Arison School of Business Weekly Seminar

Knowledge of Artificial Intelligence Predicts Lower AI Receptivity

As the field of artificial intelligence (AI) continues to progress and transform society, understanding the factors that influence receptivity towards AI has become increasingly important. Yet, little is known about how systematic differences across consumers predict AI receptivity. The current research investigates whether and how knowledge about AI influences consumers’ receptivity towards AI. To this end, we develop and validate an AI Literacy Test (AILT), a novel instrument designed to measure individuals’ objective knowledge about AI and algorithms. In contrast to three surveys documenting that most people expect greater AI knowledge to predict greater AI receptivity, the authors find the reverse; people with greater AI knowledge have a lower preference for using AI-based products and services. This reduction is not indiscriminate and is particularly pervasive for tasks that require more subjectivity to be performed well. However, greater knowledge of AI does not lead to increased AI utilization propensity among even highly objective tasks. These findings suggest that there may be unintended consequences of policymakers’ efforts to educate the public about AI, and that companies marketing AI product and services may need to re-evaluate which target segments may be more likely to adopt their technologies.

When Does Unequal Representation Reflect Bias? The Role of Political Ideology in Judgments of Distributional Outcomes

We examine judgments of bias in distributional outcomes. Such judgments are often based on imbalance in distributional outcomes, namely the under- or over-representation of a target group relative to some baseline. Using data from 26 studies (N = 14,925), we test how these judgments of bias vary with the target group’s characteristics (traditionally dominant or nondominant) and the observer’s political ideology (liberal or conservative). We find that conservatives set a higher threshold for recognizing bias against traditionally nondominant targets (women, Blacks, immigrants), as compared with liberals. Conversely, liberals set a higher threshold for recognizing bias against traditionally dominant targets (men, Whites, native-born citizens), as compared with conservatives. However, these relationships between political ideology and judgments of bias significantly diminish when the targets are unknown or ideologically irrelevant. These findings emphasize the context-dependency of bias judgments and underscore the importance of stimulus sampling and appropriate selection of controls.

Perceptions of Disability: Effects on New Product Design and Marketing

The needs of people with disabilities are often overlooked or misunderstood by society. Although we advocate a fully inclusive design approach, in which people with disabilities are integral to decision making processes affecting them, the current reality is that many decisions are still made on their behalf by others. As a result, marketers, product designers, policy makers, and individuals need to understand the needs of people with disabilities to create, market, and support products that better fulfill these needs. This research seeks to understand how observers may perceive (or misperceive) the needs of people with physical disabilities, identify inaccurate perceptions that may lead to suboptimal outcomes, and examine how these perceptions can be leveraged to improve outcomes. In contrast to prior work on dehumanization, which finds that observers diminish the importance of high-order (psychological) needs of “othered” groups, we find that observers elevate the importance of high-order needs of people with physical disabilities. Across nine studies, we identify this systematic bias and the resulting consequential decisions in the realm of product design and response to marketing campaigns. We discuss implications of these findings for managers, public policy, and future research.

Understanding When Green Engagement Initiatives Fail to Empower Material Recovery

Many businesses have moved toward a circular economy model, initiating bring-back programs to collect used packaging material from customers to be reintroduced into the manufacturing process—thereby closing the material loop. For these programs to be successful, firms must motivate individuals to voluntarily, and often effortfully, return used materials to the firm. In this project, we term material return “effortful recycling” and examine whether and how firms can increase the frequency of this desired behavior. Previous research has found that making material transformation salient can motivate recycling, and engaging individuals in firms’ decision-making processes can promote desired behaviors. Across seven studies, utilizing real disposal behavior and consequential choices, we assess whether, when, and why engaging individuals around recycling transformation can promote effortful recycling. When individuals were invited to vote on what their recycled item would become, they were more likely to effortfully recycle (Studies 1a-1b). However, some recycling outcomes feel unimportant to individuals (i.e., recycling a bottle to re-create another bottle) and voting on these outcomes reduced a sense of empowerment which, in turn, decreased participation in the material-recovery program (Studies 2-3). These effects are uniquely explained by individuals’ sense of meaningful goal attainment, rather than a sense of autonomy or competence in recycling (Study 4), nor are they influenced by the reusability of the recycled item (Study 5). Finally, the effects are unique to the context of material re-use and do not impact charitable giving contexts (Study 6). We conclude that firms’ engagement initiatives should be carefully tied to subjectively meaningful goals in order to promote desired green behaviors.

