Prediction
Oct 9, 2025
A Transparent Alternative to Neural Networks
By Megan Czasonis, Mark Kritzman and David Turkington We show that relevance-based prediction captures complex relationships, like a neural network, but with the added benefit of transparency. Many prediction tasks in economics and finance lie beyond the reach of linear regression analysis. Researchers, therefore, often turn to machine learning techniques, such as neural networks, to address these complex dynamics. A neural network has the potential to extract nearly all the useful information ...
Jul 23, 2025
Relevance-Based Importance
By Megan Czasonis, Mark Kritzman, and David Turkington As the race to design sophisticated data analytics continues, we show why relevance-based prediction offers an ideal way to measure the importance of an input variable to a prediction. T-statistics act as a hallmark for rigor by pinpointing the effect of a single variable and distinguishing signal from noise. However, they have significant limitations: (1) t-stats do not capture ‘shared’ information, (2) t-stats are not prediction-specific, ...
Apr 9, 2025
The Virtue of Transparency for Prediction
By Megan Czasonis, Mark Kritzman and David Turkington We show how our method of relevance-based prediction implements similar logic to a highly complex machine learning model, but relevance is extremely transparent. What is the best way to form predictions from a data sample? This is a big question, but at its core lies a fundamental tension between explaining the past and anticipating the future. Predictions can fail by paying too little attention to the past (underfitting) or by paying too ...
Sep 7, 2023
An Intuitive Guide to Relevance-Based Prediction
By Megan Czasonis, Mark Kritzman, and David Turkington Relevance-based prediction is a new approach to data-driven forecasting that serves as a favorable alternative to both linear regression analysis and machine learning. It follows from two seminal scientific innovations: Prasanta Mahalanobis’ distance measure and Claude Shannon’s information theory. Relevance-based prediction rests on three key tenets: 1) relevance, which measures the importance of an observation to a prediction; 2) fit, ...
Dec 21, 2022
Relevance-Based Prediction
By Megan Czasonis, Mark Kritzman, and David Turkington Published in the Journal of Financial Data Science, Winter 2023, and recipient of the 2023 Roger F. Murray First Place Prize Award. Statistics and intuition converge with the concept of “relevance”. We introduce a prediction system based on assessing the relevance of prior outcomes for future predictions, and describe the advantage it brings to both simple and complex quant models. It is hard to make good predictions about the future, ...
Feb 4, 2022
Relevance
By Megan Czasonis, Mark Kritzman, and David Turkington Published in the Journal of Investment Management, First Quarter 2022 and recipient of the 2022 Harry Markowitz Award. People learn from experience and extrapolate from the relevant past to predict the future. Data-driven regression models do the same thing. To know why, we need to shift our perspective on data. Modern statistics focus on variables: carefully selecting the right ones, measuring their impact and testing their significance. ...
Jan 24, 2022
Investable and Interpretable Machine Learning for Equities
By Yimou Li, Zachary Simon, and David Turkington. Published in the Journal of Financial Data Science, Winter 2022. We put the stock-selection skill of machine learning models to the test, with an intense focus on making sure their selections are both investable and interpretable - and therefore, believable. Imagine a line that shows remarkably stable investment performance outpacing the historical returns of nearly every mutual fund and known quantitative strategy. In a nutshell, this is the ...
Sep 1, 2020
Addition by Subtraction: A Better Way to Forecast Factor Returns (and Everything Else)
By Megan Czasonis, Mark Kritzman, and David Turkington. Published in the Journal of Portfolio Management, September 2020. Similar to how economists might think about past events, regression models consider historical relevance when generating predictions. Censoring the least relevant periods can improve their predictive power. Any introductory statistics course teaches that when it comes to regression analysis, the more data the better. This is because larger samples should produce more reliable ...
Jan 1, 2020
Beyond the Black Box: An Intuitive Approach to Investment Prediction with Machine Learning
By Yimou Li, David Turkington, and Alireza Yazdani. Published in the Journal of Financial Data Science, Winter 2020. We introduce a framework that demystifies how machine learning models “think” about investing. Machine learning (ML) enables powerful algorithms to analyze financial data in new and exciting ways. But this excitement is often tempered by fear that investors don’t really understand why a model behaves the way it does. We need to move beyond this “black box” stigma. We propose a framework ...
Sep 1, 2017
Facts about Factors
By Paula Cocoma, Megan Czasonis, Mark Kritzman, and David Turkington. Published in the Journal of Portfolio Management, Special Issue 2017. We challenge the notion that factors are superior to assets as the building blocks for forming portfolios.
1. Peter L. Bernstein Award for Best Article in an Institutional Investor Journal in 2013; Bernstein-Fabozzi/Jacobs-Levy Award for Outstanding Article in the Journal of Portfolio Management in 2006, 2009, 2011, 2013 (2), 2014, 2015, 2016, 2021; Graham & Dodd Scroll Award for article in the Financial Analysts Journal in 2002 and 2010. Roger F. Murray First Prize for Research Presented at the Q Group Conference in 2012, 2021, 2023. Harry M. Markowitz Award for Best Paper in the Journal of Investment Management in 2022, 2023. Doriot Award for Best Private Equity Research Paper in 2022.