10)
4.2 Measure of Central Tendency
143(1)
4.2.1 Arithmetic Mean (Average)
143(1)
4.2.2 Annualized Monthly Returns
143(1)
4.2.3 Median
143(1)
4.2.4 Excel Functions
143(1)
4.3 Measure of Dispersion
144(3)
4.3.1 Variance
144(1)
4.3.2 Annualized Monthly Variance
145(1)
4.3.3 Standard Deviation
145(1)
4.3.4 Annualized Monthly Standard Deviation
145(1)
4.3.5 Coefficient of Variation
146(1)
4.3.6 Excel Functions
146(1)
4.4 Measure of Relative Position
147(1)
4.4.1 Quartiles
147(1)
4.4.2 Interquartile
147(1)
4.4.3 Outliers
147(1)
4.4.4 Z-Score
147(1)
4.4.5 Excel Functions
147(1)
4.5 Measure of Shape
148(2)
4.5.1 Skewness
148(1)
4.5.2 Kurtosis
149(1)
4.5.3 Excel Functions
149(1)
4.6 Measure of Linear Relationship
150(1)
4.6.1 Coefficient of Correlation
150(1)
4.6.2 Excel Functions
150(1)
4.7 Box and Whisker Plot
151(1)
4.7.1 Outliers
151(1)
4.7.2 Extreme Outliers
152(1)
4.7.3 Vertical Whiskers
152(1)
4.7.4 Median and Mean
152(1)
4.8 Excel Rate of Return Box and Whisker Workbook
152(3)
4.8.1 Summary Worksheet
152(1)
4.8.2 Ticker1, Ticker2, Ticker3 Worksheets
153(1)
4.8.3 Ticker123 Worksheet
154(1)
4.9 Creating Box and Whisker Plot in Excel
155(8)
4.9.1 Single Box and Whisker Plot
155(2)
4.9.2 Combined Box and Whisker Plot
157(6)
4.10 Using Python to Calculate the 5-Year Numerical Measures of the Rate of Return of AAPL, MSFT, and the S&P 500
163(4)
4.11 Summary
167(1)
Bibliography
167(2)
5 Probability Concepts and Their Analysis
169(28)
5.1 Introduction
169(1)
5.2 Data Presentation
169(1)
5.3 Probability
169(19)
5.3.1 Probability Simulation with Excel VBA
170(10)
5.3.2 Probability Simulation in R
180(4)
5.3.3 Probability Simulation in Python
184(4)
5.4 Combinations
188(3)
5.4.1 Combination List with Excel VBA
189(2)
5.4.2 Combination List with R
191(1)
5.5 Permutations
191(4)
5.5.1 Permutation List with Excel VBA
192(2)
5.5.2 Permutation List with R
194(1)
5.6 Summary
195(1)
Bibliography
195(2)
6 Discrete Random Variables and Probability Distributions
197(34)
6.1 Introduction and Probability Distribution
197(1)
6.2 Cumulative Probability Distribution
198(4)
6.3 Binomial Distribution
202(9)
6.3.1 Binomial Distribution in Excel
203(4)
6.3.2 Binomial Distribution in R
207(3)
6.3.3 Binomial Distribution in Python
210(1)
6.4 Poisson Random Variable
211(7)
6.4.1 Poisson Distribution in Excel
212(3)
6.4.2 Poisson Distribution in R
215(2)
6.4.3 Poisson Distribution in Python
217(1)
6.5 Excel 4.0 Macro Functions and Excel Names
218(1)
6.6 Examples
219(5)
6.6.1 Files Function
219(5)
6.7 Stephen Bullen's Charting Method
224(6)
6.7.1 Binomial Distribution
228(1)
6.7.2 Poisson Distribution
229(1)
6.8 Summary
230(1)
Bibliography
230(1)
7 Normal and Lognormal Distributions
231(24)
7.1 Introduction
231(1)
7.2 Uniform Distribution
231(3)
7.2.1 Uniform Distribution in R
231(3)
7.3 Normal Distribution
234(5)
7.3.1 Normal Distribution in R
234(3)
7.3.2 Normal Distribution in Python
237(2)
7.4 Standard Normal Distribution
239(4)
7.4.1 Standard Normal Distribution in R
239(1)
7.4.2 Standard Normal Distribution in Excel
240(3)
7.5 Lognormal Distribution
243(4)
7.5.1 Lognormal Distribution in R
243(3)
7.5.2 Lognormal Distribution in Python
246(1)
7.6 Normal Quantile-Quantile (QQ) Plot in Excel
