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Portfolio Optimization and Performance Analysis by Jean-Luc Prigent Pdf
In answer to the intense development of new financial products and the increasing complexity of portfolio management theory, Portfolio Optimization and Performance Analysis offers a solid grounding in modern portfolio theory. The book presents both standard and novel results on the axiomatics of the individual choice in an uncertain framework, cont
Portfolio Theory and Performance Analysis by Noel Amenc,Veronique Le Sourd Pdf
For many years asset management was considered to be a marginal activity, but today, it is central to the development of financial industry throughout the world. Asset management's transition from an "art and craft" to an industry has inevitably called integrated business models into question, favouring specialisation strategies based on cost optimisation and learning curve objectives. This book connects each of these major categories of techniques and practices to the unifying and seminal conceptual developments of modern portfolio theory. In these bear market times, performance evaluation of portfolio managers is of central focus. This book will be one of very few on the market and is by a respected member of the profession. Allows the professionals, whether managers or investors, to take a step back and clearly separate true innovations from mere improvements to well-known, existing techniques Puts into context the importance of innovations with regard to the fundamental portfolio management questions, which are the evolution of the investment management process, risk analysis and performance measurement Takes the explicit or implicit assumptions contained in the promoted tools into account and, by so doing, evaluate the inherent interpretative or practical limits
Portfolio Performance Evaluation by George O. Aragon,Wayne E. Ferson Pdf
This paper provides a review of the methods for measuring portfolio performance and the evidence on the performance of professionally managed investment portfolios. Traditional performance measures, strongly influenced by the Capital Asset Pricing Model of Sharpe (1964), were developed prior to 1990. We discuss some of the properties and important problems associated with these measures. We then review the more recent Conditional Performance Evaluation techniques, designed to allow for expected returns and risks that may vary over time, and thus addressing one major shortcoming of the traditional measures. We also discuss weight-based performance measures and the stochastic discount factor approach. We review the evidence that these newer measures have produced on selectivity and market timing ability for professional managed investment funds. The evidence includes equity style mutual funds, pension funds, asset allocation style funds, fixed income funds and hedge funds.
Investments: Portfolio theory and asset pricing by Edwin J. Elton,Martin Jay Gruber Pdf
This collection of articles in investment and portfolio management spans the thirty-five-year collaborative effort of two key figures in finance. Each of the nine sections begins with an overview that introduces the main contributions of the pieces and traces the development of the field. Each volume contains a foreword by Nobel laureate Harry Markowitz. Volume I presents the authors' groundbreaking work on estimating the inputs to portfolio optimization, including the analysis of alternative structures such as single and multi-index models in forecasting correlations; portfolio maximization under alternative specifications for return structures; the impact of CAPM and APT in the investment process; and taxes and portfolio composition. Volume II covers the authors' work on analysts' expectations; performance evaluation of managed portfolios, including commodity, stock, and bond portfolios; survivorship bias and performance persistence; debt markets; and immunization and efficiency.
Frank J. Fabozzi,Petter N. Kolm,Dessislava A. Pachamanova,Sergio M. Focardi
Author : Frank J. Fabozzi,Petter N. Kolm,Dessislava A. Pachamanova,Sergio M. Focardi Publisher : John Wiley & Sons Page : 513 pages File Size : 54,7 Mb Release : 2007-04-27 Category : Business & Economics ISBN : 9780470164891
Robust Portfolio Optimization and Management by Frank J. Fabozzi,Petter N. Kolm,Dessislava A. Pachamanova,Sergio M. Focardi Pdf
Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction." --Mark Kritzman, President and CEO, Windham Capital Management, LLC "The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike." --John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University
Robust Equity Portfolio Management, + Website by Woo Chang Kim,Jang Ho Kim,Frank J. Fabozzi Pdf
A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.
Stochastic Portfolio Theory by E. Robert Fernholz Pdf
Stochastic portfolio theory is a mathematical methodology for constructing stock portfolios and for analyzing the effects induced on the behavior of these portfolios by changes in the distribution of capital in the market. Stochastic portfolio theory has both theoretical and practical applications: as a theoretical tool it can be used to construct examples of theoretical portfolios with specified characteristics and to determine the distributional component of portfolio return. This book is an introduction to stochastic portfolio theory for investment professionals and for students of mathematical finance. Each chapter includes a number of problems of varying levels of difficulty and a brief summary of the principal results of the chapter, without proofs.
Efficient Asset Management by Richard O. Michaud,Robert O. Michaud Pdf
In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives. Financial experts Richard and Robert Michaud demonstrate that the limitations of MV optimization are not the result of conceptual flaws in Markowitz theory but unrealistic representation of investment information. What is missing is a realistic treatment of estimation error in the optimization and rebalancing process. The text provides a non-technical review of classical Markowitz optimization and traditional objections. The authors demonstrate that in practice the single most important limitation of MV optimization is oversensitivity to estimation error. Portfolio optimization requires a modern statistical perspective. Efficient Asset Management, Second Edition uses Monte Carlo resampling to address information uncertainty and define Resampled Efficiency (RE) technology. RE optimized portfolios represent a new definition of portfolio optimality that is more investment intuitive, robust, and provably investment effective. RE rebalancing provides the first rigorous portfolio trading, monitoring, and asset importance rules, avoiding widespread ad hoc methods in current practice. The Second Edition resolves several open issues and misunderstandings that have emerged since the original edition. The new edition includes new proofs of effectiveness, substantial revisions of statistical estimation, extensive discussion of long-short optimization, and new tools for dealing with estimation error in applications and enhancing computational efficiency. RE optimization is shown to be a Bayesian-based generalization and enhancement of Markowitz's solution. RE technology corrects many current practices that may adversely impact the investment value of trillions of dollars under current asset management. RE optimization technology may also be useful in other financial optimizations and more generally in multivariate estimation contexts of information uncertainty with Bayesian linear constraints. Michaud and Michaud's new book includes numerous additional proposals to enhance investment value including Stein and Bayesian methods for improved input estimation, the use of portfolio priors, and an economic perspective for asset-liability optimization. Applications include investment policy, asset allocation, and equity portfolio optimization. A simple global asset allocation problem illustrates portfolio optimization techniques. A final chapter includes practical advice for avoiding simple portfolio design errors. With its important implications for investment practice, Efficient Asset Management 's highly intuitive yet rigorous approach to defining optimal portfolios will appeal to investment management executives, consultants, brokers, and anyone seeking to stay abreast of current investment technology. Through practical examples and illustrations, Michaud and Michaud update the practice of optimization for modern investment management.