The Future of Motivation in and of Teams

The study of motivation in and of teams has flourished and expanded over the past few decades. We now have a better understanding of core motivational processes at the individual and team levels of analysis, along with cross-level processes through which individuals and teams influence each other. However, societal, cultural, economic, and technological changes have led to new forms of team-based designs and teaming strategies in work organizations. In this seminar, I will review five major changes to the nature of teams and teaming and identify fruitful avenues for future research that can generate new and important knowledge about the motivation of individuals in teams as well as the motivation of team systems as wholes.  I will also summarize recent research projects that illustrate some of the new directions noted.

WOLT FOR YOUR FOOD DELIVERY: USING PHONESTHEMES TO IMBUE NON-WORD BRAND NAMES WITH MEANING

A brand name is a fundamental component of a brand’s identity. This research introduces a novel linguistic tool for brand name creation: phonesthemes — sound and spelling letter clusters that are associated with one dominant meaning. For instance, -olt, one of over 140 phonesthemes in English, consistently appears in words related to the nose or breathing (sneeze, sniff, snort). I will present a series of studies revealing positive effects of phonesthemic non-word brand names (e.g., Glif; gl-; e.g., glow, glimmer; meaning ‘light’) on consumer preference, attitude, purchase intent, incidental memory, and choice when the dominant meaning activated by the phonestheme is semantically congruent with the product category or product attribute (e.g., luminant car wax), due to enhanced processing fluency. This research advances psycholinguistic research in marketing and the emerging area of brand linguistics by broadening the focus beyond brand name phonology.

Strategic Classification: Learning With Data That ‘Behaves’

The growing success of machine learning across a wide range of domains and applications has made it appealing to be used also as a tool for informing decisions about humans. But humans are not your conventional input: they have goals, beliefs, and aspirations, and take action to promote their own self-interests. Given that standard learning methods are not designed to handle inputs that "behave", it is natural to ask: how should we design learning systems when we know they will be deployed and used in social environments?

As a starting point, I will present the problem of strategic classification, in which users can modify their features (at a cost) in response to a learned classifier in order to obtain favorable predictions. I will then describe some of our work in this field, demonstrating how even mild forms of strategic behavior can dramatically transform the learning problem, and the role game theory can play in addressing some of the new challenges that arise. Finally, I will argue for strategic classification as a framework that can be useful for formally reasoning about learning under user behavior in general, and which holds potential for weaving more elaborate forms of economic modeling into the learning pipeline.

Online Cart Abandonment: The Effect of Cart Composition

Online shopping cart abandonment is widespread, causing major losses in potential sales revenues to e-commerce companies. This research complements academic and industry efforts to mitigate cart abandonment and increase sales revenues by investigating whether the composition of the products in the cart influences abandonment. Specifically, we hypothesize that the more hedonic items are placed in the shopping cart the more likely consumers are to abandon the entire content of the cart, including both hedonic and utilitarian products. We base this assertion on findings that consumers experience more guilt regarding hedonic than utilitarian products because choice of hedonic products is more difficult to justify (Dahl, Honea, and Manchanda 2003; Khan and Dhar 2006). Analysis of a large-scale field dataset and data from three controlled experiments provide converging evidence for the research hypothesis, rule out alternative explanations, and examine avenues for practitioners to reduce cart abandonment.

We Are What We Watch: Movie Plots Predict the Personalities of Their Fans

How do movie contents relate to the psychological makeup of the audiences they attract? We explore this question by employing advanced analytical tools to a rich dataset combining detailed characterizations of movies and their plots with personality measures of their social-media fans.

We identify novel associations between movie features such as quality and genre, and the personalities of their fans. We then show that movie plots—captured via text—predict the aggregate personalities of their fans beyond all other variables studied. We further quantify how different psychological themes (e.g., leisure) and unique concepts that organically emerge from the data (e.g., adultery) relate to fans’ personalities, and show that movie plots align with the characteristic ways in which their fans think, feel, and behave (e.g., social films attract extraverted fans). Our findings provide fine-grained mappings between personality dimensions and movie preferences, facilitating automated assessment of audience psychographics at scale

System 3: The Drive for Marketplace Dignity

The articulation of processes often referred to as “system 1” and “system 2,” in behavioral economics has sparked huge advances in our understanding of human behavior. However, it is unclear how this combination of rationality and irrationality informs the design of ethical marketing practices, or help us to understand the types of phenomena that seem to arise in response to deeply-felt personal or societal wrongs. To address these gaps, conceptualize and operationalize a third system – one focused on the drive for dignity. In this paper, we begin by defining dignity as affirmed or denied in the marketplace, showing its unique pattern of cognitive and affective effects. We then identify three key determinants of marketplace dignity: representation, agency and equity. Experimental work subsequently demonstrates that dignity can be systematically analyzed and accounted for in designing customer experiences. Further, data suggests that when optimized, satisfying the drive for dignity can offer strategic advantages for firms.

