Kritzman Mark P. Livres

Discover a masterful exploration of the fallacies and challenges of asset allocation In Asset Allocation: From Theory to Practice and Beyond --the newly and substantially revised Second Edition of A Practitioner's Guide to Asset Allocation --accomplished finance professionals William Kinlaw, Mark P. Kritzman, and David Turkington deliver a robust and insightful exploration of the core tenets of asset allocation. Drawing on their experience working with hundreds of the world's largest and most sophisticated investors, the authors review foundational concepts, debunk fallacies, and address cutting-edge themes like factor investing and scenario analysis. The new edition also includes references to related topics at the end of each chapter and a summary of key takeaways to help readers rapidly locate material of interest. The book also incorporates discussions of: The characteristics that define an asset class, including stability, investability, and similarity The fundamentals of asset allocation, including definitions of expected return, portfolio risk, and diversification Advanced topics like factor investing, asymmetric diversification, fat tails, long-term investing, and enhanced scenario analysis as well as tools to address challenges such as liquidity, rebalancing, constraints, and within-horizon risk. Perfect for client-facing practitioners as well as scholars who seek to understand practical techniques, Asset Allocation: From Theory to Practice and Beyond is a must-read resource from an author team of distinguished finance experts and a forward by Nobel prize winner Harry Markowitz

This book is a groundbreaking resource for financial analysts, researchers, and data scientists, encouraging a reevaluation of classical statistics and prediction methods. The authors, Czasonis, Kritzman, and Turkington, present a novel approach to analyzing data, emphasizing the identification of patterns among various attributes and introducing the crucial concept of relevance. They demonstrate how to leverage relevance for making predictions and discuss measuring confidence in these predictions by balancing relevance against noise. The text applies this innovative perspective to assess the effectiveness of prediction models across diverse fields and hints at extending this statistical framework into machine learning. Throughout, the authors offer engaging biographical insights into key historical scientists whose work laid the groundwork for their ideas on relevance and prediction. Each chapter focuses on conceptual understanding, relying on intuition while underscoring essential takeaways that reshape the notion of prediction. The mathematical backing is accessible, allowing readers to engage with the prose without delving into complex math. This dual approach caters to different preferences, appealing to both those who appreciate poetic language and those inclined toward mathematical rigor. While some may challenge the book's insights on relevance, the authors invite ongoing debate and intellectual growth in the realm of