David Turkington – författare

A thought-provoking and startlingly insightful reworking of the science of prediction In Prediction Revisited: The Importance of Observation, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance. The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. Prediction Revisited also offers: Clarifications of commonly accepted but less commonly understood notions of statisticsInsight into the efficacy of traditional prediction models in a variety of fieldsColorful biographical sketches of some of the key prediction scientists throughout historyMutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.

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 similarityThe fundamentals of asset allocation, including definitions of expected return, portfolio risk, and diversificationAdvanced 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.

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.

A thought-provoking and startlingly insightful reworking of the science of prediction

In Prediction Revisited: The Importance of Observation, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance.

The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. Prediction Revisited also offers:

Clarifications of commonly accepted but less commonly understood notions of statistics Insight into the efficacy of traditional prediction models in a variety of fields Colorful biographical sketches of some of the key prediction scientists throughout history Mutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within

With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.