Performance Evaluation and Attribution of Security Portfolios by Bernd R. Fischer,Russ Wermers Pdf
Just how successful is that investment? Measuring portfolio performance requires evaluation (measuring portfolio results against benchmarks) and attribution (determining individual results of the portfolio's parts), In this book, a professor and an asset manager show readers how to use theories, applications, and real data to understand these tools. Unlike others, Fischer and Wermers teach readers how to pick the theories and applications that fit their specific needs. With material inspired by the recent financial crisis, Fischer and Wermers bring new clarity to defining investment success. Gives readers the theories and the empirical tools to handle their own data Features practice problems formerly from the CFA Program curriculum.
Quantitative Global Bond Portfolio Management by Gueorgui S Konstantinov,Frank J Fabozzi,Joseph Simonian Pdf
Quantitative Global Bond Portfolio Management offers a comprehensive discussion of quantitative modelling approaches to managing global bond and currency portfolios. Drawing on practitioner and academic research, as well as the extensive market experience of the authors, the book provides a timely overview of cutting-edge tools applied to the management of global bond portfolios, including in-depth discussions of factor models and optimization techniques. In addition to providing a solid theoretical foundation for global bond portfolio management, the authors focus on the practical implementation of yield curve and currency-driven approaches that can be successfully implemented in actual portfolios. As such, the book will be an indispensable resource to both new and seasoned investors looking to enhance their understanding of global bond markets and strategies.
Progress in Sustainable Development by David S-K. Ting,Paul G. O'Brien Pdf
Progress in Sustainable Development: Sustainable Engineering Practices provides readers with the latest research and best practices in sustainable engineering in the fields of urban, environmental, energy and sustainability sciences, reflecting a focus on state-of-the art insights and the latest developments. Chapters focus on the key engineering principles of effective resource use, reduction of excess waste, and taking advantage of natural resources to equip readers with the background information and practical considerations of successful implementations of sustainable technical solutions. Each chapter features detailed case studies and figures showing real-world applications of the latest technologies, ensuring they are reproduceable by the reader. The multidisciplinary chapters include environmentally-friendly technologies and the application of novel initiatives in engineering for infrastructure, renewable energy generation, advanced materials and waste, among other areas, with a strong emphasis on sustainability and conservation of resources. Provides the most recent developments and novel practices in engineering for furthering sustainable development Takes an interdisciplinary look at sustainable engineering practices across the fields of urban studies, environmental science and energy Includes case studies to show how readers can implement the practical engineering solutions detailed
Machine Learning for Algorithmic Trading by Stefan Jansen Pdf
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization by Svetlozar T. Rachev,Stoyan V. Stoyanov,Frank J. Fabozzi Pdf
This groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework. Throughout these pages, the expert authors explain the fundamentals of probability metrics, outline new approaches to portfolio optimization, and discuss a variety of essential risk measures. Using numerous examples, they illustrate a range of applications to optimal portfolio choice and risk theory, as well as applications to the area of computational finance that may be useful to financial engineers.
Diderich describes tools and techniques, which can be used to develop quantitative models for actively managing investment products, and focuses on how theoretical models can and should be used in practice. He describes the interaction between different elements of an investment process's value chain in a single and consistent framework. A key focus is placed on illustrating the theory with real world examples. At the end of the book the reader will be capable of designing or enhancing an investment process for an investment or portfolio managers products from start to finish. * Increased pressure to add value through investments makes this a hot topic in the investment world * Combined theoretical and practical approach makes this book appealing to a wide audience of quants and investors * The only book to show how to design and implement quantitative models for gaining positive alpha
Portfolio and Investment Analysis with SAS by John B. Guerard,Ziwei Wang,Ganlin Xu Pdf
Choose statistically significant stock selection models using SAS® Portfolio and Investment Analysis with SAS®: Financial Modeling Techniques for Optimization is an introduction to using SAS to choose statistically significant stock selection models, create mean-variance efficient portfolios, and aggressively invest to maximize the geometric mean. Based on the pioneering portfolio selection techniques of Harry Markowitz and others, this book shows that maximizing the geometric mean maximizes the utility of final wealth. The authors draw on decades of experience as teachers and practitioners of financial modeling to bridge the gap between theory and application. Using real-world data, the book illustrates the concept of risk-return analysis and explains why intelligent investors prefer stocks over bonds. The authors first explain how to build expected return models based on expected earnings data, valuation ratios, and past stock price performance using PROC ROBUSTREG. They then show how to construct and manage portfolios by combining the expected return and risk models. Finally, readers learn how to perform hypothesis testing using Bayesian methods to add confidence when data mining from large financial databases.