IDENTITIES WITHOUT PRODUCTS:
WHEN PREFERENCE FOR SELF-LINKED PRODUCTS WEAKENS

Extant literature and real-world marketing strategies converge around the idea that the more self-linked a consumer is to her iPhone, the less likely she will be to switch brands. In the current paper, we challenge this view by revealing circumstances where the effect of self-links on preference is attenuated. We introduce a theoretical distinction between two types of consumer identities based on whether expressing the identity relies on specific products: product-dependent (e.g., chef) and product-independent (e.g., foodie). We theorize that self-links to products will become less relevant and, therefore, less cognitively available to influence preference when a product-independent identity is prominent. Five experiments in the context of consumer leisure identities confirm that priming a product-independent (vs. product-dependent) identity weakens preference (e.g., brand loyalty, WTP) for self-linked products/brands, and can even strengthen preference for negatively self-linked (dissociative) products/brands among materialists. A sixth experiment and a Facebook field study show that the chronic product (in)dependence of a consumer’s identity can be either measured directly or inferred indirectly from social media interests, thus enabling marketers to target consumers who will be most receptive to brand switching appeals. These findings explore for the first time what happens when consumers construe the self as independent not from people but from products, and point to far-reaching implications for marketing theory and practice.

A match made in cyber-heaven: Combining user-generated images and text for brand positioning analysis

User-generated content is a unique data source, but also comes with significant challenges for marketing applications such as brand positioning analysis. On the one side, marketers can assess brand-specific associations and derive market structure in great detail. On the other side, its sheer volume, multi-modality, and publication on various platforms pose a challenge. This study demonstrates the added value of integrating text and image data for brand positioning analysis. Leveraging deep learning techniques, a new framework enables jointly modeling both data types. Looking at the market of running shoes and motorcycles, empirical evidence supports that user-shared texts and images on brands do convey different aspects. Specifically, text is frequently used to communicate functional brand associations while images typically capture symbolic brand associations. Various quantitative and qualitative evaluation techniques consistently support the added value of considering different data types from different platforms. Relying on such an approach can increase both the completeness and precision of two key tasks in brand positioning analysis

The Challenge of Shaping a Participatory Corporate Governance

A key theoretical issue in the behavioral theory of the firm and corporate governance research concerns the design elements of the board of directors (BOD) that lead to participatory corporate governance. Utilizing a game-theoretic approach, we explore how such fundamental design elements as CEO duality and CEO/Chairperson role separation affect BOD participative decision-making. Specifically, by considering endogenous formations of power distribution and coalitions among the directors, our findings reveal that both design settings lead to an expanded dominant coalition in the BOD. Concurrently, however, they also endow non-CEO/Chairperson directors with limited power to influence decision-making, thus failing to generate checks and balances in the board. We demonstrate and discuss the tradeoffs in these design settings in generating an effective participatory corporate governance.

Tracking Consumers: The Trade-off between the Value of Granular Data and Consumers’ Privacy

In recent years, most mobile apps have started tracking consumers’ location and movement patterns. This type of tracking can allow firms that access these data to better predict consumers’ future behaviors and to send targeted communications. However, such tracking also raises privacy concerns among app users and regulators. This results in potential trade-offs between the value of granular tracking and privacy concerns. This paper examines three related questions. First, we examine whether granular tracking data add value when predicting consumers’ retail visits relative to traditional metrics, such as consumers’ demographics and past behaviors. Second, we examine whether the granularity (i.e., frequency) with which these data are tracked impacts the accuracy with which we can predict future retail visits. Finally, we examine if there is heterogeneity in the value of granularity by firm type. We address our research questions by leveraging individual-level driving data tracked via a mobile app for 31,530 consumers in Texas and their restaurant visits over 14 months between September 2018 and October 2019. We propose a machine learning (ML) framework to extract informative features from granular tracking data on consumer mobility, quantify the value of these data for predicting visits, and evaluate our model’s performance under various counterfactual policies that vary the frequency with which apps can track their users. Our results show that the accuracy of prediction algorithms improves by 21% with granular tracking data relative to models that use only demographic and behavioral information on past visits. However, when tracking data are collected at longer intervals, the performance of ML algorithms decreases, but these algorithms still outperform models that use only information on demographics and past behavior. We also find that a deep learning transformer model that uses the entire sequence of latitude-longitude coordinate pairs as input outperforms the ML models by 19% in accuracy but is more computationally expensive. Our models perform significantly better with more (vs. less) granular data for non-chain rather than chain restaurants. Finally, we show an extension of our model to evaluate the app’s targeting policies

