001461880 000__ 05698cam\a2200745\i\4500
001461880 001__ 1461880
001461880 003__ OCoLC
001461880 005__ 20230503003415.0
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001461880 008__ 230330s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001461880 019__ $$a1374035312
001461880 020__ $$a9783031142833$$q(electronic bk.)
001461880 020__ $$a3031142837$$q(electronic bk.)
001461880 020__ $$z9783031142826
001461880 020__ $$z3031142829
001461880 0247_ $$a10.1007/978-3-031-14283-3$$2doi
001461880 035__ $$aSP(OCoLC)1374247494
001461880 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001461880 049__ $$aISEA
001461880 050_4 $$aHF1017
001461880 08204 $$a330.01/5195$$223/eng/20230330
001461880 1001_ $$aLee, John C.,$$eauthor.$$1https://isni.org/isni/0000000360534058
001461880 24510 $$aEssentials of Excel VBA, Python, and R.$$nVolume II,$$pFinancial derivatives, risk management and machine learning /$$cJohn Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee.
001461880 24630 $$aFinancial derivatives, risk management and machine learning
001461880 250__ $$aSecond edition.
001461880 264_1 $$aCham :$$bSpringer,$$c[2023]
001461880 264_4 $$c©2023
001461880 300__ $$a1 online resource (xv, 523 pages) :$$billustrations (chiefly color)
001461880 336__ $$atext$$btxt$$2rdacontent
001461880 337__ $$acomputer$$bc$$2rdamedia
001461880 338__ $$aonline resource$$bcr$$2rdacarrier
001461880 504__ $$aIncludes bibliographical references.
001461880 5050_ $$aChapter 1. Introduction -- Chapter 2. Introduction to Excel Programming -- Chapter 3. Introduction to VBA Programming -- Chapter 4. Professional Techniques Used in Excel and Excel VBA Techniques -- Chapter 5. Decision Tree Approach for Binomial Option Pricing Model -- Chapter 6. Microsoft Excel Approach to Estimating Alternative Option Pricing Models -- Chapter 7. Alternative Methods to Estimate Implied Variances -- Chapter 8. Greek Letters and Portfolio Insurance -- Chapter 9. Portfolio Analysis and Option Strategies -- Chapter 10. Alternative Simulation Methods and Their Applications -- Chapter 11. Linear Models for Regression -- Chapter 12. Kernel Linear Model -- Chapter 13. Neural Networks and Deep Learning -- Chapter 14. Applications of Alternative Machine Learning Methods for Credit Card Default Forecasting -- Chapter 15. An Application of Deep Neural Networks for Predicting Credit Card Delinquencies -- Chapter 16. Binomial/Trinomial Tree Option Pricing Using Python -- Chapter 17. Financial Ratios and its Applications -- Chapter 18. Time Value Money Analysis -- Chapter 19. Capital Budgeting under Certainty and Uncertainty -- Chapter 20. Financial Planning and Forecasting -- Chapter 21. Hedge Ratios: Theory and Applications -- Chapter 22. Application of simultaneous equation in finance research: Methods and empirical results -- Chapter 23. Using R Program to Estimate Binomial Option Pricing Model and Black & Scholes Option Pricing Model.
001461880 506__ $$aAccess limited to authorized users.
001461880 520__ $$aThis advanced textbook for business statistics teaches statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.
001461880 588__ $$aDescription based on print version record.
001461880 63000 $$aMicrosoft Excel (Computer file)
001461880 650_0 $$aFinance$$xData processing.
001461880 650_0 $$aFinance$$xStatistical methods.
001461880 650_0 $$aPython (Computer program language)
001461880 650_0 $$aR (Computer program language)
001461880 650_0 $$aElectronic spreadsheets$$xComputer programs.
001461880 650_0 $$aDerivative securities$$xData processing.
001461880 650_0 $$aRisk management.
001461880 650_0 $$aMachine learning.
001461880 655_0 $$aElectronic books.
001461880 7001_ $$aChang, Jow-Ran,$$eauthor.
001461880 7001_ $$aKao, Lie Jane,$$eauthor.
001461880 7001_ $$aLee, Cheng F.,$$eauthor.$$1https://isni.org/isni/0000000109639730
001461880 77608 $$iPrint version:$$aLee, John C.$$tEssentials of Excel VBA, Python, and R. Volume II, Financial derivatives, risk management and machine learning.$$bSecond edition.$$dCham : Springer, 2022$$z9783031142826$$w(OCoLC)1346950970
001461880 852__ $$bebk
001461880 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-14283-3$$zOnline Access$$91397441.1
001461880 909CO $$ooai:library.usi.edu:1461880$$pGLOBAL_SET
001461880 980__ $$aBIB
001461880 980__ $$aEBOOK
001461880 982__ $$aEbook
001461880 983__ $$aOnline
001461880 994__ $$a92$$bISE