東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Derivative securities- Data processing." ]

切換: 標籤 | MARC模式 | ISBD

Essentials of Excel VBA, Python, and...

Lee, John.

FindBook

Google Book

Amazon

博客來

Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essentials of Excel VBA, Python, and R./ by John Lee ... [et al.].
其他題名:	Financial derivatives, risk management and machine learning
其他作者:	Lee, John.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xv, 523 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	Chapter 1. Introduction -- Chapter 2. Introduction to Excel Programming -- Chapter 3. Introduction to VBA Programming -- Chapter 4. Professional Techniques Used in Excel and Excel VBA Techniques -- Chapter 5. Decision Tree Approach for Binomial Option Pricing Model -- Chapter 6. Microsoft Excel Approach to Estimating Alternative Option Pricing Models -- Chapter 7. Alternative Methods to Estimate Implied Variances -- Chapter 8. Greek Letters and Portfolio Insurance -- Chapter 9. Portfolio Analysis and Option Strategies -- Chapter 10. Alternative Simulation Methods and Their Applications -- Chapter 11. Linear Models for Regression -- Chapter 12. Kernel Linear Model -- Chapter 13. Neural Networks and Deep Learning -- Chapter 14. Applications of Alternative Machine Learning Methods for Credit Card Default Forecasting -- Chapter 15. An Application of Deep Neural Networks for Predicting Credit Card Delinquencies -- Chapter 16. Binomial/Trinomial Tree Option Pricing Using Python -- Chapter 17. Financial Ratios and its Applications -- Chapter 18. Time Value Money Analysis -- Chapter 19. Capital Budgeting under Certainty and Uncertainty -- Chapter 20. Financial Planning and Forecasting -- Chapter 21. Hedge Ratios: Theory and Applications -- Chapter 22. Application of simultaneous equation in finance research: Methods and empirical results -- Chapter 23. Using R Program to Estimate Binomial Option Pricing Model and Black & Scholes Option Pricing Model.
Contained By:	Springer Nature eBook
標題:	Finance - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-14283-3
ISBN:	9783031142833

Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
Essentials of Excel VBA, Python, and R.Volume II,Financial derivatives, risk management and machine learning[electronic resource] /Financial derivatives, risk management and machine learningby John Lee ... [et al.]. - Second edition. - Cham :Springer International Publishing :2023. - xv, 523 p. :ill. (chiefly col.), digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Introduction to Excel Programming -- Chapter 3. Introduction to VBA Programming -- Chapter 4. Professional Techniques Used in Excel and Excel VBA Techniques -- Chapter 5. Decision Tree Approach for Binomial Option Pricing Model -- Chapter 6. Microsoft Excel Approach to Estimating Alternative Option Pricing Models -- Chapter 7. Alternative Methods to Estimate Implied Variances -- Chapter 8. Greek Letters and Portfolio Insurance -- Chapter 9. Portfolio Analysis and Option Strategies -- Chapter 10. Alternative Simulation Methods and Their Applications -- Chapter 11. Linear Models for Regression -- Chapter 12. Kernel Linear Model -- Chapter 13. Neural Networks and Deep Learning -- Chapter 14. Applications of Alternative Machine Learning Methods for Credit Card Default Forecasting -- Chapter 15. An Application of Deep Neural Networks for Predicting Credit Card Delinquencies -- Chapter 16. Binomial/Trinomial Tree Option Pricing Using Python -- Chapter 17. Financial Ratios and its Applications -- Chapter 18. Time Value Money Analysis -- Chapter 19. Capital Budgeting under Certainty and Uncertainty -- Chapter 20. Financial Planning and Forecasting -- Chapter 21. Hedge Ratios: Theory and Applications -- Chapter 22. Application of simultaneous equation in finance research: Methods and empirical results -- Chapter 23. Using R Program to Estimate Binomial Option Pricing Model and Black & Scholes Option Pricing Model.

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 second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. 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 first volume for dedicated content on financial statistics, and portfolio analysis.

ISBN: 9783031142833

Standard No.: 10.1007/978-3-031-14283-3doiSubjects--Uniform Titles:

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

657417
Finance
--Data processing.

LC Class. No.: HF1017

Dewey Class. No.: 330.015195

Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
LDR:03832nmm a2200373 a 4500 001 2317807
003 DE-He213
005 20230323212158.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031142833 $q (electronic bk.)
020 $a 9783031142826 $q (paper)
024 7 $a 10.1007/978-3-031-14283-3 $2 doi
035 $a 978-3-031-14283-3
040 $a GP $c GP
041 0 $a eng
050 4 $a HF1017
072 7 $a PBT $2 bicssc
072 7 $a K $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 330.015195 $2 23
090 $a HF1017 $b .E78 2023
245 0 0 $a Essentials of Excel VBA, Python, and R. $n Volume II, $p Financial derivatives, risk management and machine learning $h [electronic resource] / $c by John Lee ... [et al.].
246 3 0 $a Financial derivatives, risk management and machine learning
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xv, 523 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Chapter 1. Introduction -- Chapter 2. Introduction to Excel Programming -- Chapter 3. Introduction to VBA Programming -- Chapter 4. Professional Techniques Used in Excel and Excel VBA Techniques -- Chapter 5. Decision Tree Approach for Binomial Option Pricing Model -- Chapter 6. Microsoft Excel Approach to Estimating Alternative Option Pricing Models -- Chapter 7. Alternative Methods to Estimate Implied Variances -- Chapter 8. Greek Letters and Portfolio Insurance -- Chapter 9. Portfolio Analysis and Option Strategies -- Chapter 10. Alternative Simulation Methods and Their Applications -- Chapter 11. Linear Models for Regression -- Chapter 12. Kernel Linear Model -- Chapter 13. Neural Networks and Deep Learning -- Chapter 14. Applications of Alternative Machine Learning Methods for Credit Card Default Forecasting -- Chapter 15. An Application of Deep Neural Networks for Predicting Credit Card Delinquencies -- Chapter 16. Binomial/Trinomial Tree Option Pricing Using Python -- Chapter 17. Financial Ratios and its Applications -- Chapter 18. Time Value Money Analysis -- Chapter 19. Capital Budgeting under Certainty and Uncertainty -- Chapter 20. Financial Planning and Forecasting -- Chapter 21. Hedge Ratios: Theory and Applications -- Chapter 22. Application of simultaneous equation in finance research: Methods and empirical results -- Chapter 23. Using R Program to Estimate Binomial Option Pricing Model and Black & Scholes Option Pricing Model.
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 second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. 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 first volume for dedicated content on financial statistics, and portfolio analysis.
630 0 0 $a Microsoft Excel (Computer file) $3 543656 $3 543656
650 0 $a Finance $x Data processing. $3 657417
650 0 $a Finance $x Statistical methods. $3 578739
650 0 $a Python (Computer program language) $3 729789
650 0 $a R (Computer program language) $3 784593
650 0 $a Electronic spreadsheets $x Computer programs. $3 678510
650 0 $a Derivative securities $x Data processing. $3 749115
650 0 $a Risk management. $3 540477
650 0 $a Machine learning. $3 533906
650 1 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
650 2 4 $a Statistics and Computing. $3 3594429
650 2 4 $a Programming Language. $3 3538935
650 2 4 $a Business Mathematics. $3 2088824
650 2 4 $a Data Analysis and Big Data. $3 3538537
700 1 $a Lee, John. $3 920614
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-14283-3
950 $a Mathematics and Statistics (SpringerNature-11649)