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Climate Investing
This edited book consists of a collection of original articles written by leading industry and academic experts in the area of climate investing. The chapters introduce the reader to some of the latest research developments in the area of low-carbon investing and climate change solutions. Each chapter deals with new methods for estimating portfolio carbon footprints, constructing Paris-aligned equity and multi-asset portfolios and hedging climate risks. This title will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge and understanding of climate investing.
Machine Learning for Asset Management
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Multi-moment Asset Allocation and Pricing Models
While mainstream financial theories and applications assume that asset returns are normally distributed and individual preferences are quadratic, the overwhelming empirical evidence shows otherwise. Indeed, most of the asset returns exhibit “fat-tails” distributions and investors exhibit asymmetric preferences. These empirical findings lead to the development of a new area of research dedicated to the introduction of higher order moments in portfolio theory and asset pricing models. Multi-moment asset pricing is a revolutionary new way of modeling time series in finance which allows various degrees of long-term memory to be generated. It allows risk and prices of risk to vary through tim...
Risk-Based and Factor Investing
This book is a compilation of recent articles written by leading academics and practitioners in the area of risk-based and factor investing (RBFI). The articles are intended to introduce readers to some of the latest, cutting edge research encountered by academics and professionals dealing with RBFI solutions. Together the authors detail both alternative non-return based portfolio construction techniques and investing style risk premia strategies. Each chapter deals with new methods of building strategic and tactical risk-based portfolios, constructing and combining systematic factor strategies and assessing the related rules-based investment performances. This book can assist portfolio managers, asset owners, consultants, academics and students who wish to further their understanding of the science and art of risk-based and factor investing. - Contains up-to-date research from the areas of RBFI - Features contributions from leading academics and practitioners in this field - Features discussions of new methods of building strategic and tactical risk-based portfolios for practitioners, academics and students
Machine Learning and Data Sciences for Financial Markets
Learn how cutting-edge AI and data science techniques are integrated in financial markets from leading experts in the industry.
Factor Investing
This new edited volume consists of a collection of original articles written by leading industry experts in the area of factor investing.The chapters introduce readers to some of the latest research developments in the area of equity and alternative investment strategies.Each chapter deals with new methods for constructing and harvesting traditional and alternative risk premia, building strategic and tactical multifactor portfolios, and assessing related systematic investment performances. This volume will be of help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge and understanding of systematic risk factor investing. A practical scope An extensive coverage and up-to-date researcch contributions Covers the topic of factor investing strategies which are increasingly popular amongst practitioners
Postmodern Portfolio Theory
This survey of portfolio theory, from its modern origins through more sophisticated, “postmodern” incarnations, evaluates portfolio risk according to the first four moments of any statistical distribution: mean, variance, skewness, and excess kurtosis. In pursuit of financial models that more accurately describe abnormal markets and investor psychology, this book bifurcates beta on either side of mean returns. It then evaluates this traditional risk measure according to its relative volatility and correlation components. After specifying a four-moment capital asset pricing model, this book devotes special attention to measures of market risk in global banking regulation. Despite the defi...
Asset Allocation im Private Banking
„Asset Allocation im Private Banking“, die Vermögensanlage wohlhabender Privatkunden, berücksichtigt persönliche Interessen und Zugang zu traditionellen und alternativen Anlageklassen. Methoden des modernen Anlage- und Risikomanagements nutzen passende Kennzahlen, Szenarioplanung zur Modellierung der Zukunft und fortschrittliche Optimierungsverfahren in der Portfolioselektion. Schwächen traditioneller Verfahren, wie die der Mean-Variance-Optimierung nach Markowitz, werden behoben. Zusammenfassend wird ein Entscheidungsmodell für wohlhabende Privatkunden zur strategischen (langfristigen) Aufteilung des Vermögens auf verschiedene Anlagen entwickelt. Die Vielfalt und Komplexität alte...
Signed path dependence in financial markets
In Signed path dependence in financial markets: Applications and implications, computer scientist and academic Fabio Dias delves into cutting-edge techniques at the intersection of machine learning, time series analysis, and finance. This comprehensive guide bridges theory and application, offering readers insights into predictive modeling, algorithmic trading, and the nuanced dynamics of option pricing. Dias combines rigorous econometric methods with hands-on machine learning approaches, presenting a toolkit for anyone looking to leverage data-driven insights to navigate and predict complex financial markets. An essential read for practitioners, researchers, and students of financial engineering and quantitative finance.