The effect of the weekend on individuals’ behaviors and decisions has been established in a variety of domains. In this research the authors contribute to these findings and investigate the effect of weekends in the domain of online reviews. They first establish that reviews written on the weekend carry systematically lower rating scores than reviews written during the week and show the robustness of this “weekend effect” across contexts. The weekend effect of online reviews is surprising given that literature usually reports higher levels of happiness and better mood on weekends. We test an extensive set of potential drivers and find strong evidence that the weekend effect is driven by a temporal reviewer selection, i.e., a different kind of reviewer being active at different times of the week and a general “weekend-loneliness” that worsens ratings written on the weekend. Evidence for these explanations stems from a multimethod approach including secondary data (a quarter billion online reviews from 5 different platforms), an observational study, text analysis and a lab study. Given the relevance of online reviews for consumers’ decisions, the authors demonstrate important managerial implications, by showing with solicited reviews from the field, that organizations should not try to trigger users to review on the weekend.

Denied by an (Unexplainable) Algorithm: Teleological Explanations for Algorithmic Decisions Enhance Customer Satisfaction

Automated algorithmic decision-making has become commonplace, with firms implementing either rule-based or statistical models to determine whether to provide services to customers based on their past behaviors and characteristics. In response, policymakers are pressing firms to explain these algorithmic decisions. However, many of these algorithms are “unexplainable” because they are too complex for humans to understand. Moreover, legal or commercial considerations often preclude explaining algorithmic decision rules. We study consumer responses to goal-oriented, or “teleological,” explanations, which present the purpose or objective of the algorithm without revealing mechanism information that might help customers reverse (or prevent future) service denials. In a field experiment with a technology firm and in several online lab experiments, we demonstrate the effectiveness of teleological explanations and identify conditions when teleological and mechanistic explanations can be equally satisfying. Whereas the epistemic value of explanations is well established, we study how explanations mitigate the negative impact of service denials on customer satisfaction. Yet in situations where companies do not want to, or cannot, reveal the mechanism, we find that teleological explanations create equivalent value through the justifications they may offer. Our results thus show that firms may benefit by offering teleological explanations for unexplainable algorithm behavior.

Co-Created Parasocial Connections Enhance Influencer Effectiveness

Social media influencers are ordinary consumers, with no prior formal experience, who engage in public consumption. Yet despite this lack of institutional legitimacy, the influencer market is rapidly growing. How do influencers gain the credibility necessary to be effective persuasion agents? Across 5 studies (N = 2,447) and one field dataset, we demonstrate that the relational elements of influencer marketing are key to enhancing influencer effectiveness. Building on theories of self-determination and intimate self-disclosure, we propose that both consumers and influencers play a crucial role in facilitating the formation of online parasocial, or one-sided, connections. We show that these co-created parasocial connections lend credibility to influencers, and that this credibility increases both consumers’ engagement with the influencer and their willingness to consider the products the influencer promotes. We additionally identify important boundary conditions, such as inappropriate self-disclosure, as well as individual differences in consumers’ propensity for parasocial connections, that moderate the relationship between parasocial connection strength and influencer effectiveness.

Does Setting a Time Limit Affect Time Spent?

From social media and streaming services to audiobooks and online games, many companies (e.g., TikTok, Instagram, YouTube) have recently introduced the option for consumers to set “time limits” on their platforms. These features invite consumers to select an amount of time after which they would like to receive a notification about their behavior. But while giving consumers this option may be well intended, how does it actually impacttime spent? Contrary to expectations, rather than leading consumers to spend less time on an activity, nine pre-registered experiments demonstrate that setting a time limit can have the opposite effect. This occurs because consumers implicitly treat time limits like budgets, perceiving time up to the limit as earmarked for the activity and facilitating such spending. Consequently, setting a time limit (vs. not) can increase time spent. The findings further understanding of the impact of new technologies, the consequences of personal quantification, and time budgeting. Further, they have clear implications for the use of limits as a time management tool: merely providing the option to set a time limit may be insufficient to benefit consumer wellbeing.

The Donor's Choice Dilemma

We hold the assumption that "any help is better than no help", but do people follow this assumption when they need to choose between two needy recipients?
In ongoing research, we first find that when prospective donors need to choose one of two similarly deserving recipients, they experience a moral conflict. Choosing to help one recipient seems unfair toward the unchosen recipient. Due to this moral conflict between the wish to help and the wish to do so in a fair manner many prospective donors prefer not to donate to any recipient (“opt-out”) acting against the intuition that "any help is better than no help". Next, we identify the types of choice sets that yield the highest opt-out rates. We then focus on one particularly interesting case which attenuates the opt-out rates – a case where donors need to choose between helping a boy or helping a girl. Overall, opt-out rates decline suggesting that gender can help mitigate the moral conflict of choosing one over the other. Interestingly, we find that in a western culture (US), participants chose to help a girl over a boy, in support of the western stereotype that women are needier than men. However, in an eastern culture (Chinese), the effect is reversed, and donors prefer to help the boy over the girl in support of male favoritism in eastern societies.

