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- All HBS Web (233)
- Faculty Publications (106)
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- 2003
- Article
The Influence of Culture and Corporate Governance on the Characteristics that Distinguish Superior Analysts
By: Michael B. Clement, Lynn Rees and Edward Swanson
We identify characteristics of financial analysts that have been shown to be associated with relative forecast accuracy in the United States and examine these characteristics within 10 countries. We find that relative forecast accuracy is influenced by years of... View Details
Clement, Michael B., Lynn Rees, and Edward Swanson. "The Influence of Culture and Corporate Governance on the Characteristics that Distinguish Superior Analysts." Journal of Accounting, Auditing & Finance 18, no. 4 (2003): 593–618.
- 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
Keywords: Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data; Accounting; Economic Growth; Forecasting and Prediction; Analytics and Data Science
Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
- 2025
- Working Paper
Mammography - Early Detection, Precise Diagnoses: Case Histories of Transformational Advances
By: Amar Bhidé, Srikant M. Datar and Katherine Stebbins
This case history describes how the development of x-ray-based techniques and equipment (“mammography”) led to widespread screening for breast cancer and enabled “minimally invasive” biopsies of breast tumors. Specifically, we chronicle how: 1) new protocols and... View Details
Keywords: Health Care and Treatment; Technological Innovation; Innovation Strategy; Technology Adoption; Collaborative Innovation and Invention; Innovation and Invention; Governing Rules, Regulations, and Reforms
Bhidé, Amar, Srikant M. Datar, and Katherine Stebbins. "Mammography - Early Detection, Precise Diagnoses: Case Histories of Transformational Advances." Harvard Business School Working Paper, No. 20-002, July 2019. (Revised January 2025.)
- December 2007
- Article
The Roles of Task-Specific Experience and Innate Ability in Understanding Analyst Performance
By: Michael B. Clement, Lisa Koonce and Thomas Lopez
Considerable debate exists about what analyst experience measures and whether analysts learn from their experiences. Extant research has argued that once innate ability is considered, analysts’ general and firm-specific experiences are not relevant to understanding... View Details
Keywords: Experience and Expertise; Learning; Performance Evaluation; Forecasting and Prediction; Financial Services Industry
Clement, Michael B., Lisa Koonce, and Thomas Lopez. "The Roles of Task-Specific Experience and Innate Ability in Understanding Analyst Performance." Journal of Accounting & Economics 44, no. 3 (December 2007): 378–398.
- 22 Feb 2021
- Working Paper Summaries
Auditor Independence and Outsourcing: Aligning Incentives to Mitigate Shilling and Shirking
- Research Summary
Thin Slices of Teams with Professor Jeff Polzer, Patricia Satterstrom, and Lisa Kwan
How do people evaluate team effectiveness from short observations of interactions among team members? What are the cues people take in in such narrow windows of experience? What contributes to the accuracy of evaluations based on thin slices of... View Details
- 12 Feb 2018
- Working Paper Summaries
Private Equity, Jobs, and Productivity: Reply to Ayash and Rastad
- 26 Apr 2017
- Working Paper Summaries
Assessing the Quality of Quality Assessment: The Role of Scheduling
- 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
Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details
Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- February 2019 (Revised July 2025)
- Case
Theranos: Who Has Blood on Their Hands? (A)
By: Nien-hê Hsieh, Christina R. Wing, Emilie Fournier and Anna Resman
This case covers the rise and fall of Theranos, the company founded by Elizabeth Holmes in 2004 to revolutionize the blood testing industry by creating a device that could provide from a small finger prick the same results and accuracy as intravenous blood draws. As... View Details
Keywords: Health Testing and Trials; Corporate Accountability; Organizational Culture; Misleading and Fraudulent Advertising; Crime and Corruption; Ethics; Entrepreneurship; Lawsuits and Litigation
Hsieh, Nien-hê, Christina R. Wing, Emilie Fournier, and Anna Resman. "Theranos: Who Has Blood on Their Hands? (A)." Harvard Business School Case 619-039, February 2019. (Revised July 2025.)
- April 2003 (Revised March 2009)
- Case
Operational Execution at Arrow Electronics
By: Ananth Raman and Zeynep Ton
Distribution center operations (from order taking to order fulfillment) and the importance of attending to process details at Arrow Electronics, a large distributor of electronic components and computer products are described. The case also details the actions the... View Details
Raman, Ananth, and Zeynep Ton. "Operational Execution at Arrow Electronics." Harvard Business School Case 603-127, April 2003. (Revised March 2009.)
- 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
Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- Article
Bringing Probability Judgments into Policy Debates via Forecasting Tournaments
By: Philip E. Tetlock, Barbara A. Mellers and J. Peter Scoblic
Political debates often suffer from vague-verbiage predictions that make it difficult to assess accuracy and improve policy. A tournament sponsored by the U.S. intelligence community revealed ways in which forecasters can better use probability estimates to make... View Details
Keywords: Tournaments; Politics; Depolarization; Knowledge Creation; Forecasting and Prediction; Government and Politics
Tetlock, Philip E., Barbara A. Mellers, and J. Peter Scoblic. "Bringing Probability Judgments into Policy Debates via Forecasting Tournaments." Science 355, no. 6324 (February 3, 2017): 481–483.
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords: Economics Of AI; Machine Learning; Non-stationarity; Perishability; Value Depreciation; Analytics and Data Science; Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)
- Research Summary
Sell-Side Analysts and Corporate Spinoffs
This study investigates the information content and accuracy of analyst reports written about companies that are about to undertake equity spinoffs. This research is among the first to provide a detailed look at the extent to which analysts evaluate upcoming... View Details
- September 2018 (Revised December 2019)
- Case
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making... View Details
Keywords: Radiology; Machine Learning; X-ray; CT Scan; Medical Technology; Probability; FDA 510(k); Diagnosis; Business Startups; Health Care and Treatment; Information Technology; Applications and Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel
Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)
- 2014
- Working Paper
Corporate Financial Policies in Misvalued Credit Markets
By: Jarrad Harford, Marc Martos-Vila and Matthew Rhodes-Kropf
We theoretically and empirically investigate the repercussions of credit market misvaluation for a firm's borrowing and investment decisions. Using an ex-post measure of the accuracy of credit ratings to capture debt market misvaluation, we find evidence that firms... View Details
Harford, Jarrad, Marc Martos-Vila, and Matthew Rhodes-Kropf. "Corporate Financial Policies in Misvalued Credit Markets." Harvard Business School Working Paper, No. 14-097, April 2014.
- February 2011
- Article
Understanding Analysts’ Use and Under-use of Stock Returns and Other Analysts’ Forecasts when Forecasting Earnings
By: Michael B. Clement, Jeffrey Hales and Yanfeng Xue
We investigate analysts' use of stock returns and other analysts' forecast revisions in revising their own forecasts after an earnings announcement. We find that analysts respond more strongly to these signals when the signals are more informative about future earnings... View Details
Keywords: Learning; Forecasting and Prediction; Performance Evaluation; Knowledge Use and Leverage; Financial Services Industry
Clement, Michael B., Jeffrey Hales, and Yanfeng Xue. "Understanding Analysts’ Use and Under-use of Stock Returns and Other Analysts’ Forecasts when Forecasting Earnings." Journal of Accounting & Economics 51, nos. 1-2 (February 2011): 279–299.