Essentials of Excel VBA, Python, and R

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.

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

Autorentext

John M. Lee is a professor of mathematics at the University of Washington. His previous Springer textbooks in the Graduate Texts in Mathematics series include the first edition of Introduction to Topological Manifolds, Introduction to Smooth Manifolds, and Riemannian Manifolds: An Introduction.

Inhalt
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.