247(3)
7.7 Normal Quantile-Quantile (QQ) Plot in Python
250(3)
7.8 Summary
253(1)
Bibliography
254(1)
8 Sampling Distributions and Central Limit Theorem
255(24)
8.1 Introduction
255(1)
8.2 Sample Distribution in Excel
255(6)
8.3 Mean of Sample Distribution Equals Mean of Population
261(5)
8.4 Sample Distribution in Python
266(1)
8.5 Central Limit Theorem
267(10)
8.5.1 Uniform Distribution in R
268(2)
8.5.2 Normal Distribution in R
270(2)
8.5.3 Lognormal Distribution in R
272(1)
8.5.4 Binomial Distribution in R
273(2)
8.5.5 Poisson Distribution in R
275(2)
8.6 Summary
277(1)
Bibliography
277(2)
9 Other Continuous Distributions
279(24)
9.1 Introduction
279(1)
9.2 T-Distribution
279(5)
9.2.1 T-Distribution in R
279(1)
9.2.2 T-Distribution in Python
280(1)
9.2.3 Student's t-Distribution in Excel
281(3)
9.3 Chi-Square (x2) Distribution
284(6)
9.3.1 Chi-Square (x2) Distribution in R
285(1)
9.3.2 Chi-Square (x2) Distribution in Python
286(1)
9.3.3 Chi-Square (x2) Distribution in Excel
287(3)
9.4 F-Distribution
290(6)
9.4.1 F-Distribution in R
290(1)
9.4.2 F-Distribution in Python
291(1)
9.4.3 F-Distribution in Excel
292(4)
9.5 Exponential Distribution
296(5)
9.5.1 Exponential Probability Density Function in Excel
296(4)
9.5.2 Exponential Cumulative Density Function in Excel
300(1)
9.6 Summary
301(1)
Bibliography
301(2)
10 Estimation
303(14)
10.1 Introduction
303(1)
10.2 Confidence Interval Simulation in Python
304(4)
10.2.1 Python Code
305(2)
10.2.2 Confidence Interval Simulation Data
307(1)
10.3 Interval Estimates for μ When σ2 is Known
308(3)
10.3.1 Z Confidence Intervals
308(3)
10.4 Confidence Intervals for μ When σ2 is Unknown
311(1)
10.4.1 T Confidence Intervals
311(1)
10.5 Confidence Intervals for the Population Proportion
312(3)
10.5.1 Example G
312(1)
10.5.2 Example H
313(1)
10.5.3 Example I
314(1)
10.5.4 Example J
314(1)
10.6 Confidence Intervals for the Variance
315(1)
10.6.1 Example K
315(1)
10.7 Summary
316(1)
Bibliography
316(1)
11 Hypothesis Testing
317(14)
11.1 Introduction
317(1)
11.2 One-Tailed Tests of Mean for Large Samples
317(1)
11.2.1 Example 11.1
318(1)
11.3 Z-Test
318(1)
11.4 Hypothesis Testing and the p-Value
319(1)
11.4.1 Example 11.2
319(1)
11.5 One-Tailed Tests of Mean for Large Samples: Two-Sample Test of Means
320(2)
11.5.1 Example 11.3
320(2)
11.6 Two-Tailed Tests of Mean for Large Samples
322(2)
11.6.1 Example 11.4
322(1)
11.6.2 Example 11.5
323(1)
11.7 One-Tailed Tests of Mean for Small Samples
324(1)
11.7.1 Example 11.6
324(1)
11.8 Hypothesis Testing for a Population Proportion
325(1)
11.8.1 Example 11.7
325(1)
11.9 The Power of a Test and Power Function
326(1)
11.9.1 Example 11.8
326(1)
11.10 Power and Sample Size
327(1)
11.11 Power and Alpha Size
327(1)
11.12 Comparing the Average EPS of AAPL and MSFT in Python
328(2)
11.13 Summary
330(1)
Bibliography
330(1)
12 Analysis of Variance and Chi-Square Tests
331(22)
12.1 Introduction
331(2)
12.2 One-Way Analysis of Variance
333(11)
12.2.1 Example 12.1
333(8)
12.2.2 Example 12.2
341(3)
12.3 Two-Way Analysis of Variance
344(3)
12.3.1 Example 12.3
344(3)
12.4 Chi-Square Test
347(1)
12.5 Goodness of Fit
347(2)
12.5.1 Example 12.4
348(1)
12.6 Test of Independence
349(1)
12.6.1 Example 12.5