Human Resources Analytics: Implications for Theory and Practice

Human Resources Analytics (HRA) is the application of business analytics and data science techniques to the field of Human Resources Management. In this talk, we will follow the basic Human Resources Management process: Recruitment – Selection – Turnover – Retention; yet from the inductive HRA perspective. We will demonstrate and discuss, through real-world analysis, what can HRA tell us when examining the new world of work.
In this ongoing research, we examine individual and organizational features and show how HRA may be used on large scale datasets to address key managerial and organizational challenges. Specifically, how does HRA support both known and established theories—so far based on deductive data-driven hypothesis testing—as well as for extracting hidden behavioral patterns from the data, including some surprising and counter intuitive patterns. Part of the analysis explores digital expressions extracted from digital footprints. We will discuss potential implications for theory and practice.

"Change - Attention - Change"

This talk is about behavior change. A thesis about how attention can help people understand, detect, predict, and influence change will be presented. This thesis of change builds on the philosophy of the Dao, on cognitive behavioral theory (CBT), and on behavioral economics. The majority of the talk will focus on empirical evidence from two different research projects, one about incentives and the second about ephemerality.

Dynamic Effects of Ad Content on Ad Liking: From Pretesting to Generalization

Advertising agencies seek to test their video advertisements based on consumers’ reactions.
The ad’s content evolves scene-by-scene at five second intervals as per the storyboard, whereas ad liking builds every second. How should advertising managers quantify the effects of ad content on ad liking for a specific video ad using asynchronous data on content and liking? To this end, we develop a method to estimate and infer dynamic models using asynchronous time series. We first demonstrate its efficacy in simulation studies. Then we apply the proposed method to a single video ad and provide diagnostic information on which video scene(s) to edit and which ad content to modify. Finally, we conduct a meta-analysis of one hundred video ads to generalize the effects of content on ad liking. This meta-analysis, which is the first study of its kind, shows how an ad’s plot structure shapes content effects, which generalize across the industry sectors.

The present research introduces and conceptualizes the paradoxical phenomenon of “pseudo-secrets” in marketing and examines its appeal and impact on real consumer behavior in the marketplace. Restaurants ranging from gourmet Michelin-starred to mainstream fast-food chains offer secret menu items, and hidden stores and “speakeasy” bars feature camouflaged entrances and secret passcodes. Paradoxically, many of these hidden places and products are famous for being a secret. We argue that pseudo-secrets often hold important symbolic value: they make consumers feel socially central. Accordingly, we demonstrate that pseudo-secrets increase word-of-mouth about the brand, and this effect is mediated by consumers’ feelings of social centrality – the subjective experience of feeling connected and focal in one’s network, and attenuated when the symbolic value of the secret is low. We further demonstrate that pseudo-secrets can even create WOM about unexciting items and experiences, reaching the level of WOM found for their iconic and desirable counterparts. Our multi-method approach, combining field experiments, company proprietary data, and lab studies, further demonstrates how marketers can effectively apply these insights and design pseudo-secrets in various product categories and consumption contexts.

Brand communities have an unparalleled power to integrate customer value with brand growth. Customers rely on brand communities tointeract with each other, to connect with the brands they love, to solve problems, and to personalize their consumption experiences. However, customers also resort to these communities to coordinate a negative collective crisis response. An uncontrolled reaction of online brand communities to brand crises can deteriorate brands' value and market performance, and push loyal and engaged consumers away from the brand social network. In this project, we assess the effect of brand crises on customer participation in online brand communities, and on ease and speed of information spread in the brand networks. We use data from 300 online brand communities, and exploit the quasi-experimental exposure of community members to over 7000 brand crisis episodes reported by news providers between 2010 and 2019. In a series of difference-in-difference analyses, we find that brand crises (i) increase the weekly participation of consumers in brand communities, (ii) affect the patterns of information-sharing in the brand networks, and (iii) have a differential impact on different consumer types. The "brand loyal" consumers effectively disengage from their brand communities following the crisis event -- therefore, the average boost in brand-related activity is attributable to "brand strangers", people who only activate after a crisis. However, we show that the decrease in engagement is mitigated among the active consumers who had proportionally more experience, loyalty, or status within the brand community. Accordingly, we suggest that brand crises are a serious threat to the integrity of online brand communities, but that consumer loyalty and commitment has the potential to preserve the functioning of brand spaces online in case of serious reputation threats. The insights from this paper support businesses and organizations managing online communities in situations of external stress and unexpected reputational threats.

