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In 1952, Harry Markowitz published "Portfolio Selection," a paper which revolutionized modern investment theory and practice. The paper proposed that, in selecting investments, the investor should consider both expected return and variability of return on the portfolio as a whole. Portfolios that minimized variance for a given expected return were demonstrated to be the most efficient. Markowitz formulated the full solution of the general mean-variance efficient set problem in 1956 and presented it in the appendix to his 1959 book, Portfolio Selection. Though certain special cases of the general model have become widely known, both in academia and among managers of large institutional portfolios, the characteristics of the general solution were not presented in finance books for students at any level. And although the results of the general solution are used in a few advanced portfolio optimization programs, the solution to the general problem should not be seen merely as a computing procedure. It is a body of propositions and formulas concerning the shapes and properties of mean-variance efficient sets with implications for financial theory and practice beyond those of widely known cases. The purpose of the present book, originally published in 1987, is to present a comprehensive and accessible account of the general mean-variance portfolio analysis, and to illustrate its usefulness in the practice of portfolio management and the theory of capital markets. The portfolio selection program in Part IV of the 1987 edition has been updated and contains exercises and solutions.
Moving Beyond Modern Portfolio Theory: Investing That Matters tells the story of how Modern Portfolio Theory (MPT) revolutionized the investing world and the real economy, but is now showing its age. MPT has no mechanism to understand its impacts on the environmental, social and financial systems, nor any tools for investors to mitigate the havoc that systemic risks can wreck on their portfolios. It’s time for MPT to evolve. The authors propose a new imperative to improve finance’s ability to fulfil its twin main purposes: providing adequate returns to individuals and directing capital to where it is needed in the economy. They show how some of the largest investors in the world focus not on picking stocks, but on mitigating systemic risks, such as climate change and a lack of gender diversity, so as to improve the risk/return of the market as a whole, despite current theory saying that should be impossible. "Moving beyond MPT" recognizes the complex relations between investing and the systems on which capital markets rely, "Investing that matters" embraces MPT’s focus on diversification and risk adjusted return, but understands them in the context of the real economy and the total return needs of investors. Whether an investor, an MBA student, a Finance Professor or a sustainability professional, Moving Beyond Modern Portfolio Theory: Investing That Matters is thought-provoking and relevant. Its bold critique shows how the real world already is moving beyond investing orthodoxy.
Most practitioners use the capital asset pricing model (CAPM) to measure risk and investment performance. The CAPM, however, assumes either that all asset returns are normally distributed (and thus symmetrical) or that investors have mean-variance preferences (and thus ignore skewness and higher order moments). Both assumptions are suspect. Assuming only that the rate of return on the MARKET portfolio is i.i.d. and that markets are quot;perfectquot;, this article shows that the CAPM and its risk measures are invalid: The market portfolio is mean-variance inefficient, and the CAPM alpha mismeasures the value added by investment managers. This mismeasurement is particularly pronounced for portfolios using options or related dynamic strategies. Strategies with positively skewed returns (relative to the market), such as strategies limiting downside risk, will incorrectly be given negative alphas. A simple modification of the CAPM beta, however, will produce correct risk measurement for portfolios with arbitrary return distributions, and the resulting alphas of all fairly-priced options and/or dynamic strategies will be zero. The risk and performance measures require no more information to implement than the CAPM. In contrast with other ad hoc risk measures, such as VaR or the quot;Sortino ratioquot;, our risk measure is built on an equilibrium model of asset pricing.
