Essentials of Excel VBA, Python, and R Volume II: Financial Derivatives, Risk Management and Machine Learning /
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,...
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| Autors principals: | , , , |
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| Autor corporatiu: | |
| Format: | Electrònic eBook |
| Idioma: | anglès |
| Publicat: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Edició: | 2nd ed. 2023. |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/978-3-031-14283-3 |
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| Sumari: | 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 andbusiness analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis. |
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| Descripció física: | 1 online resource (XV, 523 p. 548 illus.). 436 illus. in color. |
| ISBN: | 9783031142833 |
| DOI: | 10.1007/978-3-031-14283-3 |