The Research Colloquia provide a forum for interaction among faculty, students, and visitors interested in the applications of business and management. The colloquia include presentations by faculty from UC Irvine and other universities, as well as research institutes. Colloquia events are open to the public unless otherwise noted; please see event description for more details.
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 by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make "different" mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases, and its superior performance over an alternative method (simulation-extrapolation), which has been suggested by prior work as a reasonable method of addressing the measurement error problem.
Link to events page here.
Host(s): Assistant Professor Tingting Nian
Speaker(s): Lynn Wu, Associate Professor of Operations, Information and Decisions
University: University of Pennsylvania, Wharton School of Business
Time: Friday, April 1, 2022; 11:00 AM – 12:30 PM PDT
Location: SB1 2321 (Judy Rosener Flexible Classroom)
We examine how data analytics can facilitate innovation in firms that have gone through an initial public offering (IPO). It has been documented that an IPO is associated with a decline in innovation despite the infusion of capital from the IPO that should have spurred innovation. By assembling and analyzing multi-year panel data at the firm level, we find that firms that possess or acquire data analytics capability has sustained the rate of innovation compared to similar firms that have not acquired that capability. This effect is even greater when only machine learning capabilities are considered. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that combine existing technologies into new ones – the form of innovation that are especially well-supported by analytics. By examining the three main mechanisms that inhibit firm innovation after IPO—short-term financial pressure, cost of disclosure requirements, and managerial incentives, we find that data analytics can ameliorate the pressure from meeting short-term financial goals and disclosure requirements, but analytics is limited in addressing managerial incentives. Overall, our results suggest that the increased deployment of analytics may reduce some of the innovative penalties of IPOs, and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing newly acquired capital to the acquisition of analytics capabilities.
Link to events page here.
How can firms protect new technological knowledge, and for how long? Although a considerable body of strategy research has explored mechanisms that support knowledge appropriation, this has typically focused on exogenous institutional factors such as the effectiveness of patent and contract enforcement, or on the characteristics of the technology itself. In this paper we call attention to a potential knowledge-protection mechanism that has received scant attention: a highly connected (or cohesive) intrafirm inventor network structure. Drawing on social network theory, we propose that organizations whose inventor networks are more connected enjoy greater appropriation through faster follow-on innovation relative to their rivals. Using patent data on nearly 1,400 large corporations over 33 years, we find evidence consistent with our hypotheses. We discuss implications for future research.
Link to events page here.
Mutual funds’ switches to monthly holding disclosures reduce the efficiency of corporate investments. Consistent with a crowding-out mechanism, the evidence suggests that monthly portfolio disclosures discourage information production activities by other market participants and, consequently, reduce corporate managers’ ability to learn from prices. This effect increases with managers’ incentives to learn from prices and investors’ potential use of monthly fund disclosures. The study sheds light on the regulatory debate on the efficacy of making monthly holdings disclosures available to the public.
Link to events page here.
• Host(s): Associate Professor Vibhanshu Abhishek
• Speaker(s): Navdeep Sahni, Associate Professor of Marketing
• University: Stanford University, Stanford Graduate School of Business
• Time: Friday, February 25, 2022; 10:00 AM – 11:30 AM PST
• Location: Zoom – https://uci.zoom.us/j/93417550211
Consumer inertia is a well documented phenomenon that effectively creates market power for firms over their existing customer base. However, it is unknown how self-aware consumers are about their inertia and how they preemptively respond to their future inertia. We quantify actual inertia, consumer anticipated inertia, and their responses to it using a large-scale field experiment with a leading European newspaper. We vary the price, duration, and whether a contract is automatically canceled or renews after a promotional period. We document higher subscription rates after the promo among those offered an auto-renewal contract, and at the same time find 24% fewer takers of any contract during the promotional period, and 9% fewer subscribers at any point in the two years that follow. Leveraging the price and duration treatments we quantify that the average consumer predicts a 13% chance of being inert within a month, versus an actual inertia of 36%. In a complementary approach of classifying potential subscribers to different types, we classify more than a third as sophisticates who avoid subscribing, a third as time-consistent who cancel immediately after the promo, and only a tenth as naive enough to remain subscribed for more than three months due to inertia. Our results imply that more consumers avoid these mildly exploitative contracts than fall prey to them.
Link to events page here.
Link to events page here.
Host(s): Associate Professor Tonya Bradford
Speaker(s): André Martin, Marketing PhD Student
University: University of North Carolina, Kenan-Flagler Business School
Time: Friday, February 25, 2022; 10:00 AM – 11:30 AM PST
Location: SB1 5200 (Lyman Porter Colloquia Room)
As agile marketing firms increasingly invest in MarTech (Marketing Technology) infrastructure to gather and analyze customer data to spot opportunities and trends, the responsibility to protect these customer data becomes all the more important. Inadequately protecting data can have longlasting negative consequences, not only for the affected customers, but also for the affected firms. In 2021, the overall number of data compromises went up by more than 68 percent compared to the previous year (2021 Annual Data Breach Report). On average, each of these data breaches costed firms $8.64 million in damages (Varonis.com 2021). Factoring in the cost of reputation damage, these losses can swell considerably as exemplified by Citi Group’s losses of $836 million following a data breach (Martin, Borah, and Palmatier 2017). The extent of this problem is so pervasive that former FBI Director Robert Muller (2012) stated that “I am convinced that there are only two types of companies: those that have been hacked and those that will be.”