Peer-to-peer live streaming is a rapidly growing phenomenon: in 2020, users spent over 27 billion hours viewing live streams online, nearly doubling the hours spent in 2019. We examine the viewing experiences of over 2,700 consumers in both naturalistic and carefully controlled environments and find consistent positive effects of viewing online live streams (versus identical pre-recorded videos) on feelings of social connection. We find evidence that liveness itself enhances feelings of connection to the broadcaster, and that this benefit of viewing live streams is driven by an elevated sense of presence, or "being there," in events that are viewed in real-time. We also find that increased salience of other viewers enhances feelings of connection to others who are watching the same events at the same time. The social connection afforded by viewing live streams enhances consumers' enjoyment and increases their propensity to continue watching similar content. In a world where people increasingly turn to technology to satisfy their social needs, live streams present a novel opportunity for consumers to feel connected and for marketers, platform developers, and media personalities to enhance the experiences of their viewers.

Reviews have a powerful influence on product success and, accordingly, have received ample research attention. However, so far, interest in reviews has effectively stopped at checkout. This research shows that exposure to reviews—and, particularly, negative reviews—matters beyond the point of purchase, and can shape the consumption experience itself.

First, we demonstrate, for the first time, that pre-consumption exposure to reviews leads consumers to experience the reviewed products differently from naïve consumers (who experience products without reading reviews). Reviews affect the consumption experience, and not merely reports of the experience, and do so regardless of product quality and whether consumers choose the product or have it chosen for them. Second, we show that the effect is asymmetric, such that negative reviews produce more negative consumption experiences than naïve consumption, whereas positive reviews do not significantly affect experience. Third, we identify negativity bias as a mechanism for this asymmetry: We show that pre-consumption exposure to negative reviews (vs. positive reviews or naïve consumption) elicits a stronger focus on negative attributes of the product experience and, consequently, make it worse. Fourth, we show that negative consumer information has a more pronounced (negative) impact on other consumers' consumption experience compared to negative information generated by a marketer.

 Overall, this research advances the review literature by identifying and characterizing (potentially detrimental) effects of reviews after the point of purchase, something they were neither aimed nor expected to affect. Our work generates novel insights that are immediately applicable to the practice of marketing.

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We show that reviewers (on platforms like IMDB and Goodreads) tend to provide ratings in "batches," or several ratings for different items in a short time period. Our analysis of more than 16M ratings and more than 1M reviewers shows that the ubiquity of ratings given in "batches" ranges between 12.41% to 92.88% for various platforms. We further demonstrate that ratings within a batch have unique and consistent distribution. Findings from two large-scale databases—incorporating data from more than 100M ratings and 500K reviewers in a time resolution of seconds—reveal that ratings (by the same reviewer) within these "batches" drop as a function of time. In four experimental studies we replicate the decrease in sequential ratings and shed light on the role of doubt in explaining this drop. We show that reviewers have stronger doubt about later ratings. Such doubt is reflected not only via the decrease in rating score over time, but also via the length of time that passes between sequential ratings. The drop is attenuated when the reviewer has low (compared to high) levels of doubt about later ratings. Our research contributes to the literature on sequential decisions, the batch (or “burst”) phenomenon, and doubt in evaluation and online reviews. We also offer useful practical implications for platforms seeking to create a more reliable ranking scale.

In recent years, billions of dollars are spent, by both online and offline retailers, on website design aimed at increasing consumers’ online engagement. Yet, it is not clear that increasing consumers’ online engagement is the optimal strategy to increase sales. In this talk I will present three studies that examine the potential negative and positive effects of digital engagement. In the first paper, we study the relationship between online engagement and offline sales, utilizing a quasi-experimental setting whereby a leading premium automobile brand launched a new interactive website gradually across markets, allowing for a treatment-control comparison. The paper finds surprising evidence suggesting that increased online engagement reduces offline car sales. This negative effect is due to substitution between online and offline engagement, as the high-engagement website decreased users’ tendency to submit online requests that lead to personal contact with a car dealer. The second study focuses on video as an engagement feature. Using a field experiment of 53 different apps, we find that for 85% of the apps, video inclusion has either a negative or no significant effect on downloads. Moreover, the results show that viewing is negatively associated with installs, while viewing to completion is positively associated with installs, and that video has negative effects on other engagement activities. We further examine the effect of using video in an online lab experiment and find heterogenous effects by user interest and gender. In the third study, two lab experiments were conducted to isolate and measure the effects of three engagement features (gallery scroll, read more description, and read reviews). Results suggest that app description has a negative effect on downloads for users who selected an app category which differed from their favorite one, while no causal effect was identified for gallery scroll and reviews.