349(1)
12.7 Using the Chi-Square Test and Python to Determine if the Rate of Return of Apple Inc. Is a Normal Distribution
350(2)
12.8 Summary
352(1)
Bibliography
352(1)
13 Simple Linear Regression and the Correlation Coefficient
353(26)
13.1 Introduction
353(1)
13.2 Regression Analysis
353(3)
13.3 Retrieving Data Using Power Query
356(3)
13.4 Combining Power Query Data Sets
359(2)
13.5 Scatter Chart
361(3)
13.6 Deterministic Relationship and Stochastic Relationship
364(1)
13.7 Least Square Method
365(1)
13.8 Standard Assumptions for Linear Regression
365(1)
13.9 Standard Error of Estimate
366(1)
13.10 The Coefficient of Determination
367(1)
13.11 Correlation Coefficient
368(2)
13.12 Regression Analysis in Excel
370(4)
13.12.1 Correlation and Coefficient of Determination
372(1)
13.12.2 Regression Line
373(1)
13.12.3 Residuals of the Regression Line
373(1)
13.12.4 Fit Plot of the Data Set
374(1)
13.13 INTERCEPT and SLOPE Excel Functions
374(1)
13.14 Oil and Gasoline Regression Analysis in Python
375(3)
13.15 Summary
378(1)
Bibliography
378(1)
14 Simple Linear Regression and Correlation: Analyses and Applications
379(32)
14.1 Introduction
379(1)
14.2 Standard Error of Estimate
380(1)
14.3 Two-Tailed t-Test for β
380(2)
14.4 Two-Tailed t-Test for α
382(2)
14.5 Confidence Interval of β
384(1)
14.6 F Test
384(1)
14.7 The Relationship Between the F-Test and the t-Test
385(1)
14.8 Market Model
386(1)
14.9 Yahoo! Finance Beta Screener
386(1)
14.10 Historical Monthly Data from Yahoo! Finance
386(3)
14.10.1 Excel's Import Text Wizard
387(2)
14.11 Market Model of Apple Inc. in Excel
389(16)
14.11.1 Data Analysis and Regression Report
390(2)
14.11.2 Yahoo! Finance Beta and Power Query
392(1)
14.11.3 Yahoo! Finance Ticker Historical Data
393(1)
14.11.4 Yahoo! Finance S&P500 Historical Data
394(1)
14.11.5 Calculating Rate of Return
394(2)
14.11.6 Date, Time, and Epoch Time
396(5)
14.11.7 Converting to and from Epoch Time
401(1)
14.11.8 Other Power Queries
402(3)
14.12 Market Model of the Clorox Company in Excel
405(3)
14.12.1 Regression Report
405(1)
14.12.2 Yahoo! Finance Beta and the Market Model
406(1)
14.12.3 Sectors and Industry
407(1)
14.13 Market Model in Python
408(2)
14.14 Summary
410(1)
Bibliography
410(1)
15 Multiple Linear Regression
411(24)
15.1 Introduction
411(2)
15.2 R-Square
413(1)
15.3 F-Test
414(1)
15.4 Confidence Interval of B
415(1)
15.5 F-Test
415(1)
15.6 Analyzing the Determination of Price Per Share
416(11)
15.6.1 Regression Analysis
416(3)
15.6.2 Workbook Sources
419(2)
15.6.3 Data Source
421(6)
15.7 Power Query Resource Issue
427(1)
15.8 Excel 365 and OneDrive
428(3)
15.9 Using R to Predict
431(1)
15.10 Summary
432(1)
Bibliography
433(2)
16 Residual and Regression Assumption Analysis
435(30)
16.1 Introduction
435(1)
16.2 Regression Analysis
435(3)
16.3 Linearity
438(3)
16.4 The Expected Value of the Residual Term is Zero
441(2)
16.5 The Variance of the Error Term is Constant
443(7)
16.6 Autocorrelation Durbin-Watson Test
450(3)
16.6.1 VBA Code
450(2)
16.6.2 Durbin-Watson 1% Table
452(1)
16.7 Autocorrelation Walmart's Dividend and EPS from 2019 to 2000
453(2)
16.7.1 Data Source
454(1)
16.8 Using VBA to Retrieve a Ticker's Name
455(3)
