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Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning: 2 - Tapa dura
Sinopsis
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.
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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 the past three decades. He has been a consultant to many prominent groups including, the American Insurance Group, the World Bank, the United Nations, The Marmon Group Inc., Wintek Corporation, and Polaris Financial Group.Professor Lee founded the Review of Quantitative Finance and Accounting (RQFA) in 1990 and the Review of Pacific Basin Financial Markets and Policies (RPBFMP) in 1998, and serves as managing editor for both journals. He was also a co-editor of the Financial Review (1985-1991) and the Quarterly Review of Economics and Finance (1987-1989).In the past 42 years, Dr. Lee has written numerous textbooks ranging in subject matters from financial management to corporate finance, security analysis and portfolio management to financial analysis, planning and forecasting, and business statistics. In addition, he edited five popular books, Encyclopedia of Finance (with Alice C. Lee), Handbook of Quantitative Finance and Risk Management (with Alice C. Lee and John Lee), Handbook of Financial Econometrics and Statistics, Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning, and Handbook of Investment Analysis, Portfolio Management, and Financial Derivatives. Dr. Lee has also published more than 250 articles in more than 20 different journals in finance, accounting, economics, statistics, and management. Professor Lee was ranked the most published finance professor worldwide during the period 1953-2008.Professor Lee was the intellectual force behind the creation of the new Masters of Quantitative Finance program at Rutgers University. This program began in 2001 and has been ranked as one of the top fifteen quantitative finance programs in the United States. Professor Lee started the Conference on Financial Economics and Accounting in 1989. This conference is a consortium of Rutgers University, New York University, Temple University, University of Maryland, Georgia State University, Tulane University, Indiana University, and University of Toronto. This conference is the most well-known conference in finance and accounting.
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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.
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Essentials of Excel VBA, Python, and R
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Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning
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Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning
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Essentials of Excel VBA, Python, and R
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Utilizes sample data drawn from individual stocks, stock indices, options, and futuresOffers applications in Python, R, and Excel VBAProvides pedagogy from a business perspective, connecting statistical concepts to a business context. Nº de ref. del artículo: 668447754
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Essentials of Excel VBA, Python, and R: Volume II: Financial Derivatives, Risk Management and Machine Learning
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Essentials of Excel VBA, Python, and R
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 540 pp. Englisch. Nº de ref. del artículo: 9783031142826
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Essentials of Excel VBA, Python, and R : Volume II: Financial Derivatives, Risk Management and Machine Learning
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783031142826
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Essentials of Excel VBA, Python, and R
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ISBN 10: 3031142829
ISBN 13: 9783031142826
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Buch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 540 pp. Englisch. Nº de ref. del artículo: 9783031142826
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