rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Essentials of Excel VBA, Python, and R
    sub title: Volume II: Financial Derivatives, Risk Management and Machine Learning
  name:
    name part: Lee, John.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Chang, Jow-Ran.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Kao, Lie-Jane.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Lee, Cheng-Few.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2023
    edition: 2nd ed. 2023.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XV, 523 pages 548 illustrations, 436 illustrations in color)
  abstract: 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.
  table of contents: 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 and Scholes Option Pricing Model.
  note: by John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee.
  subject:
    topic: Statistics
  subject:
    topic: Mathematical statistics-Data processing
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Business mathematics
  subject:
    topic: Quantitative research
  subject:
    topic: Statistics in Business, Management, Economics, Finance, Insurance
  subject:
    topic: Statistics and Computing
  subject:
    topic: Programming Language
  subject:
    topic: Business Mathematics
  subject:
    topic: Data Analysis and Big Data
  classification: QA276-280
  classification: 300.727
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9978-3-031-14283-3
  identifier: 9783031142833
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 230323
    record change date: 20240805184145.0
    record identifier: in00004656478