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  • March 2025
  • Case

The Changing Climate on Wall Street

By: Clayton S. Rose, Maxim Pike Harrell and Michael Norris
Increasing and conflicting regulatory requirements and political pressures regarding climate change tested the leaders of U.S. financial institutions, as they struggled to determine how best to comply while managing their business and its risks. In October 2024,... View Details
Keywords: Change; Disruption; Competency and Skills; Decision Making; Cost vs Benefits; Ethics; Governance; Corporate Accountability; Leadership; Management; Risk Management; Organizations; Corporate Social Responsibility and Impact; Mission and Purpose; Organizational Change and Adaptation; Organizational Culture; Society; Civil Society or Community; Social Issues; Strategy; Adaptation; Banking Industry; Financial Services Industry; Insurance Industry; United States; Europe
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  • 2025
  • Working Paper

Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure

By: Jason Milionis, Jens Ernstberger, Joseph Bonneau, Scott Duke Kominers and Tim Roughgarden
We introduce the first formal model capturing the elicitation of unverifiable information from a party (the "source") with implicit signals derived by other players (the "observers"). Our model is motivated in part by applications in decentralized physical... View Details
Keywords: Mathematical Methods; Infrastructure; Information Infrastructure
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  • February 2025
  • Tutorial

Preparing Business Leaders for an Era of Climate Instability: Understanding and Managing Physical Climate Risk

By: Michael W. Toffel and Spencer Glendon
In this compelling video, Spencer Glendon, founder of Probable Futures and Executive Fellow at Harvard Business School, describes the profound implications of climate change for businesses, the economy, and societies around the world. Drawing from his background in... View Details
Keywords: Modeling; Climate Change; Adaptation; Risk and Uncertainty; Risk Management; Forecasting and Prediction
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  • 2025
  • Working Paper

Is Love Blind? AI-Powered Trading with Emotional Dividends

By: De-Rong Kong and Daniel Rabetti
We leverage the non-fungible tokens (NFTs) setting to assess the valuation of emotional dividends (LOVE), a long-standing empirical challenge in private-value markets such as art, antiques, and collectibles. Having created and validated our proxy, we use deep learning... View Details
Keywords: NFTs; Non-fungible Tokens; AI and Machine Learning; Valuation; Financial Markets
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  • 2025
  • Working Paper

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
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  • January 2025
  • Technical Note

AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix

By: Tsedal Neeley and Tim Englehart
This technical note introduces the confusion matrix as a foundational tool in artificial intelligence (AI) and large language models (LLMs) for assessing the performance of classification models, focusing on their reliability for decision-making. A confusion matrix... View Details
Keywords: Reliability; Confusion Matrix; AI and Machine Learning; Decision Making; Measurement and Metrics; Performance
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  • January 2025
  • Supplement

Hippo: Weathering the Storm of the Home Insurance Crisis (B)

By: Lauren Cohen, Grace Headinger and Sophia Pan
Rick McCathron, CEO of Hippo, was optimistic about the InsurTech's path to profitability after navigating the financial uncertainties of 2022. By bundling their home insurance services with third-parties and established insurance incumbents, Hippo was adopting a... View Details
Keywords: Fintech; Underwriters; Big Data; Homeowners' Insurance; Catastrophe Risk; Global Warming; Environment; Business Economics; Vertical Specialization; Bundling; Economies Of Scale; Business Model; Forecasting and Prediction; Climate Change; Environmental Sustainability; Green Technology; Technological Innovation; Natural Environment; Natural Disasters; Weather; Business Strategy; Competitive Advantage; Business Earnings; Insurance; Social Issues; Profit; Growth and Development Strategy; Insurance Industry; California; United States
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  • January 2025
  • Case

AI Meets VC: The Data-Driven Revolution at Quantum Light Capital

By: Lauren Cohen, Grace Headinger and Sophia Pan
Ilya Kondrashov, CEO of Quantum Light Capital, was driven to harness AI for identifying high-potential scale-ups. Collaborating with Nik Storonsky, founder of Revolut, the duo observed that most venture capital (VC) decisions were heavily influenced by emotion, with... View Details
Keywords: Artificial Intelligence; Business Finance; Data Analysis; Angel Investors; Cognitive Biases; Scale; Venture Capital; Investment; Business Model; Forecasting and Prediction; Technological Innovation; Innovation Strategy; Behavior; Cognition and Thinking; Public Opinion; Private Sector; Business Strategy; Competitive Advantage; Business Earnings; Behavioral Finance; AI and Machine Learning; Analytics and Data Science; Business Startups; Financial Services Industry; London; United Kingdom
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  • November 2024
  • Supplement