Overall, the negative consequences of data breaches go well beyond the immediate fines and legal actions and lawsuits, and extend to loss of customer trust, brand equity damages, and eventually detrimental financial consequences. Prior research has made first steps to capture and measure the extent of these losses. As such, extant literature established the negative impact of data breaches on the firm’s market value (Malhotra and Malhotra 2011), on risk perceptions (Aivazpour, Valecha, and Chakraborty 2018), on word-of-mouth (Martin, Borah, and Palmatier 2017), on customer trust (Martin, Borah, and Palmatier 2017), and on customer spending (Janakiraman, Lim, and Rishika 2018).
Given the potential for negative implications of data breaches, the natural question to ask is what firms can do to mitigate the impact and recover from the crisis. Martin, Borah, and Palmatier (2017) find that the transparency of firms’ data use and customers’ ability to control information flow affect trust and consumer behavior, thereby emphasizing the role of a firm’s pre-crisis efforts and policy investments. To provide first insights in effective actions once and after breaches occur, Rasoulain et al. (2017) look into the role of compensation, improving processes, and apologies to minimize the negative impact of data breaches on firms’ idiosyncratic risk. In our study, we further explore and dig deeper into data breach recovery options by systematically analyzing the short- and long-term impact of multiple crisis recovery communication options. Specifically, we use the typology, as proposed by Diesterhöft et al. (2020) that combines elements of blame attribution theory (Coombs 2007) into eight response categories: Offering Compensation, Offering Apology, Engaging in Whitewash, Taking Objective Actions, Reinforcing Value Commitment, Highlighting Customer Relationship, Transparently Informing on Type of Information Disclosed, and Offering Customer Advise. We study to what extent each of these crisis communication elements manages to mitigate data breach harm.
To gauge data breach harm we look at its impact on a wide set of consumer mind-set metrics.
Customer mind-set metrics track brand health and allows firms to track consumers on their path on the brand funnel toward brand advocacy. As such, they measure ‘what a customer thinks’, and are leading indicators of their future behavior (Srinivasan, Vanhuele, and Pauwels 2010). This gives firms advanced notice to act appropriately to minimize the impact of MarTech crises. To this effect, we examine the impact on seven mind-set metrics: two top of the funnel recall metrics (Buzz and Impression); four middle of the funnel evaluation metrics (Quality, Value, Reputation, and Satisfaction), and one bottom of the funnel commitment metric (Recommendation).
Empirically, we study the impact of data breaches using high frequency (daily) data, for a variety of product and service categories and a broad set of several brands. Specifically, we use daily brand level customer mind-set metric data from YouGov between 2012 and 2021. Our data allows us to track over 2,000 brands in the US in a wide variety of industries (Financial Services, Hospitality, Travel, Retail, Healthcare, Consumables, and Durables). This permits us to examine brand and industry level heterogeneity and thus offer empirically generalizable implications. The high frequency and long-time series permit us to model not just the short-term impact of MarTech crises, but also the long-term impact, which no research to the best of our knowledge has examined to date. The real-time nature of the data gives managers instant access to decision dashboards to make fast mitigation decisions.
To assess the firm’s crisis communication styles we focus on the direct communication the firm provides to its consumers following a data breach. Specifically, in the US market, state laws regulate privacy breaches and require the affected firms to inform victims via a letter from the firm. This creates a direct communication channel between the firm and customer, which captures firms’ intentions, without any interpretation and perceptions of news media. In our ten-year observation window, 198 brands were embroiled in a data breach. Of these, 130 brands sent out a letter to their consumers. We do a text-analysis of these letters to measure different communication recovery strategies. Next, we use dynamic panel data models, which control for firm and temporal unobserved heterogeneity, potential endogeneity biases, and several other sources of observed heterogeneity to isolate the direct impact of data breaches and the moderating effect of firm communication strategies on customer mind-set metrics.
We find that privacy breaches negatively affect all seven dimensions of our customer mindset metrics, and surprisingly, that some firm recovery strategies exacerbate the adverse effects of the privacy data breach. Moreover, using our parameter estimates we assess to what extent and how long the potential negative effects linger, and to what extent communication strategies speed up the path towards recovery.
Link to events page here.
Host(s): Assistant Professor Luke Rhee
Speaker(s): Laura Poppo, Professor of Management
University: University of Nebraska-Lincoln, Nebraska College of Business
Time: Friday, February 18, 2022; 2:00 PM – 3:30 PM PST
Location: SB1 5200 (Lyman Porter Colloquia Room)
Over the decades, strategic management has evolved from an emphasis on simply adaptation – modifying the organization to better fit, or close the gap, between the organization and the changes in the environment – to that of finding and seizing of opportunities that have the potential to create value. How organizations go about this remains undertheorized. The most rigorously theorized is problem-solving, problematic search. Left largely unaddressed is how do managers go about ‘formulating’ a problem when the external environment is changing in novel and unsettled ways and the decision-making process is both unstructured and ambiguous. To explore this gap, we question: what process supports the discovery and formulation of problems as well as enables the generation of useful and novel (e.g. creative) solutions? To ground this research, one co-author spent several years exploring broadly and then narrowly, through interviews and several organizational sites, the practice of strategic planning and corporate entrepreneurship. Based on themes identified in this qualitative work, we developed a multi-level perspective, strategic problem engagement. A service organization volunteered to participate in our academic, empirical study. Critical to its selection was that the top management (CEO, corporate staff) was currently undertaking a system-wide strategic planning initiative focused on adapting the organization to novel, unsettled changes in the external environment and generating novel solutions.
