Publications
Publications
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- All HBS Web
(59)
- Faculty Publications (16)
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- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
- April 12, 2022
- Article
Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States
By: Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li and et al.
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models... View Details
- December 2021
- Article
Entrepreneurial Learning and Strategic Foresight
By: Aticus Peterson and Andy Wu
We study how learning by experience across projects affects an entrepreneur's strategic foresight. In a quantitative study of 314 entrepreneurs across 722 crowdfunded projects supplemented with a program of qualitative interviews, we counterintuitively find that... View Details
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the... View Details
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- 2021
- Working Paper
Entrepreneurial Learning and Strategic Foresight
By: Aticus Peterson and Andy Wu
We study how learning by experience across projects affects an entrepreneur's strategic foresight. In a quantitative study of 314 entrepreneurs across 722 crowdfunded projects supplemented with a program of qualitative interviews, we counterintuitively find that... View Details
- September 2020
- Article
Analyst Forecast Bundling
By: Michael Drake, Peter Joos, Joseph Pacelli and Brady Twedt
Changing economic conditions over the past two decades have created incentives for sell-side analysts to both provide their institutional clients tiered services and to streamline their written research process. One manifestation of these changes is an increased... View Details
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
- October 2018
- Article
The Operational Value of Social Media Information
By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details
- 2025
- Working Paper
Government-Brokerage Analysts and Market Stabilization: Evidence from China
By: Sheng Cao, Xianjie He, Charles C.Y. Wang and Huifang Yin
We show analysts at government-controlled brokerage firms serve as a market stabilization tool
in China. Using earnings forecasts from 2005–2019, we find government-brokerage analysts
issue relatively more optimistic—yet less accurate and timely—forecasts during... View Details
- 2019
- Working Paper
The Wisdom of Crowds in Operations: Forecasting Using Prediction Markets
By: Achal Bassamboo, Ruomeng Cui and Antonio Moreno
Prediction is an important activity in various business processes, but it becomes difficult when historical information is not available, such as forecasting demand of a new product. One approach that can be applied in such situations is to crowdsource opinions from... View Details
- 2010
- Working Paper
When Do Analysts Add Value? Evidence from Corporate Spinoffs
By: Emilie Rose Feldman, Stuart Gilson and Belen Villalonga
We investigate the information content and forecast accuracy of 1,793 analyst reports written around 62 spinoffs—a setting in which analysts' ability to inform investors is potentially very high. We find that analysts pay little attention to subsidiaries about to be... View Details
- January 2008 (Revised July 2009)
- Case
Forecasting the Great Depression
What is proper role of professional economic forecasting in financial decision making? The case presents excerpts from three leading economic forecasters on the eve of, and just after, the stock market crash of October 1929. The first set of excerpts is from Roger... View Details
- Article
Learning and Equilibrium as Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games
By: Ido Erev, Alvin E. Roth, R. Slonim and Greg Barron
- May 2006
- Article
Detection Defection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models
By: Scott Neslin, Sunil Gupta, Wagner Kamakura, Junxiang Lu and Charlotte Mason