Quantitative Portfolio Optimization: Advanced Techniques and Applications
暫譯: 量化投資組合優化：進階技術與應用
Noguer Alonso, Miquel, Antolin Camarena, Julian, Bueno Guerrero, Alberto
商品描述
Expert guidance on implementing quantitative portfolio optimization techniques
In Quantitative Portfolio Optimization: Theory and Practice, renowned financial practitioner Miquel Noguer, alongside physicists Alberto Bueno Guerrero and Julian Antolin Camarena, who possess excellent knowledge in finance, delve into advanced mathematical techniques for portfolio optimization. The book covers a range of topics including mean-variance optimization, the Black-Litterman Model, risk parity and hierarchical risk parity, factor investing, methods based on moments, and robust optimization as well as machine learning and reinforcement technique. These techniques enable readers to develop a systematic, objective, and repeatable approach to investment decision-making, particularly in complex financial markets.
Readers will gain insights into the associated mathematical models, statistical analyses, and computational algorithms for each method, allowing them to put these techniques into practice and identify the best possible mix of assets to maximize returns while minimizing risk. Topics explored in this book include:
- Specific drivers of return across asset classes
- Personal risk tolerance and it#s impact on ideal asses allocation
- The importance of weekly and monthly variance in the returns of specific securities
Serving as a blueprint for solving portfolio optimization problems, Quantitative Portfolio Optimization: Theory and Practice is an essential resource for finance practitioners and individual investors It helps them stay on the cutting edge of modern portfolio theory and achieve the best returns on investments for themselves, their clients, and their organizations.
商品描述(中文翻譯)
專家指導量化投資組合優化技術的實施
在量化投資組合優化：理論與實踐一書中，著名的金融從業者Miquel Noguer，與擁有卓越金融知識的物理學家Alberto Bueno Guerrero和Julian Antolin Camarena，深入探討了投資組合優化的先進數學技術。本書涵蓋了一系列主題，包括均值-方差優化、Black-Litterman模型、風險平價和層級風險平價、因子投資、基於矩的方法、穩健優化以及機器學習和強化技術。這些技術使讀者能夠發展出一種系統性、客觀且可重複的投資決策方法，特別是在複雜的金融市場中。
讀者將深入了解每種方法相關的數學模型、統計分析和計算算法，使他們能夠將這些技術付諸實踐，並識別出最佳的資產組合，以最大化回報同時最小化風險。本書探討的主題包括：
- 各資產類別的回報特定驅動因素
- 個人風險承受能力及其對理想資產配置的影響
- 特定證券回報的每週和每月方差的重要性
作為解決投資組合優化問題的藍圖，量化投資組合優化：理論與實踐是金融從業者和個人投資者的重要資源，幫助他們保持在現代投資組合理論的前沿，並為自己、客戶和組織實現最佳的投資回報。
作者簡介
MIQUEL NOGUER ALONSO is a financial markets practitioner with 25+ years of experience in asset management. He is the Founder of the Artificial Intelligence Finance Institute and serves as Head of Development at Global AI. He is also the co-editor of the Journal of Machine Learning in Finance.
JULIÁN ANTOLÍN CAMARENA holds a Bachelor's, Master's and a PhD in physics. For his Master's he worked on the foundations of quantum mechanics examining alternative quantization schemes and their application to exotic atoms to discover new physics. His PhD dissertation work was on computational and theoretical optics, electromagnetic scattering from random surfaces, and nonlinear optimization. He then went on to a postdoctoral stint with the U.S. Army Research Laboratory working on inverse reinforcement learning for human-autonomy teaming.
ALBERTO BUENO GUERRERO has two Bachelor's degrees in physics and economics, and a PhD in banking and finance. Since he got his doctorate, he has dedicated himself to research in mathematical finance. His work has been presented at various international conferences and published in journals such as Quantitative Finance, Journal of Derivatives, Journal of Mathematics, and Chaos, Solitons and Fractals. His article "Bond Market Completeness Under Stochastic Strings with Distribution-Valued Strategies" has been considered a feature article in Quantitative Finance.
作者簡介(中文翻譯)
米克爾·諾格爾·阿隆索是一位擁有超過25年資產管理經驗的金融市場從業者。他是人工智慧金融研究所的創始人，並擔任全球人工智慧的發展負責人。他也是金融機器學習期刊的共同編輯。
胡利安·安托林·卡馬雷納擁有物理學的學士、碩士和博士學位。在碩士期間，他研究了量子力學的基礎，檢視替代量子化方案及其在奇異原子上的應用，以發現新物理。他的博士論文工作集中於計算和理論光學、隨機表面的電磁散射以及非線性優化。隨後，他在美國陸軍研究實驗室進行博士後研究，專注於人類與自主系統協作的逆強化學習。
阿爾貝托·布埃諾·赫雷羅擁有物理學和經濟學的兩個學士學位，以及銀行與金融的博士學位。自從獲得博士學位以來，他專注於數學金融的研究。他的研究成果已在多個國際會議上發表，並發表在如定量金融、衍生品期刊、數學期刊和混沌、孤子與分形等期刊上。他的文章《隨機字符串下的債券市場完整性與分佈值策略》被認為是定量金融的特別文章。