Our results follow. First, we illustrate a multi-level approach to strategic problem engagement, as both the TMT as well as teams of knowledge-diverse lower-level employees can be integral to strategic formulation process and the generation of creative solutions. This multi-level approach helps overcome the cognitive limitations of bounded rationality that impedes decision makers’ abilities to identify and construct the right problem.
Second, we empirically demonstrate formulation as a process of strategic problem engagement, involving simultaneously two activities: 1) problem engagement, the process of discovering the problem through exploring, identifying, defining, and reconstructing it, and 2) strategic engagement, the process of recognizing the factors that create organizational value and using them to further explore the problem. This extends prior conceptualizations of formulation as a sensing or an awareness of a potential problem followed by the second stage, formulating a causal logic for how the issue in the environment relates to organization.
Third, our results show that strategic engagement, not problem engagement, leads to the generation of more novel and useful solutions. This finding helps to uncover the black box of creative synthesis. Finally, we examine additional factors that impact the cognitive and motivation challenges associated with complex problem solving.
Link to events page here.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Brant Christensen, Associate Professor of Accounting, Glen McLaughlin Chair in Business Ethics
University: University of Oklahoma, Price College of Business
Time: Friday, February 18, 2022; 11:00 AM – 12:15 PM PST
Location: Zoom - https://uci.zoom.us/j/96996574801
Link to events page here.
Host(s): Associate Professor John Turner
Speaker(s): Luyi Yang, Assistant Professor of Operations & IT Management
University: University of California, Berkeley, Haas School of Business
Time: Friday, January 28, 2022; 10:00 AM - 11:30 AM PST
Location: Zoom - https://uci.zoom.us/j/95146558263?pwd=TUY2Zy9VaVlVV0RlR2pRalNUaHdHUT09
Link to events page here.
Host(s): Assistant Professor Tingting Nian
Speaker(s): John Horton, Associate Professor of Information Technologies
University: Massachusetts Institute of Technology, Sloan School of Management
Time: Friday, January 14, 2022; 11:00 AM - 12:30 PM PST
Location: Zoom - https://uci.zoom.us/j/95502043865
Link to events page here.
Host(s): Associate Professor John Turner
Speaker(s): Julia Yan, Assistant Professor of Operations and Logistics
University: University of British Columbia, Sauder School of Business
Time:Friday, December 3, 2021; 10:00 AM - 11:30 AM – 11:30 AM
Location: SB1 5200 (Lyman Porter Colloquium Room)
Link to events page here.
Host(s): Associate Professor John Turner
Speaker(s): Somya Singhvi, Assistant Professor in Data Sciences and Operations
University: University of Southern California, Marshall School of Business
Time:Friday, November 19, 2021; 10:00 AM – 11:30 AM
Location: Zoom
In order to further increase competition on UMP, we adopt a multi-method approach to design, implement, and evaluate the impact of a new two-stage auction on UMP. The design of the two-stage auction is informed by operational constraints and guided by theory-informed, semi-structured interviews with traders in the field. A new behavioral auction model is developed to determine when the two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The two-stage auction was implemented on the UMP for a major lentils market in February 2019. By June 2019, commodities worth more than $6 million (USD) had been traded under the new auction design. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 4.7% price increase, representing profit improvement of 60%-158% for over 10,000 farmers who traded in the treatment market. The detailed auction data provides empirical validation of the behavioral auction model.
Link to events page here.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Jonathan Glover, James L. Dohr Professor of Accounting
University: Columbia University, Columbia Business School
Time: Friday, November 19, 2021; 11:00 AM – 12:15 PM
Location: Zoom
Hold-up problems in buyer-supplier relationships can inhibit relation-specific investments, including supplier innovations aimed at cost reductions or quality improvements. One solution to such hold-up problems is to install an information system as a substitute for commitment, limiting the buyer's ability to extract rents from the supplier. Another solution is trust, i.e., a self-enforcing promise to let the supplier keep some of the rents created by innovation. The promise is made self-enforcing via a relational contract enforced by repeated play. On the surface, these two solutions appear to be substitutes for each other. We study a model in which both tools are available and find instead that trust and information system design are complements (holding innovation constant). The intuition is that the information system design solution is itself constrained by trust. Creating trust via a relational contract enables the buyer to use the information system more aggressively.
