{"product_id":"quantitative-portfolio-optimization-isbn-9781394281312","title":"Quantitative Portfolio Optimization","description":"\u003cp\u003e\u003cb\u003eExpert guidance on implementing quantitative portfolio optimization techniques\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eQuantitative Portfolio Optimization: Theory and Practice,\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eReaders 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eSpecific drivers of return across asset classes\u003c\/li\u003e \u003cli\u003ePersonal risk tolerance and it#s impact on ideal asses allocation\u003c\/li\u003e \u003cli\u003eThe importance of weekly and monthly variance in the returns of  specific securities\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eServing as a blueprint for solving  portfolio optimization problems, \u003ci\u003eQuantitative Portfolio Optimization: Theory and Practice\u003c\/i\u003e 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. \u003c\/p\u003e\u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003eContents\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e \u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003ePreface  xiii\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003eAcknowledgements  xv\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003eAbout the Authors  xvii\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e \u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003eCHAPTER  1\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e \u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003eIntroduction  1\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e \u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e1.1 Evolution of Portfolio Optimization 1\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e1.2 Role of Quantitative Techniques 1\u003c\/div\u003e \u003cdiv id=\"_mcePaste\" style=\"position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;\"\u003e1.3 Organization of the Book 4\u003c\/div\u003e \u003cdiv\u003eContents\u003cbr\u003e\u003cbr\u003e \u003cdiv\u003ePreface  xiii\u003c\/div\u003e \u003cdiv\u003eAcknowledgements  xv\u003c\/div\u003e \u003cdiv\u003eAbout the Authors  xvii\u003cbr\u003e\u003cbr\u003e \u003cdiv\u003eCHAPTER  1\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eIntroduction  1\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e \u003cdiv\u003e1.1 Evolution of Portfolio Optimization 1\u003c\/div\u003e \u003cdiv\u003e1.2 Role of Quantitative Techniques 1\u003c\/div\u003e \u003cdiv\u003e1.3 Organization of the Book 4\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e \u003cdiv\u003eCHAPTER  2\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eHistory of Portfolio Optimization 7\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e2.1 Early beginnings 7\u003c\/div\u003e \u003cdiv\u003e2.2 Harry Markowitz’s Modern Portfolio Theory (1952) 9\u003c\/div\u003e \u003cdiv\u003e2.3 Black-Litterman Model (1990s) 13\u003c\/div\u003e \u003cdiv\u003e2.4 Alternative Methods: Risk Parity, Hierarchical Risk Parity and Machine Learning  19\u003c\/div\u003e \u003cdiv\u003e     2.4.1 Risk Parity  19\u003c\/div\u003e \u003cdiv\u003e     2.4.2 Hierarchical Risk Parity  26\u003c\/div\u003e \u003cdiv\u003e     2.4.3 Machine Learning  27\u003c\/div\u003e \u003cdiv\u003e2.5 Notes  31\u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003ePART ONE\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eFoundations of Portfolio Theory\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER 3\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eModern Portfolio Theory  35\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e3.1 Efficient Frontier and Capital Market Line  35\u003c\/div\u003e \u003cdiv\u003e      3.1.1 Case Without Riskless Asset  35\u003c\/div\u003e \u003cdiv\u003e      3.1.2 Case With a Riskless Asset  41\u003c\/div\u003e \u003cdiv\u003e3.2 Capital Asset