LEADER 05190nam 22008175 450 001 9910999679103321 005 20251202150009.0 010 $a9783031843044 010 $a3031843045 024 7 $a10.1007/978-3-031-84304-4 035 $a(CKB)38485083600041 035 $a(DE-He213)978-3-031-84304-4 035 $a(MiAaPQ)EBC32011951 035 $a(Au-PeEL)EBL32011951 035 $a(EXLCZ)9938485083600041 100 $a20250417d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Portfolio Optimization $eA Cutting-edge Quantitative Approach /$fby Dany Cajas 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XV, 503 p. 216 illus., 186 illus. in color.) 311 08$a9783031843037 311 08$a3031843037 327 $aChapter 1 Introduction -- Chapter 2 Why use Python? -- Part I Parameter Estimation -- Chapter 3 Sample Based Methods -- Chapter 4 Risk Factors Models -- Chapter 5 Black Litterman Models -- Chapter 7 Convex Risk Measures -- Chapter 8 Return-Risk Trade-Off Optimization -- Chapter 9 Real Features Constraints -- Chapter 10 Risk Parity Optimization -- Chapter 11 Robust Optimization -- Part III Machine Learning Portfolio Optimization -- Chapter 12 Hierarchical Clustering Portfolios -- Chapter 13 Graph Theory Based Portfolios -- Part IV Backtesting -- Chapter 14 Generation of Synthetic Data -- Chapter 15 Backtesting Process -- Part V Appendix -- Chapter A Linear Algebra -- Chapter B Convex Optimization -- Chapter C Mixed Integer Programming. 330 $aThis book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions. Portfolio Optimization goes beyond traditional portfolio theory (Quadratic Programming), incorporating last advances in convex optimization techniques and cutting-edge machine learning algorithms. It extensively addresses risk management and uncertainty quantification, teaching readers how to measure and minimize various forms of risk in their portfolios. This book goes beyond traditional back testing methodologies based on historical data for investment portfolios, incorporating tools to create synthetic datasets and robust methodologies to identify better investment strategies considering real aspects like transaction costs. The author provides several methodologies for estimating the input parameters of investment portfolio optimization models, from classical statistics to more advanced models, such as graph-based estimators and Bayesian estimators, provide a deep understanding of advanced convex optimization models and machine learning algorithms for building investment portfolios and the necessary tools to design the back testing of investment portfolios using several methodologies based on historical and synthetic datasets that allow readers identify the better investment strategies. 606 $aStatistics 606 $aData mining 606 $aMachine learning 606 $aValuation 606 $aFinancial risk management 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aData Mining and Knowledge Discovery 606 $aMachine Learning 606 $aInvestment Appraisal 606 $aRisk Management 606 $aEstadística$2thub 606 $aMineria de dades$2thub 606 $aAprenentatge automàtic$2thub 606 $aValoració$2thub 606 $aGestió del risc$2thub 606 $aEstadística econòmica$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aData mining. 615 0$aMachine learning. 615 0$aValuation. 615 0$aFinancial risk management. 615 14$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 615 24$aInvestment Appraisal. 615 24$aRisk Management. 615 7$aEstadística 615 7$aMineria de dades 615 7$aAprenentatge automàtic 615 7$aValoració 615 7$aGestió del risc 615 7$aEstadística econòmica 676 $a300.727 700 $aCajas$b Dany$4aut$4http://id.loc.gov/vocabulary/relators/aut$01817231 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910999679103321 996 $aAdvanced Portfolio Optimization$94374791 997 $aUNINA