Link to events page here.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Jung Ho Choi, Assistant Professor of Accounting
University: Stanford University, Graduate School of Business
Time: Friday, June 11, 2021, 3:15 PM - 4:30 PM
Location: Zoom
Using half a million anonymous job seekers' detailed search data, we study the information content of earnings announcements for job seekers. In the spirit of Beaver (1968), we find first evidence of a substantial increase in on-the-job-search activities around earnings announcements. Job seekers search more actively for employers with earnings growth. Peer firm employees search for employers more actively than own firm employees around earnings announcements. Job seekers search for information about employers' potential offers & salaries, interview questions, and hiring situations. Finally, financial information is predictive of future job prospects including job growth and career growth. Overall, our paper suggests that earnings announcements guide job seekers' search activities by providing them with information about employers' job prospects.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Sean Cao, Assistant Professor of Accounting
University: Georgia State University, J. Mack Robinson College of Business
Time: Friday, June 4, 2021, 3:15 PM - 4:30 PM
Location: Zoom
An AI analyst we build to digest corporate financial information, qualitative disclosure and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analyst. In the contest of "man vs machine," the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produce the highest potential in generating accurate forecasts. Our paper portraits a future of "machine plus human" (instead of human displacement) in high-skill professions.
Host(s): Assistant Professor Luke Rhee
Speaker(s): Adam M. Kleinbaum, Associate Professor of Business Administration
University: Dartmouth College, Tuck School of Business
Time: Friday, May 21, 2021, 10:00 AM - 11:30 AM
Location: Zoom
Social networks are integral to the performance of collaborative work, but research on network change has shed little light on the tactics professionals use to deliberately stimulate collaborative network ties. In this paper, we empirically examine one such tactic, corporate offsites, as opportunity shocks for intra-organizational networking. We find that attending an offsite leads participants to significantly increase the number of new network ties that they initiate. But surprisingly, people who do not attend the offsite similarly increase their network outreach, consistent with deliberate compensatory behavior on the part of non-attendees. However, attendees also receive more incoming requests from new collaborators following offsites, a benefit that does not accrue to non-attendees. These results are consistent with a conceptualization of opportunities as affecting network change in two distinct ways: through the changes the individual makes in her own network, which are subject to individual agency and through the decisions made by others, which also shape the focal individual’s network, but which fall outside of the focal individual’s agency. Integrating the traditional egocentric perspective with an altercentric perspective moves us closer to understanding both an individual’s agency to shape her evolving network and the limits on that agency.
Host(s): Assistant Professor Ken Murphy
Speaker(s): Burak Kazaz, Professor of Supply Chain Management
University: Syracuse University, Whitman School of Management
Time: Friday, May 14, 2021, 1:00 PM - 2:30 PM
Location: Zoom
Among agricultural goods, wine is a specialty product. Fine wine grapes require exceptional care and attention. After the harvest, grapes are crushed and the wine goes through a long aging process. In the case of Bordeaux-style wines, for example, the aging process in barrels can last 18 to 24 months. The aging continues for another 20 to 30 years in the bottle. This long aging process makes wine production a risky venture. Consumers follow the evolution of these fine wines closely, track their corresponding tasting reviews and scores and are often informed about climatic conditions during the growing seasons. Thus, wine is one of the most heavily tracked and publicized agricultural products. Considering the long production time, winemakers can mitigate their operational and financial risks by selling their wines in advance in the form of wine futures. In this talk, I will describe predictive and prescriptive analytical methods that assist primary enterprises that produce and distribute wine in their decision-making processes. The seminar will begin with predictive models that estimate the true value of wine futures prices. These estimation models are essential to the financial exchange known as the London International Vintners Exchange (Liv-ex) where wine futures contracts are traded. Coined as “realistic prices” by Liv-ex, these predictive models assist buyers in their purchasing decisions as they can determine whether a futures contract is underpriced or overpriced. Next, I will develop risk mitigation models to assist winemakers in mitigating uncertainty in weather conditions and tasting expert reviews. These prescriptive models rely on predictive analytics which help determine consumers’ utilities from buying the wine in advance, or later or not purchasing it at all. Prescriptive models such as a multinomial logit model focus on determining how much of the wine should be sold in advance in order to reduce risk exposure and maximize the expected profits of the winemaker. On the buyer side, the talk will introduce stochastic portfolio optimization models for both wine distributors and importers in their decision regarding how to allocate limited budgets between wine futures contracts and bottled wine. These prescriptive models are, once again, built on predictive analytics that estimate the evolution of futures and bottle prices over time under fluctuating market and weather conditions. Wine is an exemplary agricultural product; its production and quality perceptions are widely tracked by businesses and consumers. The predictive and prescriptive models of this tutorial help create transparency in this largely opaque market. They assist the industry in its drive towards market efficiency. The tutorial also offers future research directions in wine analytics and describes how these techniques can be beneficial for the production and distribution of other agricultural products.
Host(s): Assistant Professor Travis Howell
Speaker(s): Sonali Shah, Associate Professor
University: University of Illinois at Urbana-Champaign, Geis College of Business
Time: Friday, May 14, 2021, 10:00 AM - 11:30 AM
Location: Zoom
Experimentation enables new ventures to illuminate areas of uncertainty and leads to improved decision-making and performance. Experiments are often studied as stand-alone actions, however, scholars are increasingly interested in understanding how experiments are supported by organizational characteristics and choices. Using an inductive, theory-building approach, we seek to illuminate the organizational models that accompany and support experiments, as well as their antecedent conditions and performance effects. We find that new ventures follow one of two strikingly different experimentation models: “generative” and “focused” experimentation. Generative experimentation involves knowledge sharing and collaborative experimentation with external actors, the creation of an idea-centered organization, conducting experiments across multiple knowledge domains (technological, market and business model) and external experiments. In contrast, focused experimentation tends to involve little knowledge exchange with external actors, a focus on executing a specific idea and conducting experiments focused on the technology in controlled settings. Generative experimentation is more likely to result in pivoting and better performance than focused experimentation for the new ventures in our sample. Three antecedent conditions––having at least one serial entrepreneur on the founding team, knowledge of multiple industries and an orientation towards technologies (rather than products)––are associated with new ventures’ decision to pursue generative, rather than focused, experimentation. This study examines new ventures in the nascent smart lighting industry and is grounded in detailed interview data collected from founders.