Pricing Model  48\u003c\/div\u003e \u003cdiv\u003e      3.2.1 Case Without Riskless Asset  48\u003c\/div\u003e \u003cdiv\u003e      3.2.2 Case With a Riskless Asset  52\u003c\/div\u003e \u003cdiv\u003e3.3 Multifactor Models  54\u003c\/div\u003e \u003cdiv\u003e3.4 Challenges of Modern Portfolio Theory  59\u003c\/div\u003e \u003cdiv\u003e      3.4.1 Estimation Techniques in Portfolio Allocation  60\u003c\/div\u003e \u003cdiv\u003e      3.4.2 Non-Elliptical Distributions and Conditional Value-at-Risk (CVaR)  63\u003c\/div\u003e \u003cdiv\u003e3.5 Quantum Annealing in Portfolio Management  65\u003c\/div\u003e \u003cdiv\u003e3.6 Mean-Variance Optimization with CVaR Constraint  67\u003c\/div\u003e \u003cdiv\u003e      3.6.1 Problem Formulation  67\u003c\/div\u003e \u003cdiv\u003e      3.6.2 Optimization Problem  68\u003c\/div\u003e \u003cdiv\u003e      3.6.3 Clarification of Optimization Classes  68\u003c\/div\u003e \u003cdiv\u003e      3.6.4 Numerical Example  69\u003c\/div\u003e \u003cdiv\u003e3.7 Notes  70\u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003eCHAPTER  4\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eBayesian Methods in Portfolio Optimization   73\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e4.1 The Prior  75\u003c\/div\u003e \u003cdiv\u003e4.2 The Likelihood  79\u003c\/div\u003e \u003cdiv\u003e4.3 The Posterior  80\u003c\/div\u003e \u003cdiv\u003e4.4 Filtering  83\u003c\/div\u003e \u003cdiv\u003e4.5 Hierarchical Bayesian Models  87\u003c\/div\u003e \u003cdiv\u003e4.6 Bayesian Optimization  89\u003c\/div\u003e \u003cdiv\u003e      4.6.1 Gaussian Processes in a Nutshell  90\u003c\/div\u003e \u003cdiv\u003e     4.6.2 Uncertainty Quantification and Bayesian Decision Theory  94\u003c\/div\u003e \u003cdiv\u003e4.7 Applications to Portfolio Optimization  96\u003c\/div\u003e \u003cdiv\u003e     4.7.1 GP Regression for Asset Returns  96\u003c\/div\u003e \u003cdiv\u003e     4.7.2 Decision Theory in Portfolio Optimization  96\u003c\/div\u003e \u003cdiv\u003e     4.7.3 The Black-Litterman Model  99\u003c\/div\u003e \u003cdiv\u003e4.8 Notes  103\u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003ePART TWO\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eRisk Management\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER 5\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eRisk Models and Measures  107\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e5.1 Risk Measures  107\u003c\/div\u003e \u003cdiv\u003e5.2 VaR and CVaR  109\u003c\/div\u003e \u003cdiv\u003e      5.2.1 VaR  110\u003c\/div\u003e \u003cdiv\u003e     5.2.2 CVaR  112\u003c\/div\u003e \u003cdiv\u003e5.3 Estimation Methods  116\u003c\/div\u003e \u003cdiv\u003e     5.3.1 Variance-Covariance Method  116\u003c\/div\u003e \u003cdiv\u003e     5.3.2 Historical Simulation  116\u003c\/div\u003e \u003cdiv\u003e     5.3.3 Monte Carlo Simulation  117\u003c\/div\u003e \u003cdiv\u003e5.4 Advanced Risk Measures: Tail Risk and Spectral Measures  118\u003c\/div\u003e \u003cdiv\u003e     5.4.1 Tail Risk Measures  118\u003c\/div\u003e \u003cdiv\u003e     5.4.2 Spectral Measures  120\u003c\/div\u003e \u003cdiv\u003e5.5 Notes 123\u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003eCHAPTER 6\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eFactor Models and Factor Investing  125\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e6.1 Single and Multifactor Models  126\u003c\/div\u003e \u003cdiv\u003e      6.1.1 Statistical Models  127\u003c\/div\u003e \u003cdiv\u003e      6.1.2 Macroeconomic Models  128\u003c\/div\u003e \u003cdiv\u003e      6.1.3 Cross-sectional Models  130\u003c\/div\u003e \u003cdiv\u003e6.2 Factor Risk and Performance Attribution  135\u003c\/div\u003e \u003cdiv\u003e6.3 Machine Learning in Factor Investing  141\u003c\/div\u003e \u003cdiv\u003e6.4 Notes  144\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER 7\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eMarket