LEADER 03988nam 22005895 450 001 9910483320603321 005 20250610110234.0 010 $a3-030-37740-7 024 7 $a10.1007/978-3-030-37740-3 035 $a(CKB)4100000010770875 035 $a(DE-He213)978-3-030-37740-3 035 $a(MiAaPQ)EBC6147797 035 $a(PPN)243226543 035 $a(MiAaPQ)EBC6147770 035 $a(MiAaPQ)EBC29092669 035 $a(EXLCZ)994100000010770875 100 $a20200328d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuantitative Portfolio Management $ewith Applications in Python /$fby Pierre Brugière 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 205 p. 23 illus., 22 illus. in color.) 225 1 $aSpringer Texts in Business and Economics,$x2192-4341 311 08$a3-030-37739-3 327 $aReturns and the Gaussian Hypothesis -- Utility Functions and the Theory of Choice -- The Markowitz Framework -- Markowitz Without a Risk-Free Asset -- Markowitz with a Risk-Free Asset -- Performance and Diversification Indicators -- Risk Measures and Capital Allocation -- Factor Models -- Identification of the Factors -- Exercises and Problems. 330 $aThis self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages. The concepts of investment portfolios, self-financing portfolios and absence of arbitrage opportunities are extensively used and enable the translation of all the mathematical concepts in an easily interpretable way. All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data. This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Those who want to run the code will have to install Python on their pc, or alternatively can use Google Colab on the cloud. Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject. 410 0$aSpringer Texts in Business and Economics,$x2192-4341 606 $aSocial sciences?Mathematics 606 $aStatistics 606 $aApplication software 606 $aMathematics in Business, Economics and Finance 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aComputer and Information Systems Applications 615 0$aSocial sciences?Mathematics. 615 0$aStatistics. 615 0$aApplication software. 615 14$aMathematics in Business, Economics and Finance. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aComputer and Information Systems Applications. 676 $a332.6 700 $aBrugie?re$b Pierre$4aut$4http://id.loc.gov/vocabulary/relators/aut$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483320603321 996 $aQuantitative Portfolio Management$92135435 997 $aUNINA