Host(s): Assistant Professor Jinfei Sheng
Speaker(s): Will Cong, Associate Professor
University: Cornell University, Johnson College of Business
Time: Friday, April 23, 2021, 1:00 PM - 2:00 PM
Location: Zoom
We directly optimize the objectives of portfolio management via reinforcement learning---an alternative to conventional supervised-learning-based paradigms that entail first-step estimations of return distributions, pricing kernels, or risk premia. Building upon breakthroughs in AI, we develop multi-sequence neural network models tailored to distinguishing features of economic and financial data, while allowing training without labels and potential market interactions. The resulting AlphaPortfolio yields stellar out-of-sample performances (e.g., Sharpe ratio above two and over 13% risk-adjusted alpha with monthly re-balancing) that are robust under various economic restrictions and market conditions (e.g., exclusion of small stocks and short-selling). Moreover, we project AlphaPortfolio onto simpler modeling spaces (e.g., using polynomial-feature-sensitivity) to uncover key drivers of investment performance, including their rotation and nonlinearity. More generally, we highlight the utility of deep reinforcement learning in finance and invent "economic distillation" tools for interpreting AI and big data models.
Host(s): Assistant Professor Luke Rhee
Speaker(s): Sarah Kaplan, Professor of Strategy and Management
University: University of Toronto, Rotman School of Management
Time: Friday, April 16, 2021, 10:00 AM - 11:30 AM
Location: Zoom
Many environmental and social regulations emphasize disclosures as a means to hold organizations accountable for their progress. Strategically, organizations may want to comply with the regulations or not. To mitigate the negative impacts of non-compliance, organizations may use strategic obfuscation tactics in their disclosures. This paper investigates how organizations make disclosures about social issues and whether their disclosure behavior is associated with change. We address this question by examining disclosures that respond to a mandatory “comply-or-explain” regulation for women’s representation on boards and executive levels in which organizations must disclose their practices and targets or provide reasons (explanations) for not doing so. We find that disclosures from organizations that do not comply substantively are more obfuscating. Further, organizations with harder-to-read disclosures do not significantly improve women’s representation on their board in the ensuing years. Second, we explore how the legal liability of transparency shapes which practices are adopted. We find that organizations using legal templates in their disclosures are associated with adopting a written policy to identify and select women candidates for their top positions but not with the adoption of more binding constraints such as targets for women on their boards. This paper contributes to the literatures on decoupling and diversity by suggesting that external pressures solely based on transparency are not sufficient to improve on the status quo.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Joseph Pacelli, Assistant Professor
University: Indiana University, Kelley School of Business
Time: Friday, April 9, 2021, 3:15 PM - 4:30 PM
Location: Zoom
We provide the first comprehensive analysis of the properties of investment recommendations generated by “Robo-Analysts,” which are human-analyst-assisted computer programs conducting automated research analysis. Our results indicate that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several important dimensions. First, Robo-Analysts produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts and are less likely to recommend “glamour” stocks and firms with prospective investment banking business. Second, automation allows Robo-Analysts to revise their recommendations more frequently than human analysts and better incorporate information from complex period filings. Third, while Robo-Analysts’ recommendations exhibit weak short-window return reactions, they have long-term investment value. Specifically, portfolios formed based on the buy recommendations of Robo-Analysts significantly outperform those of human analysts. Overall, our results suggest that Robo-Analysts are a valuable, alternative information intermediary to traditional sell-side analysts for investment advice.
Host(s): Associate Professor Kevin Bradford
Speaker(s): Yiting Deng, Assistant Professor of Marketing
University: University College of London, School of Management
Time: Friday, April 9, 2021, 2:00 PM - 3:00 PM
Location: Zoom
The consumer purchase journey is influenced by both expert opinions and consumer reviews. However, it is not clear whether favorable expert opinions improve or hurt consumer evaluations of quality. On the one hand, positive expert opinions can enhance the reputation of a business and lead to higher consumer ratings; on the other hand, they may raise consumer expectations and lead to lower consumer ratings. This paper explores the effect of expert opinions on consumer reviews in the context of Michelin stars in the restaurant industry. We constructed a data set based on the Michelin Guide for Great Britain & Ireland from 2010 to2020. For each restaurant that was awarded Michelin stars during these 11 years, we collected online consumer reviews from TripAdvisor, OpenTable, and Yelp and retrieved relevant historical menus from the restaurant’s official website. Based on the data, we first estimate the effect of Michelin star changes on the valence of consumer reviews. We find that decreases in Michelin star status improve consumers’ star ratings. Next, we estimate a Bayesian topic model and analyze the effects of changes in Michelin star status on review topics, and we find service to be the main driver of customer satisfaction. Finally, we analyze how restaurants respond to changes in Michelin star status by analyzing the relevant historical menus, and we show that in response to Michelin star awards, restaurants modify their menu structure to achieve higher consumer satisfaction.