Impact, Transaction Costs, and Liquidity  145\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e7.1 Market Impact Models  145\u003c\/div\u003e \u003cdiv\u003e7.2 Modeling Transaction Costs  148\u003c\/div\u003e \u003cdiv\u003e      7.2.1 Single Asset  151\u003c\/div\u003e \u003cdiv\u003e      7.2.2 Multiple Assets  154\u003c\/div\u003e \u003cdiv\u003e7.3 Optimal Trading Strategies  155\u003c\/div\u003e \u003cdiv\u003e      7.3.1 Mei, DeMiguel, and Nogales (2016)  156\u003c\/div\u003e \u003cdiv\u003e      7.3.2 Skaf and Boyd (2009)  159\u003c\/div\u003e \u003cdiv\u003e7.4 Liquidity Considerations in Portfolio Optimization  161\u003c\/div\u003e \u003cdiv\u003e      7.4.1 MV and Liquidity  162\u003c\/div\u003e \u003cdiv\u003e      7.4.2 CAPM and Liquidity  163\u003c\/div\u003e \u003cdiv\u003e      7.4.3 APT and Liquidity  165\u003c\/div\u003e \u003cdiv\u003e7.5 Notes  167\u003cbr\u003e\u003cbr\u003e \u003cdiv\u003ePART THREE\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eDynamic Models and Control\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER 8\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eOptimal Control  171\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e8.1 Dynamic Programming  171\u003c\/div\u003e \u003cdiv\u003e8.2 Approximate Dynamic Programming  171\u003c\/div\u003e \u003cdiv\u003e8.3 The Hamilton-Jacobi-Bellman Equation  172\u003c\/div\u003e \u003cdiv\u003e8.4 Sufficiently Smooth Problems  174\u003c\/div\u003e \u003cdiv\u003e8.5 Viscosity Solutions  176\u003c\/div\u003e \u003cdiv\u003e8.6 Applications to Portfolio Optimization  180\u003c\/div\u003e \u003cdiv\u003e      8.6.1 Classical Merton Problem  180\u003c\/div\u003e \u003cdiv\u003e      8.6.2 Multi-asset Portfolio with Transaction Costs  181\u003c\/div\u003e \u003cdiv\u003e      8.6.3 Risk-sensitive Portfolio Optimization  183\u003c\/div\u003e \u003cdiv\u003e      8.6.4 Optimal Portfolio Allocation with Transaction Costs  184\u003c\/div\u003e \u003cdiv\u003e      8.6.5 American Option Pricing  184\u003c\/div\u003e \u003cdiv\u003e      8.6.6 Portfolio Optimization with Constraints  184\u003c\/div\u003e \u003cdiv\u003e      8.6.7 Mean-variance Portfolio Optimization  185\u003c\/div\u003e \u003cdiv\u003e      8.6.8 Schödinger Control in Wealth Management  185\u003c\/div\u003e \u003cdiv\u003e8.7 Notes  187\u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003eCHAPTER 9\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eMarkov Decision Processes  189\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e9.1 Fully Observed MDPs  191\u003c\/div\u003e \u003cdiv\u003e9.2 Partially Observed MDPs  192\u003c\/div\u003e \u003cdiv\u003e9.3 Infinite Horizon Problems  194\u003c\/div\u003e \u003cdiv\u003e9.4 Finite Horizon Problems  198\u003c\/div\u003e \u003cdiv\u003e9.5 The Bellman Equation  200\u003c\/div\u003e \u003cdiv\u003e9.6 Solving the Bellman Equation  203\u003c\/div\u003e \u003cdiv\u003e9.7 Examples in Portfolio Optimization  205\u003c\/div\u003e \u003cdiv\u003e      9.7.1 An MDP in Multi-asset Allocation with Transaction Costs  205\u003c\/div\u003e \u003cdiv\u003e      9.7.2 A POMDP for Asset Allocation with Regime Switching  205\u003c\/div\u003e \u003cdiv\u003e      9.7.3 An MDP with Continuous State and Action Spaces for Option Hedging with Stochastic Volatility  206\u003c\/div\u003e \u003cdiv\u003e9.8 Notes  207\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER  10\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eReinforcement Learning  209\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e10.1 Connections to Optimal Control  211\u003c\/div\u003e \u003cdiv\u003e       10.1.1 Policy Iteration  212\u003c\/div\u003e \u003cdiv\u003e       10.1.2 Value Iteration  214\u003c\/div\u003e \u003cdiv\u003e       10.1.3 Continuous vs. Discrete Formulations  215\u003c\/div\u003e \u003cdiv\u003e10.2 The Environment and The Reward Function  217\u003c\/div\u003e \u003cdiv\u003e         