Research suggests that employees in startup firms are more likely to become founders of entrepreneurial ventures than employees in large corporations. However, a particular mechanism has not yet been identified. In the current research, we are drawing on role categorization and self-efficacy theories to explain this phenomenon. Specifically, we propose that employees who work in startup firms are more likely to categorize the roles they enact and the skills they acquire as pertaining to entrepreneurship. As a result, they become more confident in their abilities to succeed as entrepreneurs and more likely to consider an entrepreneurial career path. To examine our prediction, we adopt an experimental approach and randomly assigning participants to complete identical work categorized either as “startup” related or “large corporation” related. In two experiments, we found that subjects who performed work for a “startup” were more likely to indicate intentions to start a business, and also to take more entrepreneurship classes, than participants who performed the exact same tasks for a “large corporation”. In addition, entrepreneurial self-efficacy mediated the relationship between work categorization and entrepreneurial intentions. In my presentation I will further discuss on how these findings could help generate a set of insights for entrepreneurship programs and intrapreneurship.

Humans navigate a world of agents: people first, but also animals, societal groups, and corporations, as well as more and more AI. This complex landscape of autonomous entities yields to a simple but powerful human judgment of others’ intent for good or ill, plus their ability to enact it. Agents mapped in a warmth x competence space explain phenomena from stereotyping under increasing diversity to economic decision-making under uncertainty. Evidence from surveys and experiments over time and place—including some adversarial collaborations—suggest apparently universal dimensions that allow humans to respond to a rapidly changing world.

An increasing proliferation of misinformation and “fake news” has been widely reported and documented. Reality is now under attack from advertising-optimized information architectures mediating our contemporary reality. Disinformation campaigns proliferate on online forums and social media. My research program aims to answer questions regarding why we believe and share fake news and how to prevent or correct inaccurate beliefs. In a paper titled “Perceived Social Presence Reduces Fact-Checking” (Proceedings of the National Academy of Science 2017), we find that consumers are less likely to fact check ambiguous news headlines when they feel they are in the presence of others compared to when they are alone. We find that this reluctance to fact check is caused by reduced vigilance in group settings such as social media. A second paper examines how to involve consumers in fact-checking news articles. We propose a method to leverage the input of general consumers (crowdsourcing), algorithms (supervised learning), and experts (third-party fact-checkers) to rate the veracity of scientific articles on news media. We propose and test the use of similarity judgments to facilitate unbiased consumer responses. In a third research project, I address the issue of fake news from a different perspective and examine sharers of fake news. Who are they, and what motivates them to share fake news? We contrast fake news sharers, fact-check sharers, sharers of news articles from general media outlets and a random sample of social media users across five dimensions-demographics, political ideology, social media usage, emotions and personality. We access these characteristics by collecting their personal information as posted on Twitter as well as the content of their tweets. Fake news sharers differ from the other groups on multiple characteristics, but they also show similarities to fact check sharers on their emotional profile. Our findings can help social platforms to screen, prioritize and scrutinize messages posted by potential fake news sharers before false messages are widely disseminated. A fourth project in this research stream titled “Social Marginalization Motivates Indiscriminate Sharing of COVID-19 News on Social Media” (Journal of the Association of Consumer Research 2020), finds that people who feel socially marginalized are more likely to share COVID-19 news indiscriminately. They are likely to share news that is factually untrue and true, as well as news that seems surprising and unsurprising. This effect is driven by a general motivation to seek meaning. Helping people obtain a temporary sense of meaning by endowing them with a feeling of power can reduce indiscriminate news sharing. Taken together, this research program aims to guide policy discussions on how to combat the spread of fake news.