Host(s): Assistant Professor Jinfei Sheng
Speaker(s): Haoxiang Zhu, Associate Professor of Finance
University: Massachusetts Institute of Technology, Sloan School of Management
Time: Friday, April 9, 2021, 1:00 PM - 2:00 PM
Location: Zoom
We find large overnight returns, with no abnormal variance, before the release of nonfarm payrolls, ISM, and GDP, similar to the pre-FOMC returns. To explain this common pattern, we propose a two-risk model with the uncertainty about the magnitude of the impending news’ market impact as an additional risk, and link there-announcement return directly to the accumulation of heightened uncertainty and its later resolution prior to the announcement. We empirically test and verify the model’s distinct predictions on the joint intertemporal behavior of return, variance, and particularly VIX – a gauge of impact uncertainty by our model, surrounding macroeconomic announcements.
Host(s): Assistant Professor Sharon Koppman
Speaker(s): Forrest S. Briscoe, Professor of Management
University: Pennsylvania State University, Smeal College of Business
Time: Friday, April 9, 2021, 10:30 AM - 12:00 PM
Location: Zoom
Despite recognizing the potential risks for employees who choose to participate in protest at the workplace, researchers have rarely explored the actual career consequences that stem from such activism. We integrate research on employee activism and worker norms to theorize that workplace protest represents a perceived violation of idealized norms for professional employees that can lead to negative responses in the organization and labor market. We investigate this premise with the 2016 National Football League (NFL) “Take a Knee” protests as a strategic research setting. Constructing a matched sample of comparable protesting and non-protesting NFL players, we then use a difference-in-difference approach to causal identification. The results indicate that protesting is associated with negative consequences for subsequent compensation, as well as an increase in the probability of exiting from the occupational labor market. We further find that the negative effect of protesting on compensation is reduced for employees who have a potential managerial ally in their organization, in the form of a Black head coach. Overall, the findings offer contributions for research on employee activism, careers and inequality.
Host(s): Assistant Professor Jinfei Sheng
Speaker(s): Alberto Manconi, Associate Professor of Finance
University: Bocconi University, Finance Department
Time: Friday, April 2, 2021, 2:00 PM - 3:00 PM
Location: Zoom
We use the 2007 acquisition of Dow Jones & Co. by News Corporation to study whether the perception of a news source’s political affiliation affects its credibility and financial market impact. Following 2007, the price of Republican- (Democrat-) aligned stocks becomes less sensitive to positive (negative) Dow Jones Newswires (DJNW) sentiment, consistent with the market perceiving a pro-Republican bias. There is, however, no evidence of an actual bias in DJNW, suggesting a loss of price informativeness. Consistent with this view, a trading strategy exploiting the attenuated reaction to DJNW news earns abnormal returns following 2007.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Robert Hills, Assistant Professor of Accounting
University: Pennsylvania State University, Smeal College of Business
Time: Friday, March 26, 2021, 3:15 PM - 4:30 PM
Location: Zoom
We examine the definition of EBITDA in private credit agreements. Using textual analysis and supervised learning, we assess a permissiveness score associated with EBITDA definitions based on the number of adjustments included in an EBITDA computation. We find that permissiveness is greater for larger deals, deals with pledged collateral and deals with larger spreads. Permissiveness in EBITDA definitions is also positively (negatively) related to accrual (cash flow) volatility, suggesting that accruals are not as informative as cash flows to lenders about borrowers’ underlying ability to meet obligations. Because EBITDA is included in the definition of many financial covenants, we investigate the relation between permissiveness and both covenant slack and violations. We find that permissiveness is associated with less covenant slack, yet fewer violations; however, market responses to violations are more negative when permissiveness is higher. Altogether, our findings suggest that the permissiveness of EBITDA definitions enhances contracting efficiency by removing variation in contractual EBITDA that is less reflective of the true state of the world, thereby enhancing the informativeness of covenant realizations.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Siew Hong Teoh, Dean’s Professor of Accounting
University: University of California, Irvine, The Paul Merage School of Business
Time: Friday, March 19, 2021, 3:15 PM - 4:30 PM
Location: Zoom
Using machine learning-based algorithms, we extract key impressions about personality traits from the LinkedIn profile photos of sell-side analysts. We find that these face-based factors are associated with analyst behavior, performance, and capital- and labor-market outcomes. The trustworthiness (TRUST) and dominance (DOM) factors are positively associated with analyst forecast accuracy and report length. Analysts with high TRUST scores tend to herd with managerial guidance forecasts; those with high DOM scores actively participate in conference calls. The positive association of the attractiveness (ATTRACT) factor on forecast accuracy diminishes with market learning and after Reg-FD. Forecasts from analysts with higher TRUST and DOM scores generate stronger price reactions. High DOM scores help male analysts but hurt female analysts to attain All-Star status. These findings suggest that impressions formed from observing analysts’ physical facial attributes are associated with analysts’ economic behaviors. Some of the investor and peer responses to these impressions seem to reflect societal biases and gender stereotypes.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Kimball Chapman, Professor of Accounting
University: Washington University in St. Louis, Olin School of Business
Time: Friday, March 12, 2021, 3:15 PM - 4:30 PM
Location: Zoom
The SEC limits sell-side analysts’ research activities on IPO firms both before and immediately after going public (the IPO quiet period). We examine whether, in spite of these restrictions, analysts serve an indirect information role during the quiet period through their research of rival firms in the IPO firm’s industry. Our evidence suggests analysts provide informative signals about IPO firms during the quiet period through their stock recommendation revisions of rival firms. In particular, we find that analysts revise the stock recommendations of rival firms in response to IPO news and that these recommendation changes are predictive of future IPO performance. We also find that IPO investors trade on this information on the IPO date. However, we find that only institutional investors make use of this information, and that retail investors are inattentive to information in analyst research of rival firms, except when the analyst is affiliated with the IPO firm or when the rival firm is highly visible. Our findings suggest that, even during the IPO quiet period, analysts provide informative signals about IPO firms through their coverage of rival firms.
