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.
Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning
Title: Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning
Author(s): John Lee; Jow-Ran Chang; Lie-Jane Kao; Cheng-Few Lee
Publisher: Springer Nature