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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

Article

Financial Innovation and Endogenous Growth

By: Luc Laeven, Ross Levine and Stelios Michalopoulos

Is financial innovation necessary for sustaining economic growth? To address this question, we build a Schumpeterian model in which entrepreneurs earn profits by inventing better goods, and profit-maximizing financiers arise to screen entrepreneurs. The model has two... View Details

Keywords: Technological Innovation; Economic Growth

March 2022
Article

Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models

By: Fiammetta Menchetti and Iavor Bojinov

Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details

Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods

2022
Working Paper

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju

As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details

Keywords: AI and Machine Learning; Analytics and Data Science; Mathematical Methods

2022
Article

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations

By: Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach and Himabindu Lakkaraju

As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across all subgroups of a population. For instance, it... View Details

Keywords: Prejudice and Bias; Mathematical Methods; Research; Analytics and Data Science

Article

Valuation of Bankrupt Firms

By: S. C. Gilson, E. S. Hotchkiss and R. S. Ruback

This study compares the market value of firms that reorganize in bankruptcy with estimates of value based on management's published cash flow projections. We estimate firm values using models that have been shown in other contexts to generate relatively precise... View Details

Keywords: Valuation; Business Ventures; Insolvency and Bankruptcy

20 Dec 2013
Working Paper Summaries

Zooming In: A Practical Manual for Identifying Geographic Clusters

Keywords: by Juan Alcácer & Minyuan Zhao

2008
Working Paper

Allocating Marketing Resources

By: Sunil Gupta and Thomas J. Steenburgh

Marketing is essential for the organic growth of a company. Not surprisingly, firms spend billions of dollars on marketing. Given these large investments, marketing managers have the responsibility to optimally allocate these resources and demonstrate that these... View Details

Keywords: Investment Return; Resource Allocation; Marketing; Demand and Consumers; Mathematical Methods

Research Summary

Markets and Market Design

The topic on which I currently spend the most of my research energy is the study of strategic interaction and reputation systems on eBay and similar markets from an applied, market design perspective. The rise of the Internet allowed a whole new generation of markets... View Details

2023
Article

MoPe: Model Perturbation-based Privacy Attacks on Language Models

By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel

Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details

Keywords: Large Language Model; AI and Machine Learning; Cybersecurity

23 Sep 2013
Working Paper Summaries

Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs

Keywords: by Juan Alcácer, Wilbur Chung, Ashton Hawk & Gonçalo Pacheco-de-Almeida

Forthcoming
Article

Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers

By: Mengjie Cheng and Shunyuan Zhang

The growth of the influencer marketing industry warrants an empirical examination of the effect of posting sponsored videos on influencers' reputations. We collected a novel dataset of user-generated YouTube videos created by prominent English-speaking influencers in... View Details

Keywords: Reputation; Mathematical Methods; Marketing Reference Programs; Social Media; Brands and Branding

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

SSRN

2009
Working Paper

Estimating the Effects of Large Shareholders Using a Geographic Instrument

By: Bo Becker, Henrik Cronqvist and Rudiger Fahlenbrach

Large shareholders may play an important role for firm performance and policies, but identifying this empirically presents a challenge due to the endogeneity of ownership structures. We develop and test an empirical framework which allows us to separate selection from... View Details

Keywords: Business Headquarters; Geographic Location; Corporate Governance; Governance Controls; Performance Effectiveness; Business and Shareholder Relations; Mathematical Methods

2010
Working Paper

Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans

By: Doug J. Chung, Thomas J. Steenburgh and K. Sudhir

We estimate a dynamic structural model of sales force response to a bonus based compensation plan. The paper has two main methodological innovations: First, we implement empirically the method proposed by Arcidiacono and Miller (2010) to accommodate unobserved latent... View Details

Keywords: Compensation and Benefits; Performance Productivity; Mathematical Methods; Salesforce Management; Motivation and Incentives

SSRN

Article

Fast Subset Scan for Multivariate Spatial Biosurveillance

By: Daniel B. Neill, Edward McFowland III and Huanian Zheng

We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details

Keywords: Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan

2013
Working Paper

Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs

By: Juan Alcacer, Wilbur Chung, Ashton Hawk and Goncalo Pacheco-de-Almeida

Although Strategy research aims to understand how firm actions have differential effects on performance, most empirical research estimates the average effects of these actions across firms. This paper promotes Random Coefficients Models (RCMs) as an ideal empirical... View Details

Keywords: Strategy; Mathematical Methods

SSRN

May 2022
Article

Coins for Bombs: The Predictive Ability of On-Chain Transfers for Terrorist Attacks

By: Dan Amiram, Evgeny Lyandres and Daniel Rabetti

This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The... View Details

Keywords: Blockchain; Bitcoin; Accounting; AI and Machine Learning; National Security; Governing Rules, Regulations, and Reforms