Host(s): Assistant Professor Ken Murphy
Speaker(s): Banafsheh Behzad, Associate Professor
University: California State University at Long Beach, College of Business Administration
Time: Friday, March 12, 2021, 10:00 AM - 11:30 AM
Location: Zoom
In recent years, scholars have raised concerns on the effects that unreliable news, or `fake news', has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election and the 2016 British Brexit referendum. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader's own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of fake news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political bias and read articles of opposing political bias.
Host(s): Assistant Professor Travis Howell
Speaker(s): Emily Cox Pahnke, Associate Professor
University: University of Washington, Michael G. Foster School of Business
Time: Friday, March 5, 2021, 10:00 AM - 11:30 AM
Location: Zoom
Although partnering with higher status industry players can be especially critical for ventures seeking to establish themselves in a network, it is unclear what actions these firms can take to facilitate such relationship formation. Building on theories of interorganizational tie formation, we explore how a venture can take strategic actions—in the form of public acts of deference toward higher-status potential partners—to make such asymmetric alliance formation more likely. Empirical results from a 11-year panel dataset of 2,436 firms in the medical device industry suggest that a venture engaging in proactive acts of deference toward a higher-status firm significantly increases the likelihood of their alliance formation. In addition, we find evidence suggesting that the effectiveness of deference on asymmetric alliance formation can be stronger or weaker depending on other characteristics of the exchange parties, such as their relative age and rounds of venture capital funding.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Yiwei Dou, Professor of Accounting
University: New York University, Stern School of Business
Time: Friday, February 26, 2021, 3:15 PM - 4:30 PM
Location: Zoom
Since 2012, all U.S. public companies must tag quantitative amounts in financial statements and footnotes of their 10-K reports using the eXtensible Business Reporting Language (XBRL). We conduct a fundamental analysis of this large set of detailed financial information to predict earnings. Using machine learning methods, we combine the XBRL data into a summary measure for the direction of one-year-ahead earnings changes. Hedge portfolios are formed based on this measure during the period 2015-2018. The annual size-adjusted returns to the hedge portfolios range from 5.02 to 9.7 percent. These returns persist after accounting for transaction costs and risk. Our strategies outperform those of Ou and Penman (1989), who extract the summary measure from 65 accounting variables using logistic regressions. Additional analyses suggest that the outperformance stems from both nonlinear predictor interactions missed by regressions and more detailed financial data in XBRL documents.
Host(s): Assistant Professor Jinfei Sheng
Speaker(s): Gordon M. Philips, Professor of Finance
University: Dartmouth College, Tuck School of Business
Time: Friday, February 26, 2021, 1:00 PM - 2:00 PM
Location: Zoom
We provide evidence that over the past 30 years, U.S. firms have expanded their scope of operations. Increases in scope and scale were achieved largely without increasing traditional operating segments. Scope expansion significantly increases valuation and is primarily realized through acquisitions and investment in R&D, but not through capital expenditures. We show that traditional concentration ratios do not capture this expansion of scope and are upward biased. After accounting for scope, we do not find evidence that industry concentration is increasing. Our findings point to a new type of firm that increases scope through related expansion, which is highly valued by the market.
Host(s): Assistant Professor Kenneth Murphy
Speaker(s): Susan Martonosi, Professor of Mathematics
University: Harvey Mudd College, Department of Mathematics
Time: Friday, February 26, 2021, 10:00 AM - 11:30 AM
Location: Zoom
Vaccine markets in the United States are vulnerable to the development of monopolies due to few manufacturers and high research and development costs. This work addresses how the government can ensure the cost-effective procurement of pediatric vaccines and the new COVID-19 vaccine from private manufacturers. The Centers for Disease Control and Prevention’s (CDC) significant patronage of vaccines affords them leverage in negotiating public-sector prices that prevent the formation of monopolies, but existing vaccine pricing literature excludes the CDC as a rational player. We combine optimization and game theoretic techniques to address cost-effective immunization. We present two case studies, one of the pediatric diphtheria-tetanus-pertussis vaccine (DTaP) and one of the recently developed COVID-19 vaccine. The talk then concludes with reflections on supervising research with undergraduate students. [Joint work with Banafsheh Behzad and Kayla Cummings.]