Business growth depends on more than asking a single question. Challenging the widely touted Net Promoter Score (NPS) claims, author Bob E. Hayes provides compelling evidence that, to grow their business, companies need to look beyond this simple question to efforts on improving the entire customer feedback program (CFP). First, customer loyalty consists of three components, advocacy, purchasing, and retention, each providing unique and useful information regarding future business growth. By measuring these three components of customer loyalty, companies will be better able to manage their customer relationships to maximize growth through new and existing customers. Second, because of the diverse business practices companies can employ with respect to their CFPs, there are hundreds of different ways a company can structure its particular program. Some companies have top executive support for their programs while others do not. Some companies integrate their customer feedback data into their daily business processes while others keep them separate. Some companies use customer feedback results as part of their employee incentive programs while other companies rely on more traditional incentive programs. Still some companies conduct in-depth customer research using their feedback data while others rely on basic reporting of their customer feedback data for their customer insight. But are there critical elements of a customer feedback program that are absolutely necessary for its success? Can a company exclude some elements from its program without adversely impacting its effectiveness? How important are certain components in increasing customer loyalty? This book answers these questions. It is a direct result of the author’s scientific research and professional experience in the field of customer satisfaction and loyalty. This book represents the first scientific study that has tried to identify the best practices of customer feedback programs. Hayes formally collected information from many CFP professionals regarding how they structure their CFPs, and identified specific CFP practices that lead to higher levels of customer loyalty. Additionally, he worked first-hand with employees from Microsoft, Oracle, Harris Stratex Networks, Akamai, and American Express Business Travel in gathering insights and case studies to illustrate how to build a world class CFP. Learn why companies should look beyond the NPS as the ultimate question and learn how to design an effective CFP that will help improve the customer experience, increase customer loyalty, and, ultimately, drive business growth. For those unfamiliar with CFPs, the appendices provide detail on methods used in the main body of the book: a discussion on methods of determining customer requirements (those elements of your business that are important to your customers), a complete discussion on how to write survey questions, and brief discussions on particular statistical analysis methods that can help you understand how customer feedback data are analyzed.
本书向您介绍了投资分析与组合管理。
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Today, when so many countries share the same transboundary river basins, unilateral water management becomes extremely critical. Joint water management, on the other hand, faces many obstacles, because of states' interests in particular function of the water. Water is needed for drinking, household, irrigation, hydro-energy, fishing, navigation, tourism as well as many other purposes, and unfortunately, it cannot meet all needs simultaneously. The satisfaction of one need often impedes the others. In this regard, transboundary river basins often create challenges among states by leading them to negotiate in asymmetric relations.This book is an attempt to address the effectiveness of transboundary water management from a social and political perspective by offering new theoretical underpinnings to understand the success conditions in sharing transboundary water resources. The author focused in the work on three conditions, i.e. institutional conditions and country specific conditions, as well as conditions of regional integrity level and role of external actors. The proposed conditions were tested on two river basins i.e. the successful one in the example of Orange/Senqu river basin and unsuccessful one in the case of Naryn/Syrdarya river basin.
Mixed modelling is one of the most promising and exciting areas ofstatistical analysis, enabling more powerful interpretation of datathrough the recognition of random effects. However, many perceivemixed modelling as an intimidating and specialized technique. Thisbook introduces mixed modelling analysis in a simple andstraightforward way, allowing the reader to apply the techniqueconfidently in a wide range of situations. Introduction to Mixed Modelling shows that mixedmodelling is a natural extension of the more familiar statisticalmethods of regression analysis and analysis of variance. In doingso, it provides the ideal introduction to this importantstatistical technique for those engaged in the statistical analysisof data. This essential book: Demonstrates the power of mixed modelling in a wide range ofdisciplines, including industrial research, social sciences,genetics, clinical research, ecology and agriculturalresearch. Illustrates how the capabilities of regression analysis can becombined with those of ANOVA by the specification of a mixedmodel. Introduces the criterion of Restricted Maximum Likelihood(REML) for the fitting of a mixed model to data. Presents the application of mixed model analysis to a widerange of situations and explains how to obtain and interpret BestLinear Unbiased Predictors (BLUPs). Features a supplementary website containing solutions toexercises, further examples, and links to the computer softwaresystems GenStat and R. This book provides a comprehensive introduction to mixedmodelling, ideal for final year undergraduate students,postgraduate students and professional researchers alike. Readerswill come from a wide range of scientific disciplines includingstatistics, biology, bioinformatics, medicine, agriculture,engineering, economics, and social sciences.