16.9 Durbin-Watson Test Market Model Python Code
458(1)
16.10 The Independent Variables Are Uncorrelated: Multicollinearity
459(2)
16.11 Variance Inflationary Factor (VIF)
461(2)
16.12 Summary
463(1)
Bibliography
463(2)
17 Nonparametric Statistics
465(16)
17.1 Introduction
465(1)
17.2 Mann-Whitney U Test
465(3)
17.2.1 Calculation in Microsoft Excel
466(2)
17.2.2 Calculation in R
468(1)
17.3 Kruskal-Wallis Test
468(4)
17.3.1 Calculation in Microsoft Excel
469(1)
17.3.2 Calculation in R
470(1)
17.3.3 Calculation in Python
471(1)
17.4 Spearman's Rank Correlation Test
472(4)
17.4.1 Calculation in R
474(1)
17.4.2 Calculation in Python
475(1)
17.5 Using Python to Test the Randomness of the Rate of Return of JNJ
476(2)
17.6 Using Python to Test the Randomness of the Rate of Return of MSFT
478(1)
17.7 Summary
479(1)
Bibliography
479(2)
18 Time Series: Analysis, Model, and Forecasting
481(32)
18.1 Introduction
481(1)
18.2 Moving Averages
481(12)
18.2.1 Moving Averages in Excel
482(8)
18.2.2 Moving Averages in R
490(1)
18.2.3 Moving Averages in Python
491(1)
18.2.4 Data Source
492(1)
18.3 Linear Trend
493(14)
18.3.1 Linear Trend Analysis in Excel
493(6)
18.3.2 Linear Trend Analysis in R
499(3)
18.3.3 Linear Trend Analysis in Python
502(5)
18.4 Exponential Smoothing
507(4)
18.4.1 Exponential Smoothing in Excel
508(2)
18.4.2 Exponential Smoothing in Python
510(1)
18.5 Summary
511(1)
Bibliography
511(2)
19 Index Numbers and Stock Market Indexes
513(24)
19.1 Introduction
513(1)
19.2 Simple Price Index
513(5)
19.2.1 Example 19.1
513(5)
19.3 Laspeyres Price Index
518(1)
19.4 Paasche Price Index
519(1)
19.5 Fisher's Ideal Price Index
520(1)
19.6 Stock Indexes: S&P500 Index and NASDAQ Composite Index
521(1)
19.7 Stock Indexes: Dow Jones Industrial Average (DJIA)
522(2)
19.8 Components of the Dow Jones Industrial Average (DJIA)
524(2)
19.8.1 Using Power Query to Retrieve the Dow 30 Components
525(1)
19.9 Components of the S&P 500 Index
526(4)
19.9.1 Using Power Query to Retrieve the S&P 500 Components
526(4)
19.10 Components of the NASDAQ Composite Index
530(3)
19.10.1 Using Power Query to Retrieve the NASDAQ Composite Components
533(1)
19.11 Using Python to Calculate the Four Statistical Moments of the Rate of Returns of Every Component in the S&P 500
533(3)
19.12 Summary
536(1)
Bibliography
536(1)
20 Sampling Surveys: Methods and Applications
537(6)
20.1 Introduction
537(1)
20.2 Random Number Tables
537(1)
20.2.1 Excel VBA
537(1)
20.2.2 Python Code
538(1)
20.3 Confidence Interval for the Population Mean
538(2)
20.3.1 Example 20.1
538(2)
20.4 Confidence Interval for the Population Proportion
540(1)
20.4.1 Example 20.2
540(1)
20.5 Determining Sample Size
541(1)
20.5.1 Example 20.3
541(1)
20.6 Summary
541(1)
Bibliography
542(1)
21 Statistical Decision Theory
543(16)
21.1 Introduction
543(1)
21.2 Decision Trees and Expected Monetary Values
543(1)
21.3 NPV and IRR Method for Capital Budgeting Decision Under Certainty
543(4)
21.4 The Statistical Distribution Method
547(8)
21.4.1 Methodology
547(5)
21.4.2 Excel and VBA Application
552(3)
21.5 Summary
555(1)
Bibliography
556(3)
Part II Portfolio Analysis
22 Risk Classification, Estimation, and Diversification
559(28)
22.1 Introduction