Host(s): Associate Professor Kevin Bradford
Speaker(s): David K. Crockett, Professor Moore Fellow
University: University of South Carolina, Darla Moore School of Business
Time: Monday, February 22, 2021, 1:30 PM - 2:30 PM
Location: Zoom
Marketing and consumer researchers have studied race, racialization, and racism in markets since at least the mid-20th century. Yet the discipline has not aggregated and integrated that knowledge into the same conceptual whole, which is necessary for knowledge-building. To correct this, I propose a theoretical framework that synthesizes racial formation theoretic approaches from sociological and Black history to account for racialized markets. The goal is to build knowledge about how market systems perpetuate and/or challenge ideas of race, processes of racialization, and the persistence of racial inequality.
Host(s): Assistant Professor Kenneth Murphy
Speaker(s): Lingxiu Dong, Professor of Operations and Manufacturing Management
University: Washington University in St. Louis, Olin School of Business
Time: Friday, February 12, 2021, 10:00 AM - 11:30 AM
Location: Zoom
For many supply chains, deep-tier suppliers, due to their small sizes and lack of access to capital, are most vulnerable to disruptions. Traditionally, because of the limited visibility in the deep-tiers, powerful downstream manufacturers’ financing schemes offered to their immediate upstream suppliers are not effective in instilling capital into the deep-tiers. Advancements in blockchain technology improve supply chain visibility and enable manufacturers to better devise deep-tier supply chain financing (SCF) to improve supply chain resilience. In this paper, we study the use of advance payment (AP) as a financing instrument in a multi-tier supply chain to mitigate the supply disruption risk. We compare the traditional system (deep-tier financing with limited visibility) and the blockchain-enabled system (financing with perfect visibility) to shed light on how blockchain adoption impacts agents’ operational and financial decisions and profit levels in a multi-tier supply chain environment. Our study of SCF in a three-tier supply chain finds that the improved supply chain visibility (by blockchain adoption) always benefits the manufacturer by enabling her to induce the desired operational risk-mitigation investment from the tier-1 and tier-2 suppliers. However, depending on the directional change in the operational risk-mitigation investment, which depends on the suppliers’ initial wealth levels, the tier-1 and tier-2 suppliers can be worse off. The “win-win-win” outcome takes place only when all operational risk-mitigation measures increase. Comparing the two types of blockchain-enabled SCF, delegated financing versus cross-tier direct financing, the manufacturer strictly prefers the latter. In contrast, the tier-1 supplier strictly prefers the former because the former endows the tier-1 with financial leverage over the manufacturer.
Host(s): Assistant Professor Chenqi Zhu
Speaker(s): Anil Arya, Professor of Accounting and MIS
University: Ohio State University, Fischer College of Business
Time: Friday, February 5, 2021, 3:00 PM - 4:30 PM
Location: Zoom
The typical view of firm actions and structure emphasizes enhancing efficiency by fully aligning incentives of all internal participants to achieve a common objective. Over the years, research in accounting, economics, and marketing has stressed how competition in output markets can alter this view. More recently, there has been an emphasis on how a firm’s concurrent participation in input markets, wherein strategic supplier considerations are in play, can further change the traditional view of firm actions. The seminar will seek to synthesize such results and present key considerations and conclusions that can be gleaned from this research. In particular, the emphasis will be on implications for decentralized organizational structure, transfer pricing, performance measurement, and cause marketing.
Host(s): Assistant Professor Jinfei Sheng
Speaker(s): Kelly Shue, Professor of Finance
University: Yale University, Yale School of Management
Time: Friday, January 15, 2021, 1:00 PM - 2:00 PM
Location: Zoom
Housing wealth represents the dominant form of savings for American households. Using detailed data on housing transactions across the United States since 1991, we find that single men earn 1.5 percentage points higher unlevered returns per year on housing relative to single women. If homeowners use leverage via a standard 30-year fixed rate mortgage, the gender gap grows significantly larger: men earn 7.9 percentage points higher levered returns per year relative to women. Approximately 45% of the gap in housing returns can be explained by gender differences in the location and timing of transactions. The remaining gap arises primarily from gender differences in execution prices: data on repeat sales reveal that women buy the same property for approximately 2% more and sell for 2% less. Women experience worse execution prices because of differences in the choice of initial list price and negotiated discount relative to the list price. Gender differences in liquidity, upgrade and maintenance rates, and preferences for housing characteristics and listing agents appear to be less important factors. Overall, the gender gap in housing returns is economically large and can explain 30% of the gender gap in wealth accumulation at retirement for the median household.
Host(s): Assistant Professor Jinfei Sheng
Speaker(s): Johannes Stroebel, Professor of Finance
University: New York University, Stern School of Business
Time: Friday, January 8, 2021, 1:00 PM - 2:00 PM
Location: Zoom
We use social network data from Facebook to show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties. This effect of social proximity on investment behavior is distinct from the effect of geographic proximity. Social connections have the largest influence on investments of small investors with concentrated holdings as well as on investments in firms with a low market capitalization and little analyst coverage. We also find that the response of investment decisions to social connectedness affects equilibrium capital market outcomes: firms in locations with stronger social ties to places with substantial institutional capital have higher institutional ownership, higher valuations, and higher liquidity. These effects of social proximity to capital on capital market outcomes are largest for small firms with little analyst coverage. We find no evidence that investors generate differential returns from investments in locations to which they are socially connected. Our results suggest that the social structure of regions affects firms’ access to capital and contributes to geographic differences in economic outcomes.
