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  • All HBS Web  (2,884)
    • News  (476)
    • Research  (2,210)
    • Events  (43)
    • Multimedia  (14)
  • Faculty Publications  (1,424)

Show Results For

  • All HBS Web  (2,884)
    • News  (476)
    • Research  (2,210)
    • Events  (43)
    • Multimedia  (14)
  • Faculty Publications  (1,424)
← Page 7 of 2,884 Results →
  • Article

Mining Big Data to Extract Patterns and Predict Real-Life Outcomes

By: Michal Kosinki, Yilun Wang, Himabindu Lakkaraju and Jure Leskovec
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Kosinki, Michal, Yilun Wang, Himabindu Lakkaraju, and Jure Leskovec. "Mining Big Data to Extract Patterns and Predict Real-Life Outcomes." Psychological Methods 21, no. 4 (December 2016): 493–506.
  • 21 Jun 2013 - 22 Jun 2013
  • Conference Presentation

Stock Market Prediction via Social Media: The Importance of Competitors

By: Frank Nagle
Citation
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Nagle, Frank. "Stock Market Prediction via Social Media: The Importance of Competitors." Paper presented at the 11th ZEW Conference on the Economics of Information and Communication Technologies, Center for European Economic Research (ZEW), Mannheim, Germany, June 21–22, 2013.
  • Link

Machine Learning Models for Prediction of Scope 3 Carbon Emissions

  • 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... View Details
Keywords: Bubble; Market Efficiency; Predictability; Price Bubble; Stocks; Price; Forecasting and Prediction
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Greenwood, Robin, Andrei Shleifer, and Yang You. "Bubbles for Fama." Journal of Financial Economics 131, no. 1 (January 2019): 20–43. (Internet Appendix Here.)
  • 16 Mar 2018
  • Working Paper Summaries

Amount and Diversity of Digital Emotional Expression Predicts Happiness

Keywords: by Laura Vuillier, Alison Wood Brooks, June Gruber, Rui Sun, Michael I. Norton, Matthew James Samson, Emiliana Simon-Thomas, Paul Piff, Sarah Fan, Jordi Quoidbach, Charles Gorintin, Pete Fleming, Arturo Bejar, and Dacher Keltner
  • July– September 2002
  • Article

Predictive Value and the Usefulness of Game Theoretic Models

By: Ido Erev, Alvin E. Roth, Robert L. Slonim and Greg Barron
Keywords: Value; Games, Gaming, and Gambling; Theory
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Erev, Ido, Alvin E. Roth, Robert L. Slonim, and Greg Barron. "Predictive Value and the Usefulness of Game Theoretic Models." International Journal of Forecasting 18, no. 3 (July– September 2002): 359–368.
  • May 2018
  • Article

The Amount and Source of Millionaires' Wealth (Moderately) Predicts Their Happiness

By: Grant Edward Donnelly, Tianyi Zheng, Emily Haisley and Michael I. Norton
Two samples of more than 4,000 millionaires reveal two primary findings. First, only at high levels of wealth—in excess of $8 million (Study 1) and $10 million (Study 2)—are wealthier millionaires happier than millionaires with lower levels of wealth, though these... View Details
Keywords: Income; Well-being; Happiness; Wealth; Money; Attitudes; Situation or Environment
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Donnelly, Grant Edward, Tianyi Zheng, Emily Haisley, and Michael I. Norton. "The Amount and Source of Millionaires' Wealth (Moderately) Predicts Their Happiness." Personality and Social Psychology Bulletin 44, no. 5 (May 2018): 684–699.
  • 21 Oct 2015
  • Research & Ideas

How to Predict if a New Business Idea is Any Good

other once unlikely, now successful startups (LinkedIn similarly got more than 20 rejections back in 2003) seem to beg: How do you tell a good idea from a bad one? “With startups, especially high-growth startups, it’s extremely hard to View Details
Keywords: by Michael Blanding; Accommodations; Financial Services
  • 31 May 2023
  • Research & Ideas