10.2.1 The Environment  217\u003c\/div\u003e \u003cdiv\u003e         10.2.2 The Reward Function  220\u003c\/div\u003e \u003cdiv\u003e10.3 Agents Acting in an Environment  223\u003c\/div\u003e \u003cdiv\u003e10.4 State-Action and Value Functions  225\u003c\/div\u003e \u003cdiv\u003e        10.4.1 Value Functions  226\u003c\/div\u003e \u003cdiv\u003e        10.4.2 Gradients and Policy Improvement  227\u003c\/div\u003e \u003cdiv\u003e10.5 The Policy  230\u003c\/div\u003e \u003cdiv\u003e10.6 On-Policy Methods  233\u003c\/div\u003e \u003cdiv\u003e10.7 Off-Policy Methods  235\u003c\/div\u003e \u003cdiv\u003e10.8 Applications to Portfolio Optimization  238\u003c\/div\u003e \u003cdiv\u003e       10.8.1 Mean-variance Optimization  238\u003c\/div\u003e \u003cdiv\u003e       10.8.2 Reinforcement Learning Comparison with Mean-variance Optimization  239\u003c\/div\u003e \u003cdiv\u003e       10.8.3 G-Learning and GIRL  241\u003c\/div\u003e \u003cdiv\u003e       10.8.4 Continuous-time Penalization in Portfolio Optimization  244\u003c\/div\u003e \u003cdiv\u003e       10.8.5 Reinforcement Learning for Utility Maximization  246\u003c\/div\u003e \u003cdiv\u003e       10.8.6 Continuous-time Portfolio Optimization with Transaction Costs  246\u003c\/div\u003e \u003cdiv\u003e10.9 Notes  247\u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003ePART FOUR\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eMachine Learning and Deep Learning\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER 11\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eDeep Learning in Portfolio Management  253\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e11.1 Neurons and Activation Functions  253\u003c\/div\u003e \u003cdiv\u003e11.2 Neural Networks and Function Approximation  256\u003c\/div\u003e \u003cdiv\u003e11.3 Review of Some Important Architectures  259\u003c\/div\u003e \u003cdiv\u003e11.4 Physics-Informed Neural Networks  269\u003c\/div\u003e \u003cdiv\u003e11.5 Applications to Portfolio Optimization  276\u003c\/div\u003e \u003cdiv\u003e        11.5.1 Dynamic Asset Allocation Using the Heston Model  276\u003c\/div\u003e \u003cdiv\u003e        11.5.2 Option-Based Portfolio Insurance Using the Bates Model  277\u003c\/div\u003e \u003cdiv\u003e        11.5.3 Factor Learning Approach to Generative Modeling of Equities  278\u003c\/div\u003e \u003cdiv\u003e11.6 The Case for and Against Deep Learning  280\u003c\/div\u003e \u003cdiv\u003e11.7 Notes  282\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER 12\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eGraph-based Portfolios  285\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e12.1 Graph Theory-Based Portfolios  285\u003c\/div\u003e \u003cdiv\u003e       12.1.1 Literature Review  285\u003c\/div\u003e \u003cdiv\u003e12.2 Graph Theory Portfolios: MST and TMFG  285\u003c\/div\u003e \u003cdiv\u003e       12.2.1 Equations and Formulas  286\u003c\/div\u003e \u003cdiv\u003e       12.2.2 Results  287\u003c\/div\u003e \u003cdiv\u003e12.3 Hierarchical Risk Parity  289\u003c\/div\u003e \u003cdiv\u003e12.4 Notes  294\u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003eCHAPTER 13\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eSensitivity-based Portfolios  295\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e13.1 Modeling Portfolios Dynamics with PDEs  296\u003c\/div\u003e \u003cdiv\u003e13.2 Optimal Drivers Selection: Causality and Persistence  297\u003c\/div\u003e \u003cdiv\u003e13.3 AAD Sensitivities Approximation  303\u003c\/div\u003e \u003cdiv\u003e        13.3.1 Optimal Network Selection  304\u003c\/div\u003e \u003cdiv\u003e        13.3.2 Sensitivity Analysis  304\u003c\/div\u003e \u003cdiv\u003e        13.3.3 Sensitivity Distance Matrix  304\u003c\/div\u003e \u003cdiv\u003e13.4 Hierarchical Sensitivity Parity  307\u003c\/div\u003e \u003cdiv\u003e13.5 Implementation  307\u003c\/div\u003e \u003cdiv\u003e        13.5.1 