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- October 2023 (Revised January 2024)
- Case
Ball: EVA Driving the World's Leading Can Manufacturer (A)
By: Jonas Heese and Susan Pinckney
The case describes Ball’s multi decade history of using Economic Value Added to drive decision making and workforce compensation. In 2016, the company acquired Rexam PLC and became the world’s leading metal beverage container company. Consumer demand for varied...
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Keywords:
Budgets and Budgeting;
Cost Accounting;
Financial Reporting;
Financial Statements;
Buildings and Facilities;
Green Building;
Mergers and Acquisitions;
Customer Satisfaction;
Decisions;
Forecasting and Prediction;
Machinery and Machining;
Asset Pricing;
Corporate Finance;
Capital;
Cost;
Financial Management;
Goods and Commodities;
Compensation and Benefits;
Executive Compensation;
Employee Relationship Management;
Goals and Objectives;
Resource Allocation;
Business Strategy;
Corporate Strategy;
Food and Beverage Industry;
United States;
Arizona;
California;
Texas
Heese, Jonas, and Susan Pinckney. "Ball: EVA Driving the World's Leading Can Manufacturer (A)." Harvard Business School Case 124-002, October 2023. (Revised January 2024.)
- September 2023 (Revised January 2024)
- Case
Forecasting Climate Risks: Aviva’s Climate Calculus
By: Mark Egan and Peter Tufano
In late 2021, Ben Carr, Director of Analytics and Capital Modeling at Aviva Plc (Aviva)—a leading insurer with core operations in the UK, Ireland and Canada,—was preparing for an upcoming presentation before the company's board which included its CEO, Amanda Blanc,...
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Keywords:
Climate Risk;
Climate Finance;
Forecasting;
Insurance;
Risk Measurement;
Climate Change;
Risk Management;
Forecasting and Prediction;
Insurance Industry;
United States
Egan, Mark, and Peter Tufano. "Forecasting Climate Risks: Aviva’s Climate Calculus." Harvard Business School Case 224-025, September 2023. (Revised January 2024.)
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false...
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Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 2023
- Working Paper
Evaluation and Learning in R&D Investment
By: Alexander P. Frankel, Joshua L. Krieger, Danielle Li and Dimitris Papanikolaou
We examine the role of spillover learning in shaping the value of exploratory versus incremental
R&D. Using data from drug development, we show that novel drug candidates generate more
knowledge spillovers than incremental ones. Despite being less likely to reach...
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Frankel, Alexander P., Joshua L. Krieger, Danielle Li, and Dimitris Papanikolaou. "Evaluation and Learning in R&D Investment." Harvard Business School Working Paper, No. 23-074, May 2023. (NBER Working Paper Series, No. 31290, May 2023.)
- 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...
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Keywords:
COVID-19;
Forecasting and Prediction;
Health Pandemics;
Mathematical Methods;
Partners and Partnerships
Cramer, Estee Y., Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li, and et al. "Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States." e2113561119. Proceedings of the National Academy of Sciences 119, no. 15 (April 12, 2022). (See full author list here.)
- January 2022 (Revised March 2022)
- Module Note
Analysis of Financial and Non-Financial Information for Forecasting Performance
This note describes the main themes and cases of a teaching module on the analysis of information from, and outside of, financial statements for forecasting firms’ future financial performance. The module’s pedagogical goal is to deepen students’ understanding of the...
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Wang, Charles C.Y. "Analysis of Financial and Non-Financial Information for Forecasting Performance." Harvard Business School Module Note 122-071, January 2022. (Revised March 2022.)
- September 2021 (Revised November 2022)
- Case
Community Solutions
By: Brian Trelstad and Tom Quinn
Community Solutions was an anti-homelessness nonprofit founded in 2011 after protagonist Rosanne Haggerty grew frustrated with the limited impact of traditional housing and outreach strategies. It set an ambitious goal, reached in some partner communities, of ending...
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Keywords:
Change;
Change Management;
Disruption;
Transformation;
Communication;
Communication Strategy;
Decision Making;
Cost vs Benefits;
Decision Choices and Conditions;
Decisions;
Forecasting and Prediction;
Social Entrepreneurship;
Ethics;
Values and Beliefs;
Capital Budgeting;
Capital Markets;
Country;
Government Administration;
Government Legislation;
Housing;
Disruptive Innovation;
Innovation and Invention;
Innovation Strategy;
Knowledge Sharing;
Leading Change;
Resource Allocation;
Mission and Purpose;
Performance Evaluation;
Performance Improvement;
Philanthropy and Charitable Giving;
Opportunities;
Social Enterprise;
Nonprofit Organizations;
Human Needs;
Public Opinion;
Social Issues;
Societal Protocols;
Poverty;
Welfare;
Well-being;
System;
Equality and Inequality;
Consulting Industry;
Real Estate Industry;
United States;
New York (city, NY);
Florida;
Texas
Trelstad, Brian, and Tom Quinn. "Community Solutions." Harvard Business School Case 322-021, September 2021. (Revised November 2022.)
- August 2021 (Revised February 2024)
- Case
Data Science at the Warriors
By: Iavor I. Bojinov and Michael Parzen
The case explores the development and early growth of a data science team at the Golden State Warriors, an NBA team based in San Francisco. The case begins by explaining the initial rationale for investing in data science, then covers a debate on the appropriate team...
