Jean-Francois Collard

Hands-On Data Analysis in R for Finance (eBook, PDF)

• Format: PDF

Produktbeschreibung

The subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference.

• Assumes no prior knowledge of R
• The content has been tested in actual university classes
• Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more
• Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 414
• Erscheinungstermin: 16. November 2022
• Englisch
• ISBN-13: 9781000787313
• Artikelnr.: 65963390

Autorenporträt

Jean-Francois Collard holds a PhD in Computer Science from the University of Paris and a Habilitation from the University of Versailles, France. He was a scientist at the National Center for Scientific Research (CNRS) in Versailles then at HP Labs in Palo Alto, California, and an engineer at Intel in Santa Clara, California. He then had various quantitative roles at Citigroup, Santander and currently in the Investment Consulting Group of New York Life Investment Management. He is also an Adjunct Associate Professor at Baruch College in New York, USA.

Inhaltsangabe

1. Your Working Environment 2. Reading Data in R 3. Financial Data 4.
Introduction to R 5. Functions 6. Data Transformation 7. Merging Data Sets
8. Graphing Using Ggplot 9. Returns and Returns-based Statistics 10.
Portfolios 11. Modeling Returns and Simulations 12. Linear and Polynomial
Regression 13. Fixed Income 14. Principal Component Analysis 15. Options
16. Value at Risk 17. Time Series Analysis 18. Machine Learning 19.
Presenting the Results of Your Analyses 20. Appendix: Main Packages Seen in
this Book