Datasets  307\u003c\/div\u003e \u003cdiv\u003e        13.5.2 Experimental Setup  308\u003c\/div\u003e \u003cdiv\u003e        13.5.3 Short-to-medium Investments  309\u003c\/div\u003e \u003cdiv\u003e        13.5.4 Long-term Investments  312\u003c\/div\u003e \u003cdiv\u003e13.6 Conclusion  315\u003c\/div\u003e \u003c\/div\u003e \u003cdiv\u003e\n\u003cbr\u003e \u003cdiv\u003ePART FIVE\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eBacktesting\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER  14\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eBacktesting in Portfolio Management  319\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e14.1 Introduction  319\u003c\/div\u003e \u003cdiv\u003e14.2 Data Preparation and Handling  319\u003c\/div\u003e \u003cdiv\u003e14.3 Implementation of Trading Strategies  320\u003c\/div\u003e \u003cdiv\u003e14.4 Types of Backtests  321\u003c\/div\u003e \u003cdiv\u003e        14.4.1 Walk-forward Backtest  321\u003c\/div\u003e \u003cdiv\u003e        14.4.2 Resampling Method  321\u003c\/div\u003e \u003cdiv\u003e        14.4.3 Monte Carlo Simulations and Generative Models  321\u003c\/div\u003e \u003cdiv\u003e14.5 Performance Metrics  322\u003c\/div\u003e \u003cdiv\u003e14.6 Avoiding Common Pitfalls  323\u003c\/div\u003e \u003cdiv\u003e14.7 Advanced Techniques  323\u003c\/div\u003e \u003cdiv\u003e14.8 Case Study: Applying Backtesting to a Real-World Strategy  324\u003c\/div\u003e \u003cdiv\u003e14.9 Impact of Market Conditions on Backtest Results  324\u003c\/div\u003e \u003cdiv\u003e14.10 Integration with Portfolio Management  325\u003c\/div\u003e \u003cdiv\u003e14.11 Tools and Software for Backtesting  325\u003c\/div\u003e \u003cdiv\u003e14.12 Regulatory Considerations  326\u003c\/div\u003e \u003cdiv\u003e14.13 Conclusion  326\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eCHAPTER  15\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eScenario Generation  329\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003e15.1 Historical Scenarios  329\u003c\/div\u003e \u003cdiv\u003e15.2 Bootstrapping Scenarios  330\u003c\/div\u003e \u003cdiv\u003e15.3 Copula-Based Scenarios  330\u003c\/div\u003e \u003cdiv\u003e15.4 Risk Factor Model-Based Scenarios  330\u003c\/div\u003e \u003cdiv\u003e15.5 Time Series Model Scenarios  331\u003c\/div\u003e \u003cdiv\u003e15.6 Variational Autoencoders  331\u003c\/div\u003e \u003cdiv\u003e15.7 Generative Adversarial Networks (GANs)  332\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eAppendix 333\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eA.1 Software and Tools for Portfolio Optimization  333\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eBibliography  335\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e \u003cdiv\u003eIndex  357\u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e  \u003cp\u003e\u003cb\u003eMIQUEL NOGUER ALONSO\u003c\/b\u003e 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 \u003ci\u003eJournal of Machine Learning in Finance\u003c\/i\u003e. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJULIÁN ANTOLÍN CAMARENA\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eALBERTO BUENO GUERRERO\u003c\/b\u003e 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 \u003ci\u003eQuantitative Finance\u003c\/i\u003e, \u003ci\u003eJournal of Derivatives\u003c\/i\u003e, \u003ci\u003eJournal of Mathematics\u003c\/i\u003e, and \u003ci\u003eChaos, Solitons and Fractals\u003c\/i\u003e. His article “Bond Market Completeness Under Stochastic Strings with Distribution-Valued Strategies” has been considered a feature article in \u003ci\u003eQuantitative Finance\u003c\/i\u003e.   \u003c\/p\u003e\u003cp\u003e\u003ci\u003eQuantitative Portfolio Optimization: Advanced Techniques and Applications\u003c\/i\u003e is an authoritative guide on using mathematical models, statistical analyses, and computational algorithms to optimize the composition of an investment portfolio and allow for a systematic, objective, and repeatable approach to investment decision-making, especially in complex financial markets. In this book, readers will learn to identify the best possible mix of assets that can maximize returns while minimizing risk based on the investor’s specific objectives and constraints. \u003c\/p\u003e\u003cp\u003eWritten by Miquel Noguer Alonso, an experienced financial markets practitioner and pioneer in the field, Julián Antolín Camarena, experienced AI researcher and physicist, and Alberto Bueno Guerrero, accomplished researcher in mathematical finance, this book takes a deep dive into various methods in quantitative portfolio optimization, including mean-variance optimization, the Black-Litterman Model, risk parity and hierarchical risk parity, factor investing, machine learning models, methods based on moments, and robust optimization. Readers will learn about the unique approach and application of each of these methods and receive a variety of tools that can be used in their efforts to practically construct and manage their portfolios. \u003c\/p\u003e\u003cp\u003eProviding key knowledge on advanced mathematical techniques for portfolio optimization to solve one of the central problems in finance, \u003ci\u003eQuantitative Portfolio Optimization: Advanced Techniques and Applications\u003c\/i\u003e earns a well-deserved spot on the bookshelves of finance practitioners and academics interested in portfolio management, along with all investors looking to stay on the cutting edge of modern investment techniques.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003csmall\u003ePRAISE FOR\u003c\/small\u003e\u003cbr\u003e QUANTITATIVE PORTFOLIO OPTIMIZATIONOPTIMIZATION\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e“This book provides an excellent exposition on portfolio optimization, serving not only as a self-contained guide to this important topic, but also modernizing the field with the latest advances in battle-tested machine learning approaches. The book is well structured and application centric. This is a must read for every quantitative portfolio manager.”\u003cbr\u003e \u003cb\u003e— Matthew Dixon, FRM, Ph.D.,\u003c\/b\u003e Associate Professor of Applied Math at the Illinois Institute of Technology and an Affiliate Associate Professor of the Stuart School of Business \u003c\/p\u003e\u003cp\u003e“\u003ci\u003eQuantitative Portfolio Optimization: Advanced Techniques and Applications\u003c\/i\u003e is an essential guide for anyone seeking to navigate the complex world of modern portfolio management. This book masterfully blends the foundational principles of portfolio theory with cutting-edge advancements in risk management, dynamic models, and control systems. Its integration of machine learning and deep learning offers readers a forward-looking perspective on leveraging AI-driven techniques for optimization. What truly sets this book apart is its comprehensive approach. From theoretical insights to practical backtesting applications, it equips professionals, researchers, and students with the tools to design and refine robust investment strategies. Whether you're delving into the nuances of risk modelling or exploring dynamic portfolio control with the latest AI methodologies, this text is an invaluable resource. This book isn’t just about managing portfolios—it’s about mastering the art and science behind it. Highly recommended for anyone aiming to achieve excellence in quantitative finance and portfolio optimization.”\u003cbr\u003e \u003cb\u003e—Daniel Bloch,\u003c\/b\u003e Director, Quant Finance Limited\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989896773861,"sku":"NP9781394281312","price":95.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394281312.jpg?v=1761785832","url":"https:\/\/k12savings.com\/products\/quantitative-portfolio-optimization-isbn-9781394281312","provider":"K12savings","version":"1.0","type":"link"}