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, contains a precise overview of standard portfolio optimization, provides a review of the main results for static and dynamic cases, and shows how theoretical results can be applied to practical and operational portfolio optimization. Divided into four sections that mirror the book's aims, this resource first describes the fundamental results of decision theory, including utility maximization and risk measure minimization. Covering both active and passive portfolio management, the second part discusses standard portfolio optimization and performance measures. The book subsequently introduces dynamic portfolio optimization based on stochastic control and martingale theory. It also outlines portfolio optimization with market frictions, such as incompleteness, transaction costs, labor income, and random time horizon. The final section applies theoretical results to practical portfolio optimization, including structured portfolio management. It details portfolio insurance methods as well as performance measures for alternative investments, such as hedge funds. Taking into account the different features of portfolio management theory, this book promotes a thorough understanding for students and professionals in the field.
Based on an exclusive business-to-business database comprising nearly 1,000 customers, the applicability of portfolio analysis is documented, and it is examined how such an optimization analysis can be used to explore the growth potential of a company. As opposed to any previous analyses, optimal customer portfolios are determined, and it is shown how marketing decision-makers can use this information in their marketing strategies to optimize the revenue growth of the company. Finally, our analysis is the first analysis which applies portfolio based methods to measure customer performance, and it is shown how these performance measures complement the optimization analysis.
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
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.
Most of the existing portfolio selection models are based on the probability theory. Though they often deal with the uncertainty via probabilistic - proaches, we have to mention that the probabilistic approaches only partly capture the reality. Some other techniques have also been applied to handle the uncertainty of the ?nancial markets, for instance, the fuzzy set theory [Zadeh (1965)]. In reality, many events with fuzziness are characterized by probabilistic approaches, although they are not random events. The fuzzy set theory has been widely used to solve many practical problems, including ?nancial risk management. By using fuzzy mathematical approaches, quan- tative analysis, qualitative analysis, the experts’ knowledge and the investors’ subjective opinions can be better integrated into a portfolio selection model. The contents of this book mainly comprise of the authors’ research results for fuzzy portfolio selection problems in recent years. In addition, in the book, the authors will also introduce some other important progress in the ?eld of fuzzy portfolio optimization. Some fundamental issues and problems of po- folioselectionhavebeenstudiedsystematicallyandextensivelybytheauthors to apply fuzzy systems theory and optimization methods. A new framework for investment analysis is presented in this book. A series of portfolio sel- tion models are given and some of them might be more e?cient for practical applications. Some application examples are given to illustrate these models by using real data from the Chinese securities markets.
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
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. 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.
Although emerging market economies consist of 50% of the global population, they are relatively unknown. Filling this knowledge gap, Emerging Markets: Performance, Analysis and Innovation compiles the latest research by noteworthy academics and money managers from around the world. With a focus on both traditional emerging markets and new areas, such as the Balkan, Middle East, and North African regions, it looks at how these markets can serve as drivers of portfolios and a significant force over the long term. This noteworthy collection sheds some light on what lies ahead for emerging markets with the most up-to-date research from academics and practitioners. It covers general issues in emerging markets and provides in-depth studies of regional markets experiencing transition, including the European Union, Latin America, and the Middle East. The book also explores Asian and Indian markets as well as financial instruments, such as bonds and funds, relative to these markets. It concludes with chapters on regulations, corporate governance, and corruption.
This book combines academic research and practical expertise on alternative assets and trading strategies in a unique way. The asset classes that are discussed include: credit risk, cross-asset derivatives, energy, private equity, freight agreements, real alternative assets (RAA), and socially responsible investments (SRI). The coverage on trading and investment strategies are directed at portfolio insurance, especially constant proportion portfolio insurance (CPPI) and constant proportion debt obligation (CPDO) strategies, robust portfolio optimization, and hedging strategies for exotic optio
Optimization of a portfolio involves the efficient allocation of assets given a specific goal and its application to a portfolio will improve performance to some degree. Combining alpha calculation and analysis with optimization may enhance this improved portfolio performance. Thus, it can be assumed that portfolio rebalancing is worth the cost over time. This relationship may be explained by the significance of a security's alpha and the goals of an optimization scenario.
This handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U.S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U.S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U.S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.