559(1)
22.2 Risk Classification
559(3)
22.2.1 Business Risk
559(2)
22.2.2 Financial Risk
561(1)
22.2.3 Total Risk
561(1)
22.3 Portfolio Analysis and Application
562(6)
22.3.1 Expected Rate of Return on a Portfolio
562(2)
22.3.2 The Two-Asset Case
564(1)
22.3.3 The N-asset Case
565(1)
22.3.4 The Efficient Portfolios
565(3)
22.3.5 Corporate Application of Diversification
568(1)
22.4 Determination of Commercial Lending Rates
568(2)
22.5 The Dominance Principle and Performance EVALUATION
570(2)
22.6 Summary
572(14)
Bibliography
586(1)
23 Asset Allocation and Markowitz Portfolio-Selection Model
587(30)
23.1 Introduction
587(1)
23.2 Utility Theory, Utility Functions, and Indifference Curves
587(8)
23.2.1 Utility Theory
588(1)
23.2.2 Utility Functions
588(5)
23.2.3 Risk Aversion and Asset Allocation
593(1)
23.2.4 Indifference Curves
594(1)
23.3 Efficient Portfolios
595(4)
23.3.1 Portfolio Combinations
596(1)
23.3.2 Short Selling
596(3)
23.4 Techniques for Calculating the Efficient Frontier with Short Selling
599(6)
23.4.1 The Normal Distribution
599(1)
23.4.2 The Log-Normal Distribution
600(1)
23.4.3 Mathematical Method to Calculate Efficient Frontier
601(2)
23.4.4 Portfolio Determination with Specific Adjustment for Short Selling
603(2)
23.4.5 Portfolio Determination Without Short Selling
605(1)
23.5 Summary
605(10)
Bibliogrphy
615(2)
24 Capm, Beta Estimation, and Forecasting
617(26)
24.1 Introduction
617(1)
24.2 A Graphical Approach to the Derivation of the Capm
617(5)
24.2.1 The Lending, Borrowing, and Market Portfolios
617(1)
24.2.2 The Capital Market Line
618(2)
24.2.3 The Security Market Line--The Capital Asset Pricing Model
620(2)
24.3 Mathematical Approach to the Derivation of the Capm
622(1)
24.4 The Market Model and Risk Decomposition
623(3)
24.4.1 The Market Model
623(1)
24.4.2 Risk Decomposition
623(1)
24.4.3 Why Beta is Important for Security Analysis
624(1)
24.4.4 Determination of Systematic Risk
625(1)
24.5 Growth Rates, Accounting Betas, and Variance in Ebit
626(7)
24.5.1 Sustainable Growth Rates
626(2)
24.5.2 Accounting Beta
628(1)
24.5.3 Variance in EBIT
628(1)
24.5.4 Capital-Labor Ratio
628(1)
24.5.5 Fixed Costs and Variable Costs
629(1)
24.5.6 Beta Forecasting
629(1)
24.5.7 Market-Based Versus Accounting-Based Beta Forecasting
630(3)
24.6 Some Applications and Implications of the Capm
633(1)
24.7 Summary
634(6)
Bibliography
640(3)
25 Portfolio Selection Methods: Theory and Application
643(28)
25.1 Introduction
643(1)
25.2 The Single-Index Model
643(10)
25.2.1 Deriving the Single-Index Model
645(3)
25.2.2 Portfolio Analysis and the Single-Index Model <S2>
648(4)
25.2.3 The Market Model and Beta
652(1)
25.3 Multiple Indexes and the Multiple-Index Model
653(3)
25.4 Summary
656(12)
Bibliography
668(3)
26 Investment Performance Approach to Portfolio Selection
671
26.1 Introduction
671(1)
26.2 Sharpe Performance-Measure Approach with Short Sales Allowed
671(6)
26.3 Sharpe Performance-Measure Approach with Short Sales and Upper Bound Constraints
677(2)
26.4 Treynor-Measure Approach with Short Sales Allowed
679(2)
26.5 Treynor-Measure Approach with Short Sales not Allowed
681(3)
26.6 Impact of Short Sales on Optimal-Weight Determination
684(1)
