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(3,273)
- News (508)
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- Faculty Publications (1,598)
Show Results For
- All HBS Web
(3,273)
- News (508)
- Research (2,495)
- Events (43)
- Multimedia (18)
- Faculty Publications (1,598)
- 2013
- Article
What Goes Up Must Come Down? Experimental Evidence on Intuitive Forecasting
By: John Beshears, James J. Choi, Andreas Fuster, David Laibson and Brigitte C. Madrian
Do laboratory subjects correctly perceive the dynamics of a mean-reverting time series? In our experiment, subjects receive historical data and make forecasts at different horizons. The time series process that we use features short-run momentum and long-run partial... View Details
Beshears, John, James J. Choi, Andreas Fuster, David Laibson, and Brigitte C. Madrian. "What Goes Up Must Come Down? Experimental Evidence on Intuitive Forecasting." American Economic Review: Papers and Proceedings 103, no. 3 (May 2013): 570–574.
- June 2014
- Supplement
Financial Policy at Apple, 2013 (B)
By: Mihir Desai and Elizabeth A. Meyer
This case is meant to accompany Financial Policy at Apple, 2013 (A) and details the results of Apple's Q2 2013 earnings call. View Details
Keywords: Apple; Steve Jobs; Forecast; Forecasting; Forecasting And Prediction; Shareholder Activism; Share Repurchase; Dividends; Financial Ratios; Preferred Shares; Cash Distribution; Corporate Finance; Borrowing and Debt; Financial Management; Financial Strategy; Technology Industry; Consumer Products Industry; United States; Republic of Ireland
Desai, Mihir, and Elizabeth A. Meyer. "Financial Policy at Apple, 2013 (B)." Harvard Business School Supplement 214-094, June 2014.
- August 1999
- Article
Positive Illusions and Biases of Prediction in Mutual Fund Investment Decisions
By: D. A. Moore, T. R. Kurtzberg, C. R. Fox and M. H. Bazerman
Moore, D. A., T. R. Kurtzberg, C. R. Fox, and M. H. Bazerman. "Positive Illusions and Biases of Prediction in Mutual Fund Investment Decisions." Organizational Behavior and Human Decision Processes 79, no. 2 (August 1999): 95–114.
- May 1981 (Revised June 1994)
- Case
Stride Rite: Demand Forecasting Process (A)
Schleifer, Arthur, Jr. "Stride Rite: Demand Forecasting Process (A)." Harvard Business School Case 181-122, May 1981. (Revised June 1994.)
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 1999
- Working Paper
Positive Illusions and Forecasting Errors in Mutual Fund Investment Decisions
By: Don A. Moore, Terri Kurtzberg, Craig R. Fox and Max Bazerman
- October 17, 2022
- Article
Relational Diversity in Social Portfolios Predicts Well-Being
By: Hanne K. Collins, Serena F. Hagerty, Jordi Quoidbach, Michael I. Norton and Alison Wood Brooks
We document a link between the relational diversity of one’s social portfolio—the richness and evenness of relationship types across one’s social interactions—and well-being. Across four distinct samples, respondents from the United States who completed a preregistered... View Details
Keywords: Social Interaction; Social Engagement; Well-being; Happiness; Social and Collaborative Networks; Family and Family Relationships
Collins, Hanne K., Serena F. Hagerty, Jordi Quoidbach, Michael I. Norton, and Alison Wood Brooks. "Relational Diversity in Social Portfolios Predicts Well-Being." Proceedings of the National Academy of Sciences 119, no. 43 (October 17, 2022).
- June 2014
- Supplement
Financial Policy at Apple, 2013 Student Supplement
By: Mihir Desai and Elizabeth A. Meyer
This is the student spreadsheet supplement to case 214-085, Financial Policy at Apple, 2013 (A). View Details
Keywords: Apple; Steve Jobs; Forecast; Forecasting; Forecasting And Prediction; Shareholder Activism; Share Repurchase; Dividends; Financial Ratios; Preferred Shares; Cash Distribution; Corporate Finance; Borrowing and Debt; Financial Management; Financial Strategy; Technology Industry; Consumer Products Industry; United States; Republic of Ireland
- 10 Dec 2015
- News
Can Google Street View Images Predict Household Income?
- 2016
- Chapter
Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity
By: Carolin E. Pflueger and Luis M. Viceira
Pflueger, Carolin E., and Luis M. Viceira. "Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity." Chap. 10 in Handbook of Fixed-Income Securities, edited by Pietro Veronesi, 191–209. Wiley Handbooks in Financial Engineering and Econometrics. Hoboken, NJ: John Wiley & Sons, 2016.
- January 1982
- Case
New York Telephone Co.: The 1975 Revenue Forecast
Schleifer, Arthur, Jr. "New York Telephone Co.: The 1975 Revenue Forecast." Harvard Business School Case 182-060, January 1982.
- November 1974 (Revised November 1977)
- Background Note
Developing Forecasts with the Aid of Regression Analysis
By: Paul A. Vatter
Vatter, Paul A. "Developing Forecasts with the Aid of Regression Analysis." Harvard Business School Background Note 175-105, November 1974. (Revised November 1977.)
- January 1982 (Revised May 1983)
- Case
CBS Record Co. (A): The Long-Range Industry Forecast
Schleifer, Arthur, Jr. "CBS Record Co. (A): The Long-Range Industry Forecast." Harvard Business School Case 182-182, January 1982. (Revised May 1983.)
- 01 Mar 2013
- Working Paper Summaries
Hurry Up and Wait: Differential Impacts of Congestion, Bottleneck Pressure, and Predictability on Patient Length of Stay
- 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.
- fall 1999
- Article
(Dis)Respecting versus (Dis)liking: Status and Interdepenences Predict Ambivalent Stereotypes of Competence and Warmth
By: S.T. Fiske, J. Xu, A.J.C. Cuddy and P. Glick
Fiske, S.T., J. Xu, A.J.C. Cuddy, and P. Glick. "(Dis)Respecting versus (Dis)liking: Status and Interdepenences Predict Ambivalent Stereotypes of Competence and Warmth." Journal of Social Issues 55, no. 3 (fall 1999): 473–490.
- June 7, 1990
- Article
New Trading Practices and the Short-run Predictability of the S&P 500
By: André Perold, Kenneth A. Froot and James F. Gammill Jr.
- 1992
- Chapter
Exchange Rate Forecasting Techniques, Survey Data, and Implications for the Foreign Exchange Market
By: J. Frankel and K. A. Froot
Keywords: Currencies; Exchange Rates; International Macroeconomics; Monetary Policy; Currency Controls; Fixed Exchange Rates; Floating Exchange Rates; Currency Bands; Currency Zones; Currency Areas; Rational Expectations; International Finance; Currency Exchange Rate; Asset Pricing; Forecasting and Prediction; Policy
Frankel, J., and K. A. Froot. "Exchange Rate Forecasting Techniques, Survey Data, and Implications for the Foreign Exchange Market." In International Business Reader, edited by D. Duta. London: Oxford University Press, 1992. (Revised from IMF Working Paper No. 90/43 and NBER Working Paper No. 3470, October 1990.)
- 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
Neslin, Scott, Sunil Gupta, Wagner Kamakura, Junxiang Lu, and Charlotte Mason. "Detection Defection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models." Journal of Marketing Research (JMR) 43, no. 2 (May 2006): 204–211.