Kritzman Mark P. Books

Discover a masterful exploration of the fallacies and challenges of asset allocation in this newly revised Second Edition of a practitioner's guide. Accomplished finance professionals William Kinlaw, Mark P. Kritzman, and David Turkington provide a robust examination of the core tenets of asset allocation. Drawing on their extensive experience with some of the world's largest and most sophisticated investors, the authors review foundational concepts, debunk common fallacies, and tackle cutting-edge themes like factor investing and scenario analysis. This edition features references to related topics at the end of each chapter and a summary of key takeaways for quick material access. The book discusses the defining characteristics of asset classes, including stability, investability, and similarity, as well as the fundamentals of asset allocation—expected return, portfolio risk, and diversification. It also covers advanced topics such as asymmetric diversification, fat tails, long-term investing, and enhanced scenario analysis, along with tools to address challenges like liquidity, rebalancing, constraints, and within-horizon risk. This resource is essential for client-facing practitioners and scholars seeking practical techniques, supported by insights from a team of distinguished finance experts and a foreword by Nobel laureate 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