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Keywords:
Data Science;
Digital Marketing;
Analysis;
Forecasting and Prediction;
Technological Innovation;
Information Technology;
Sports Industry;
San Francisco;
United States
Bojinov, Iavor I., and Michael Parzen. "Data Science at the Warriors." Harvard Business School Case 622-048, August 2021. (Revised February 2024.)
- June 2021
- Technical Note
Introduction to Linear Regression
By: Michael Parzen and Paul Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical...
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- 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...
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- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption...
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Keywords:
Machine Learning Models;
Algorithmic Recourse;
Decision Making;
Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- March 2019 (Revised May 2019)
- Case
Growth Investing at Totem Point
By: Suraj Srinivasan, Charles C.Y. Wang and Jonah Goldberg
The case describes the investment of hedge fund, Totem Point Management in Analog Semiconductors (ADI) as a way to discuss forecasting and valuation in growth companies. In June 2016, hedge fund Totem Point invested in ADI at around $55 a share. In general, Totem Point...
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Srinivasan, Suraj, Charles C.Y. Wang, and Jonah Goldberg. "Growth Investing at Totem Point." Harvard Business School Case 119-091, March 2019. (Revised May 2019.)
- January 2019
- Article
Bubbles for Fama
By: Robin Greenwood, Andrei Shleifer and Yang You
We evaluate Eugene Fama's claim that stock prices do not exhibit price bubbles. Based on U.S. industry returns 1926–2014 and international sector returns 1985–2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio...
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Keywords:
Bubble;
Market Efficiency;
Predictability;
Price Bubble;
Stocks;
Price;
Forecasting and Prediction
Greenwood, Robin, Andrei Shleifer, and Yang You. "Bubbles for Fama." Journal of Financial Economics 131, no. 1 (January 2019): 20–43. (Internet Appendix Here.)
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the...
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Keywords:
Data Science;
Analytics and Data Science;
Analysis;
Customers;
Household;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- August 2018 (Revised September 2018)
- Case
LendingClub (A): Data Analytic Thinking (Abridged)
By: Srikant M. Datar and Caitlin N. Bowler
LendingClub was founded in 2006 as an alternative, peer-to-peer lending model to connect individual borrowers to individual investor-lenders through an online platform. Since 2014 the company has worked with institutional investors at scale. While the company assigns...
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Keywords:
Data Science;
Data Analytics;
Investing;
Loans;
Investment;
Financing and Loans;
Analytics and Data Science;
Analysis;
Forecasting and Prediction;
Business Model
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (A): Data Analytic Thinking (Abridged)." Harvard Business School Case 119-020, August 2018. (Revised September 2018.)
- August 2018 (Revised September 2018)
- Supplement
LendingClub (B): Decision Trees & Random Forests
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
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Keywords:
Data Science;
Data Analytics;
Decision Trees;
Investment;
Financing and Loans;
Analytics and Data Science;
Analysis;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (B): Decision Trees & Random Forests." Harvard Business School Supplement 119-021, August 2018. (Revised September 2018.)
- August 2018 (Revised September 2018)
- Supplement
LendingClub (C): Gradient Boosting & Payoff Matrix
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default...
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Keywords:
Data Analytics;
Data Science;
Investment;
Financing and Loans;
Analytics and Data Science;
Analysis;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (C): Gradient Boosting & Payoff Matrix." Harvard Business School Supplement 119-022, August 2018. (Revised September 2018.)
- January 2018 (Revised January 2020)
- Case
People Analytics at McKinsey
By: Jeffrey T. Polzer and Olivia Hull
A private equity–backed fast food chain has hired McKinsey’s new People Analytics group to help it improve performance. As the final client workshop approaches, Associate Partner Alex DiLeonardo ponders the best way to present the team’s findings, especially those that...
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Keywords:
Talent and Talent Management;
Customer Relationship Management;
Forecasting and Prediction;
Cost Management;
Human Resources;
Employees;
Recruitment;
Retention;
Selection and Staffing;
Measurement and Metrics;
Performance;
Performance Capacity;
Performance Efficiency;
Performance Evaluation;
Performance Improvement;
Consulting Industry;
Service Industry
Polzer, Jeffrey T., and Olivia Hull. "People Analytics at McKinsey." Harvard Business School Case 418-023, January 2018. (Revised January 2020.)
- Article
Scenario Generation for Long Run Interest Rate Risk Assessment
By: Robert F. Engle, Guillaume Roussellet and Emil N. Siriwardane
We propose a statistical model of the term structure of U.S. treasury yields tailored for long-term probability-based scenario generation and forecasts. Our model is easy to estimate and is able to simultaneously reproduce the positivity, persistence, and factor...
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Keywords:
Forecasting;
Stress Testing;
Interest Rates;
Forecasting and Prediction;
Risk Management;
United States
Engle, Robert F., Guillaume Roussellet, and Emil N. Siriwardane. "Scenario Generation for Long Run Interest Rate Risk Assessment." Special Issue on Theoretical and Financial Econometrics: Essays in Honor of C. Gourieroux. Journal of Econometrics 201, no. 2 (December 2017): 333–347.
- October 2017 (Revised April 2018)
- Case
Improving Worker Safety in the Era of Machine Learning (A)
By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I...
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Keywords:
Machine Learning;
Policy Implementation;
Empirical Research;
Inspection;
Occupational Safety;
Occupational Health;
Regulation;
Analysis;
Forecasting and Prediction;
Policy;
Operations;
Supply Chain Management;
Safety;
Manufacturing Industry;
Construction Industry;
United States
Toffel, Michael W., Dan Levy, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (A)." Harvard Business School Case 618-019, October 2017. (Revised April 2018.)