With Predictive Analytics, Companies Can Tap the Ultimate Opportunity: Customers’ Routines

If knowing what customers need is marketing gold, pinpointing exactly when they need it may just be platinum. Services that become part of a customer’s routine may deliver advantages beyond repeat business for a company, Harvard Business School Associate Professor Eva... View Details
Keywords: by Rachel Layne; Transportation
  • 2023
  • Working Paper

Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development

By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial intelligence and machine learning business applications. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms)... View Details
Keywords: Analytics and Data Science
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Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)

    Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development

    Predictive model development is understudied despite its importance to modern businesses. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms) as the primary driver of model quality, the value of... View Details
    • April 2024
    • Article

    A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification

    By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
    Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
    Keywords: Health Disorders; Health Testing and Trials; AI and Machine Learning; Health Industry
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    Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
    • October 2023
    • Teaching Note

    Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs

    By: Jonas Heese and Jung Koo Kang
    Teaching Note for HBS Case No. 123-042. View Details
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    Heese, Jonas, and Jung Koo Kang. "Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs." Harvard Business School Teaching Note 124-039, October 2023.
    • November 2023
    • Article

    Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success

    By: Yael Millgram, Matthew K. Nock, David D. Bailey and Amit Goldenberg
    People’s ability to regulate emotions is crucial to healthy emotional functioning. One overlooked aspect in emotion-regulation research is that knowledge about the source of emotions can vary across situations and individuals, which could impact people’s ability to... View Details
    Keywords: Emotions; Personal Characteristics; Well-being
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    Millgram, Yael, Matthew K. Nock, David D. Bailey, and Amit Goldenberg. "Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success." Psychological Science 34, no. 11 (November 2023): 1244–1255.
    • December 2023
    • Supplement

    Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs

    By: Jonas Heese and Jung Koo Kang
    Citation
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    Heese, Jonas, and Jung Koo Kang. "Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs." Harvard Business School Spreadsheet Supplement 124-708, December 2023.
    • Article

    Interpretable Decision Sets: A Joint Framework for Description and Prediction

    By: Himabindu Lakkaraju, Stephen H. Bach and Jure Leskovec
    Citation
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    Lakkaraju, Himabindu, Stephen H. Bach, and Jure Leskovec. "Interpretable Decision Sets: A Joint Framework for Description and Prediction." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 22nd (2016).
    • June 2023
    • Case

    Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs

    By: Jonas Heese, Jung Koo Kang and James Weber
    The case examines the accounting for loan losses at a large bank, how a bank sets its Allowance for Loan and Lease Losses (ALLL) on its financial statements. ALLL, and the rules that set them, determine when banks would and would not extend loans, which significantly... View Details
    Keywords: Accounting Standards; Accrual Accounting; Financial Statements; Financial Reporting; Banks and Banking; Financing and Loans; Banking Industry; United States
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    Heese, Jonas, Jung Koo Kang, and James Weber. "Accounting for Loan Losses at JPMorgan Chase: Predicting Credit Costs." Harvard Business School Case 123-042, June 2023.
    • 1996
    • Other Unpublished Work

    Testing for Structural Change in the Predictability of Asset Returns

    By: Luis M. Viceira
    Citation
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    Viceira, Luis M. "Testing for Structural Change in the Predictability of Asset Returns." 1996.
    • 10 Aug 2013 - 13 Aug 2013
    • Conference Presentation

    Stock Market Prediction via Social Media: The Importance of Competitors

    By: Frank Nagle
    Citation
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    Nagle, Frank. "Stock Market Prediction via Social Media: The Importance of Competitors." Paper presented at the Academy of Management Annual Meeting, Lake Buena Vista, FL, August 10–13, 2013.

      5 Predictions for America's Small Businesses in the Biden Era

      Karen Mills, the SBA Administrator under President Barack Obama, says the tea leaves suggest small business will be key to Biden’s economic agenda. View Details
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