Prediction Revisited - by Mark P Kritzman & David Turkington & Megan Czasonis (Hardcover)

About the Book

"Prediction Revisited is a ground-breaking book for financial analysts and researchers--as well as data scientists in other disciplines--to reconsider classical statistics and approaches to forming predictions. Czasonis, Kritzman, and Turkington lay out the foundations of their cutting-edge approach to observing information from data. And then characterize patterns between multiple attributes, soon introducing the key concept of relevance. They then show how to use relevance to form predictions, discussing how to measure confidence in predictions by considering the tradeoff between relevance and noise. Prediction Revisited applies this new perspective to evaluate the efficacy of prediction models across many fields and preview the extension of the authors' new statistical approach to machine learning. Along the way they provide colorful biographical sketches of some of the key scientists throughout history who established the theoretical foundation that underpins the authors' notion of relevance--and its importance to prediction. In each chapter, material is presented conceptually, leaning heavily on intuition, and highlighting the key takeaways reframe prediction conceptually. They back it up mathematically and introduce an empirical application of the key concepts to understand. (If you are strongly disinclined toward mathematics, you can pass by the math and concentrate only on the prose, which is sufficient to convey the key concepts of this book.) In fact, you can think of this book as two books: one written in the language of poets and one written in the language of mathematics. Some readers may view the book's key insight about relevance skeptically, because it calls into question notions about statistical analysis that are deeply entrenched in beliefs from earlier training. The authors welcome a groundswell of debate and advancement of thought about prediction."--

Book Synopsis

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.

From the Back Cover

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

In Prediction Revisited: The Importance of Observation, a team of renowned finance and risk experts at the top of their game describes a ground-breaking realignment of the connection between past experiences and future outcomes. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it maps out an elegant prediction system based on a novel measure of statistical relevance.

Drawing upon information theory and an obscure yet profound mathematical equivalence, the authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions. Additionally, they introduce a new and more nuanced measure of a prediction's reliability, enabling researchers to fine tune their responses to specific predictions.

Prediction Revisited also:

• Illuminates many commonly accepted but less commonly understood notions of statistics
• Reveals several valuable yet previously unrecognized mathematical equivalences
• Includes colorful biographical sketches of some of the key scientists whose contributions paved the path to relevance-based prediction
• Enables access to the mathematically minded reader as well as those who prefer an intuitive and conceptual discussion of the book's key ideas

With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is a must-read for anyone who aspires to reach a new level of understanding and mastery of data-driven prediction.

About the Author

MEGAN CZASONIS is Managing Director and Head of Portfolio Management Research at State Street Associates.

MARK KRITZMAN is a Founding Partner and CEO of Windham Capital Management. He is also a Founding Partner of State Street Associates and teaches a graduate course at the Massachusetts Institute of Technology.

DAVID TURKINGTON is Senior Managing Director and Head of State Street Associates.