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  • February 2005
  • Article

Financial Analyst Characteristics and Herding Behavior in Forecasting

By: Michael B. Clement and Senyo Tse
This study classifies analysts' earnings forecasts as herding or bold and finds that (1) boldness likelihood increases with the analyst's prior accuracy, brokerage size, and experience and declines with the number of industries the analyst follows, consistent with... View Details
Keywords: Experience and Expertise; Forecasting and Prediction; Performance Evaluation; Financial Services Industry
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  • 11 Jan 2007
  • Working Paper Summaries

A Perceptions Framework for Categorizing Inventory Policies in Single-stage Inventory Systems

Keywords: by Noel Watson
  • 2016
  • Working Paper

Bias in Official Fiscal Forecasts: Can Private Forecasts Help?

By: Jeffrey A. Frankel and Jesse Schreger
Government forecasts of GDP growth and budget balances are generally more over optimistic than private sector forecasts. When official forecasts are especially optimistic relative to private forecasts ex ante, they are more likely also to be over optimistic relative to... View Details
Keywords: Forecasting and Prediction; Macroeconomics
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  • 2013
  • Article

Boardroom Centrality and Firm Performance

By: David F. Larcker, Eric C. So and Charles C.Y. Wang
Firms with central or well-connected boards of directors earn superior risk-adjusted stock returns. Initiating a long position in the most central firms and a short position in the least central firms earns an average risk-adjusted return of 4.68% per year. Firms with... View Details
Keywords: Networks; Governing and Advisory Boards; Forecasting and Prediction; Performance
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  • 12 Jan 2009
  • Research & Ideas

The Value of a ‘Portable’ Career

organizations much like many others, subject to the errors that are common elsewhere. But their successes and failures are highly visible and amplified by the fact that they perform in a zero-sum world. In order for one team to win,... View Details
Keywords: by Martha Lagace; Sports
  • 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... View Details
Keywords: AI and Machine Learning; Forecasting and Prediction; Prejudice and Bias
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  • 2023
  • Working Paper

PRIMO: Private Regression in Multiple Outcomes

By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details
Keywords: Analytics and Data Science; Mathematical Methods
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  • Research Summary

Overview

By: Ethan C. Rouen
Relying on empirical archival methodologies—as well as techniques in data science—to develop and structure new sources of data by which to approach questions of looming disclosure changes, Professor Rouen has focused on one of the Securities and Exchange Commission’s... 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
Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media
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  • 12 Jun 2018
  • First Look

New Research and Ideas, June 12, 2018

technology, but these results are driven almost entirely by heterogeneous effects by those with and without computer science and engineering (CS&E) backgrounds. Specifically, tests of underlying mechanisms reveal that unlocking superior View Details
Keywords: Dina Gerdeman
  • 20 Aug 2013
  • First Look

First Look: August 20

evidence. In our model, firms over-extrapolate exogenous demand shocks and partially neglect the endogenous investment responses of their competitors. Formal estimation of the model confirms that both types of expectational errors are... View Details
Keywords: Anna Secino
  • 22 Feb 2024
  • Research & Ideas

How to Make AI 'Forget' All the Private Data It Shouldn't Have

predictions about the world. And now, even though generative AI feels very different from making a simple prediction, at a technical level, that's really what it is. In order to train these predictive... View Details
Keywords: by Rachel Layne; Technology; Information Technology
  • 12 Jul 2016
  • First Look

July 12, 2016

forecasts as an input into the government budgeting-making process would probably reduce official forecast errors for budget deficits. Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=51324 College Tuition, Public... View Details
Keywords: Sean Silverthorne
  • 17 Aug 2020
  • Research & Ideas

What the Stockdale Paradox Tells Us About Crisis Leadership

naturally that way; I knew too much about the politics of Asia when I got shot down. I think there was a lot of damage done by optimists; other writers from other wars share that opinion. The problem is, some people believe what professional optimists are passing out... View Details
Keywords: by Boris Groysberg and Robin Abrahams
  • 22 Oct 2019
  • Research & Ideas

Use Artificial Intelligence to Set Sales Targets That Motivate

advanced analytics that incorporate artificial intelligence (AI). “Chung has seen companies dramatically improve productivity after adopting advanced analytics to guide compensation.” In an ideal world, a company would use trial and error... View Details
Keywords: by Michael Blanding
  • 21 Aug 2018
  • First Look

New Research and Ideas, August 21, 2018

horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best-performing method (random forest) yields an out-of-sample mean absolute percentage View Details
Keywords: Dina Gerdeman
  • 20 Nov 2019
  • Research & Ideas

It's No Joke: AI Beats Humans at Making You Laugh

this laughing matter to the test. In a new study, he used that joke and 32 others to determine whether people or artificial intelligence (AI) could do a better job of predicting which jokes other people consider funny. The question is... View Details
Keywords: by Dina Gerdeman
  • 04 Jan 2022
  • What Do You Think?

Firing McDonald’s Easterbrook: What Could the Board Have Done Differently?

decided to fire Easterbrook without cause and allow him to keep $105 million in stock and options. Easterbrook fulfilled his promise, saying publicly that he “acknowledges his error in judgment” and that he was “deeply grateful” for his... View Details
Keywords: by James Heskett
  • 15 Jul 2019
  • Book

Many Executives Are Afraid of Finance. Here's How They Can Gain Confidence

it’s a forward-looking, subjective, creative exercise that requires a lot of imagination. Think about valuing Facebook today. It requires predicting the future of privacy policies around the world, the actions of Google and Amazon in the... View Details
Keywords: by Dina Gerdeman
  • 17 Aug 2021
  • Research & Ideas

Can Autonomous Vehicles Drive with Common Sense?

Human error causes at least 90 percent of the 5.25 million accidents in the United States annually. Could driverless cars save lives? Yes, but it may take a long road to get there. “Autonomous vehicles (AVs) are never drunk or tired or... View Details
Keywords: by Michael Blanding; Auto

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