AlphaGo (C): Birth of a New Intelligence

By: Shikhar Ghosh and Shweta Bagai
This case, the final of a three-part series, explores DeepMind's pivotal transition from mastering games to solving real-world scientific challenges. In December 2020, DeepMind's AI system AlphaFold 2 achieved a breakthrough by solving protein folding—a 50-year-old... View Details
Keywords: Autonomy; Deep Learning; Drug Discovery; Healthcare Innovation; Neural Networks; Scientific Research; Technology Startup; AI and Machine Learning; Technological Innovation; Research and Development; Business Model; Business Strategy; Open Source Distribution; Technology Industry; United States
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  • 2024
  • Working Paper

Scaling Core Earnings Measurement with Large Language Models

By: Matthew Shaffer and Charles CY Wang
We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details
Keywords: Large Language Models; AI and Machine Learning; Accounting; Profit; Corporate Disclosure; Analytics and Data Science; Measurement and Metrics
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  • November 2024
  • Article

Perceptions About Monetary Policy

By: Michael D. Bauer, Carolin Pflueger and Adi Sunderam
We estimate perceptions about the Federal Reserve’s monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions varies substantially over time,... View Details
Keywords: Monetary Policy; Policy; Interest Rates; Perception; Economy; Forecasting and Prediction
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  • October 2024
  • Article

Canary Categories

By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
Past customer spending in a category is generally a positive signal of future customer spending. We show that there exist “canary categories” for which the reverse is true. Purchases in these categories are a signal that customers are less likely to return to that... View Details
Keywords: Churn; Churn Management; Churn/retention; Assortment Planning; Retail; Retailing; Retailing Industry; Preference Heterogeneity; Assortment Optimization; Customers; Retention; Consumer Behavior; Forecasting and Prediction; Retail Industry
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  • 2024
  • Working Paper

Pitfalls of Demographic Forecasts of U.S. Elections

By: Richard Calvo, Vincent Pons and Jesse M. Shapiro
Many observers have forecast large partisan shifts in the US electorate based on demographic trends. Such forecasts are appealing because demographic trends are often predictable even over long horizons. We backtest demographic forecasts using data on US elections... View Details
Keywords: Mathematical Methods; Voting; Political Elections; Trends; Forecasting and Prediction; Demographics
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  • 2024
  • Article

Learning Under Random Distributional Shifts

By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the U.S. and Switzerland. These approaches use data on past arrivals to generate machine learning models that can... View Details
Keywords: AI and Machine Learning; Refugees; Employment
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  • July 2024
  • Article

Chatbots and Mental Health: Insights into the Safety of Generative AI

By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp and Stefano Puntoni
Chatbots are now able to engage in sophisticated conversations with consumers. Due to the ‘black box’ nature of the algorithms, it is impossible to predict in advance how these conversations will unfold. Behavioral research provides little insight into potential safety... View Details
Keywords: Autonomy; Chatbots; New Technology; Brand Crises; Mental Health; Large Language Model; AI and Machine Learning; Behavior; Well-being; Technological Innovation; Ethics
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  • 2024
  • Working Paper

How Inflation Expectations De-Anchor: The Role of Selective Memory Cues

By: Nicola Gennaioli, Marta Leva, Raphael Schoenle and Andrei Shleifer
In a model of memory and selective recall, household inflation expectations remain rigid when inflation is anchored but exhibit sharp instability during inflation surges, as similarity prompts retrieval of forgotten high-inflation experiences. Using data from the New... View Details
Keywords: Cognition and Thinking; Inflation and Deflation; Personal Finance
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  • 2024
  • Working Paper

Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization

By: Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
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  • 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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  • April 2024
  • Article

Demand-and-Supply Imbalance Risk and Long-Term Swap Spreads

By: Samuel G. Hanson, Aytek Malkhozov and Gyuri Venter
We develop and test a model in which swap spreads are determined by end users' demand for and constrained intermediaries’ supply of long-term interest rate swaps. Swap spreads reflect compensation both for using scarce intermediary capital and for bearing convergence... View Details
Keywords: Swap Spreads; Credit Derivatives and Swaps; Interest Rates; Risk and Uncertainty; Volatility
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  • March 2024 (Revised January 2025)
  • Case

Hippo: Weathering the Storm of the Home Insurance Crisis

By: Lauren Cohen, Grace Headinger and Sophia Pan
Rick McCathron, CEO of Hippo, considered how the firm’s underwriting model could account for the effects of climate change. Along with providing smart home packages, targeting risk-friendly customers, and using data-driven pricing, the Insurtech used technologically... View Details
Keywords: Fintech; Underwriters; Big Data; Insurance Companies; Business Model Design; Weather Insurance; Business Model; Forecasting and Prediction; Climate Change; Environmental Sustainability; Green Technology; Technological Innovation; Natural Environment; Natural Disasters; Weather; Business Strategy; Competitive Advantage; Business Earnings; Insurance; Social Issues; Insurance Industry; United States; California
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