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  • 2023
  • Working Paper

Complexity and Time
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
We provide experimental evidence that core intertemporal choice anomalies -- including extreme short-run impatience, structural estimates of present bias, hyperbolicity and transitivity violations -- are driven by complexity rather than time or risk preferences. First,...  View Details
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  • 2022
  • 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 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 tools...  View Details
Keywords: Analytics and Data Science
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  • October–December 2022
  • Article

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...  View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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  • 2022
  • Working Paper

Perceptions about Monetary Policy
By: Michael D. Bauer, Carolin Pflueger and Adi Sunderam
We estimate perceptions about the Fed's monetary policy rule from micro data on professional forecasters. The perceived rule varies significantly over time, with important consequences for monetary policy and bond markets. Over the monetary policy cycle, easings are...  View Details
Keywords: Monetary Policy; Central Banking; Forecasting and Prediction; Policy; Interest Rates
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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...  View Details
Keywords: Machine Learning; Statistics; Experiments; Forecasting and Prediction; Performance Evaluation
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  • 2021
  • Working Paper

Real Credit Cycles
By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S....  View Details
Keywords: Econometric Models; Business Cycles; Credit
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  • September–October 2020
  • Article

Managing Churn to Maximize Profits
By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability or their responsiveness to a...  View Details
Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit
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  • August 2020 (Revised September 2020)
  • Technical Note

Assessing Prediction Accuracy of Machine Learning Models
By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...  View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
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  • 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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  • 2018
  • Working Paper

Channeled Attention and Stable Errors -- Previous Working Version
By: Tristan Gagnon-Bartsch, Matthew Rabin and Joshua Schwartzstein
A common critique of models of mistaken beliefs is that people should recognize their error after observations they thought were unlikely. This paper develops a framework for assessing when a given error is likely to be discovered, in the sense that the error-maker...  View Details
Keywords: Perception; Behavior; Theory; Situation or Environment
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  • Article

Can Analysts Assess Fundamental Risk and Valuation Uncertainty? An Empirical Analysis of Scenario-Based Value Estimates
By: Peter R. Joos, Joseph D. Piotroski and Suraj Srinivasan
We use a dataset of sell-side analysts' scenario-based valuation estimates to examine whether analysts reliably assess the risk surrounding a firm's fundamental value. We find that the spread in analysts' state-side contingent valuations captures the riskiness of...  View Details
Keywords: Analyst Forecasts; Scenarios; Uncertainty; Risk and Uncertainty; Valuation; Forecasting and Prediction
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  • August 2016
  • Article

The Role of (Dis)similarity in (Mis)predicting Others' Preferences
By: Kate Barasz, Tami Kim and Leslie K. John
Consumers readily indicate liking options that appear dissimilar—for example, enjoying both rustic lake vacations and chic city vacations or liking both scholarly documentary films and action-packed thrillers. However, when predicting other consumers’ tastes for the...  View Details
Keywords: Perceived Similarity; Prediction Error; Preference Prediction; Self-other Difference; Social Inference; Cognition and Thinking; Perception; Forecasting and Prediction
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  • July 2015
  • Article

Executives' 'Off-the-Job' Behaviors and Financial Reporting Risk
By: Robert Davidson, Aiyesha Dey and Abbie Smith
We examine how executives' behavior outside the workplace, as measured by their ownership of luxury goods (low “frugality”) and prior legal infractions, is related to financial reporting risk. We predict and find that chief executive officers (CEOs) and chief financial...  View Details
Keywords: Management Teams; Behavior; Personal Characteristics; Crime and Corruption; Governance Compliance; Financial Reporting; Organizational Culture
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  • 2019
  • Working Paper

Managing Churn to Maximize Profits
By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability, or their responsiveness to a...  View Details
Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit
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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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  • 2011
  • Working Paper

The Importance of Work Context in Organizational Learning from Error
By: Lucy H. MacPhail and Amy C. Edmondson
This paper examines the implications of work context for learning from errors in organizations. Prior research has shown that attitudes and behaviors related to error vary between groups within organizations but has not investigated or theorized the ways in which...  View Details
Keywords: Judgments; Learning; Business Processes; Organizational Culture; Failure; Performance Improvement; Opportunities; Complexity
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  • 2010
  • Chapter

Understanding and Coping with the Increasing Risk of System-Level Accidents
By: Dutch Leonard and Arnold M. Howitt
The world has seen a number of recent events in which major systems came to a standstill, not from one cause alone but from the interaction of a combination of causes. System-level accidents occur when anomalies or errors in different parts of an interconnected system...  View Details
Keywords: Economics; Globalization; Risk Management; Boundaries; System Shocks
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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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  • Forthcoming
  • Article

Do Rating Agencies Behave Defensively for Higher Risk Issuers?
By: Samuel B. Bonsall IV, Kevin Koharki, Pepa Kraft, Karl A. Muller III and Anywhere Sikochi
We examine whether rating agencies act defensively toward issuers with a higher likelihood of default. We find that agencies' qualitative soft rating adjustments are more accurate as issuers' default risk grows, as evidenced by the adjustments leading to lower Type I...  View Details
Keywords: Credit Rating Agencies; Soft Rating Adjustments; Default; Credit; Performance Evaluation; Measurement and Metrics; Financial Institutions; Risk Management
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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

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