26.7 Economic Rationale of the Treynor Performance-Measure Method
684(1)
26.8 Summary
685(10)
Bibliography
695
Cheng-Few Lee is a Distinguished Professor of Finance at Rutgers Business School, Rutgers University and was chairperson of the Department of Finance from 19881995. He has also served on the faculty of the University of Illinois (IBE Professor of Finance) and the University of Georgia. He has maintained academic and consulting ties in Taiwan, Hong Kong, China and the United States for the past three decades. He has been a consultant to many prominent groups including, the American Insurance Group, the World Bank, the United Nations, The Marmon Group Inc., Wintek Corporation, and Polaris Financial Group.Professor Lee founded the Review of Quantitative Finance and Accounting (RQFA) in 1990 and the Review of Pacific Basin Financial Markets and Policies (RPBFMP) in 1998, and serves as managing editor for both journals. He was also a co-editor of the Financial Review (1985-1991) and the Quarterly Review of Economics and Finance (1987-1989).In thepast 42 years, Dr. Lee has written numerous textbooks ranging in subject matters from financial management to corporate finance, security analysis and portfolio management to financial analysis, planning and forecasting, and business statistics. In addition, he edited five popular books, Encyclopedia of Finance (with Alice C. Lee), Handbook of Quantitative Finance and Risk Management (with Alice C. Lee and John Lee), Handbook of Financial Econometrics and Statistics, Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning, and Handbook of Investment Analysis, Portfolio Management, and Financial Derivatives. Dr. Lee has also published more than 250 articles in more than 20 different journals in finance, accounting, economics, statistics, and management. Professor Lee was ranked the most published finance professor worldwide during the period 1953-2008.Professor Lee was the intellectual force behind the creation of the new Masters of Quantitative Finance program at Rutgers University. This program began in 2001 and has been ranked as one of the top fifteen quantitative finance programs in the United States. Professor Lee started the Conference on Financial Economics and Accounting in 1989. This conference is a consortium of Rutgers University, New York University, Temple University, University of Maryland, Georgia State University, Tulane University, Indiana University, and University of Toronto. This conference is the most well-known conference in finance and accounting. John C. Lee is Director of the Center for PBBEF Research. A Microsoft Certified Professional in Microsoft Visual Basic and Microsoft Excel VBA, Mr. Lee has worked over 20 years in both the business and technical fields as an accountant, auditor, systems analyst, as well as a business software developer. Formerly, the Senior Technology Officer at the Chase Manhattan Bank and Assistant Vice Presidentat Merrill Lynch, he is also the author of Business and Financial Statistics Using Minitab 12 and Microsoft Excel 97, as well as Financial Analysis, Planning and Forecasting with Cheng-Few Lee and Alice Lee.
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