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Essentials of Excel VBA, Python, and R.. Volume I, . Financial statistics and portfolio analysis

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Essentials of Excel VBA, Python, and R./ by John Lee, Cheng-Few Lee.
其他題名:	Financial statistics and portfolio analysis
作者:	Lee, John C.
其他作者:	Lee, Cheng F.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xvi, 696 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	R (Computer program language) - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-031-14236-9
ISBN:	9783031142369

Essentials of Excel VBA, Python, and R.. Volume I, . Financial statistics and portfolio analysis
Lee, John C.

Essentials of Excel VBA, Python, and R.Volume I, Financial statistics and portfolio analysis[electronic resource] /Financial statistics and portfolio analysisby John Lee, Cheng-Few Lee. - Second edition. - Cham :Springer International Publishing :2022. - xvi, 696 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Data Collection, Presentation, and Yahoo Finance -- Chapter 3. Histograms and the Rate of Returns of JPM and JNJ -- Chapter 4. Numerical Summary Measures on Stock Rates of Return and Market Rates of Return -- Chapter 5. Probability Concepts and their Analysis -- Chapter 6. Discrete Random Variables and Probability Distributions -- Chapter 7. The Normal and Lognormal Distributions -- Chapter 8. Sampling Distributions and Central Limit Theorem -- Chapter 9. Other Continuous Distributions -- Chapter 10. Estimation -- Chapter 11. Hypothesis Testing -- Chapter 12. Analysis of Variance and Chi-Square Tests -- Chapter 13. Simple Linear Regression and the Correlation Coefficient -- Chapter 14. Simple Linear Regression and Correlation: Analyses and Applications -- Chapter 15. Multiple Linear Regression -- Chapter 16. Residual and Regression Assumption Analysis -- Chapter 17. Nonparametric Statistics -- Chapter 18. Time Series: Analysis, Model, and Forecasting -- Chapter 19. Index Numbers and Stock Market Indexes -- Chapter 20. Sampling Surveys: Methods and Applications -- Chapter 21. Statistical Decision Theory -- Chapter 22. Sources of Risks and their Determination -- Chapter 23. Risk-Aversion, Capital Asset Allocation, and Markowitz Portfolio Selection Model -- Chapter 24. Capital Asset Pricing Model and Beta Forecasting -- Chapter 25. Single-Index Models for Portfolio Selection -- Chapter 26. Sharpe Performance Measure and Treynor Performance Measure Approach to Portfolio Analysis.

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.

ISBN: 9783031142369

Standard No.: 10.1007/978-3-031-14236-9doiSubjects--Uniform Titles:

Microsoft Excel (Computer file)
Subjects--Topical Terms:

1408254
R (Computer program language)
--Statistical methods.

LC Class. No.: HG173

Dewey Class. No.: 332.0285

Essentials of Excel VBA, Python, and R.. Volume I, . Financial statistics and portfolio analysis
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520 $a 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.
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