Essentials of Excel VBA, Python, and R, 1st ed.
Langue : Anglais
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.
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.
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 President at 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.
Jow-Ran Chang is Professor and Department Chairperson of the Department of Quantitative Finance at National Tsing Hua University (Taiwan). He is the author of Financial Engineering and Computational Finance: A Matlab-based Introduction (2007). Dr. Chang's research focuses on asset pricing, risk management, financial management, and financial product design.
Lie-Jane Kao is a Professor and Dean of the college of Finance at Takming University of Science and Technology (Taiwan). Dr. Kao's research focuses on quantitative financial/risk modeling, machine learning in finance, blockchain and its application, and had published papers in relevant Journals, including Review of Derivatives Research, Economic Modelling, International Journal of Information Technology and Decision Making, International Review of Economics & Finance, etc.
Cheng-Few Lee is a Distinguished Professor of Finance at Rutgers Business School, Rutgers University and was chairperson of the Department of Finance from 1988–1995. 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
Utilizes sample data drawn from individual stocks, stock indices, options, and futures Offers applications in Python, R, and Excel VBA Provides pedagogy from a business perspective, connecting statistical concepts to a business context
Date de parution : 12-2023
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 295,39 €
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