Advanced Portfolio Optimization A Cutting-edge Quantitative Approach /

This 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 quantitati...

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Detaylı Bibliyografya
Yazar:	Cajas, Dany (Yazar)
Müşterek Yazar:	SpringerLink (Online service)
Materyal Türü:	Elektronik Ekitap
Dil:	İngilizce
Baskı/Yayın Bilgisi:	Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edisyon:	1st ed. 2025.
Konular:	Statistics . Data mining. Machine learning. Valuation. Financial risk management. Statistics in Business, Management, Economics, Finance, Insurance. Data Mining and Knowledge Discovery. Machine Learning. Investment Appraisal. Risk Management.
Online Erişim:	https://doi.org/10.1007/978-3-031-84304-4
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

İçindekiler:

Chapter 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.

Advanced Portfolio Optimization A Cutting-edge Quantitative Approach /

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