Don’t put an abstract in the meanwhile

As the quantity and value of data increase, so do the severity of data breaches and customer privacy invasions. While firms typically publicize their post-breach protective actions, little is known about the social, behavioral, and economic aftereffects of major breaches. Specifically, do individual customers alter their interactions with the firm, or do they continue with “business as usual”? We address this general issue via data stemming from a matchmaking website, one for those seeking an extramarital affair, that was breached. The data include de-identified profiles of paying male users from the United States, and their activities on the website since joining, and up to 3 weeks after, the disclosure of the data breach. A challenge in making causal inference(s) in the setting of a massive and highly publicized data breach is that all users were informed of the breach at the same time. In such cases of “information shock”, there is no obvious control group. To resolve this problem, we propose Temporal Causal Inference: for each group of users who joined in a specific time period, we create an appropriate control group from all users who had joined prior to it. This procedure helps control for, among other elements, potential trends in both individual and temporal site usage that broadly fall under the rubric of “normal” usage trajectories. Following construction of suitable control groups, we apply and extend several causal inference approaches. In particular, we adapt Athey, Tibshirani and Wager’s (2019) Causal Forests (among other forest-based methods) into Temporal Causal Forests, to better align ‘temporal’ inference settings. The combination of Temporal Causal Inference and Temporal Causal Forests methods allows us to extract insights regarding the homogenous (average) treatment effect, along with nontrivial heterogeneity in responses to the data breach. Our analyses reveal that there is a decrease in the probability of being active in searching or messaging on the website, and a notable increase in the probability of deleting photos, ostensibly to avoid personal identification. We investigate several potential sources of heterogeneity in response to the breach announcement, and conclude with a discussion of both managerial consequences and policy considerations.

Marketing seminar

Marketing seminar

Marketing seminar

Despite well-meaning intentions, people rarely allocate their charitable donations in the most cost-effective way possible. Whereas most early research on ineffective altruism focused on donation decisions in the absence of effectiveness metrics, the growth of the effective altruism movement has led to much improved information and transparency intended to help people make more effective donation decisions. These changes to the decision making environment yield new research questions about how people decide how much and to whom they should donate. In this talk, I plan to cover two papers: In the first paper, we demonstrate that a common form of effectiveness information provision—the cost per unit (e.g., cost of a malaria net, cost to save of life) has a perverse effect on how much people give. Specifically, people give less when the cost is cheaper. This result arises because people want their donation to have a tangible impact, and when the cost of such an impact is lower, people can achieve it with a smaller donation. In the second paper, we demonstrate that when choosing between charities to support, few choose the charity with the highest return, even when it is obvious which option is “best”. The key reason why is people construe of this decision as a matter of subjective preferences, and thus are willing to do less good overall if they can support the cause they prefer. I will end the talk by discussing how effectiveness information provision is not a cure-all for improving donation decisions, but can be improved upon with better framing.

Crowdfunding platforms are peer-to-peer two-sided markets that enable amateur entrepreneurs to raise money online for their ventures. However, in allowing practically anyone to enter, such platforms enable opportunistic suppliers to flood the market with offerings, many of which are of low quality. This situation creates choice overload for potential backers and may thus influence their investment decisions. To empirically study the implications of this phenomenon for crowdfunding performance, we use a quasi-natural experiment in the form of an exogenous media shock that occurred on Kickstarter.com. The shock was followed by a sharp increase in the number of campaigns, particularly low-quality ones, offered on the supply side of the market; no such increase was observed on the demand side of the market. These unique conditions enable us to estimate how crowdfunding platforms are affected by the presence of an atypically large number of low-quality campaigns, while controlling for fluctuations in demand. We use two identification strategies, which enable us to control for changes in quality, to show that an increase in low-quality supply significantly decreases the performance of the average crowdfunding campaign, manifested in a lower likelihood of success (reaching funding goals) and less money raised per campaign. We also offer a new measure to estimate campaign quality and study the moderating role of campaign quality in the observed effects. We find that high-quality campaigns are less affected than low-quality campaigns by the influx of low-quality offerings. In the talk, I would also discuss theoretical implications as well as managerial implications for entrepreneurs and platform designers. This talk is based on an MISQ forthcoming paper, co-authored with Hilah Geva and Gal Oestreicher-Singer.

We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the science, technology, engineering and math fields. This ad was explicitly intended to be gender neutral in its delivery. Empirically, however, fewer women saw the ad than men. This happened because younger women are a prized demographic and are more expensive to show ads to. An algorithm that simply optimizes costeffectiveness in ad delivery will deliver ads that were intended to be gender neutral in an apparently discriminatory way, because of crowding out. We show that this empirical regularity extends to other major digital platforms.

Graded and Context-Dependent Technology Associations: Why Using a Smartphone Can Signal Both More and Less Social Connection

There’s No Free Lunch Conversation: The Effect of Brand Advertising on Word of Mouth

Post-Consumption Susceptibility of Online Reviewers to Random Weather-Related Events

n Dubious Battle: Issue salience, Polarization, and Corporate Social Counterpositioning

Consumer Search and Decision-Making in HyperComplex Decision Environments

When Signaling Status Backfires: How Signals of Self-Interest Undermine Cooperation

We examine why corporate social responsibility (CSR) practices vary across countries and firms, and the value implications of such variation. Using Thomson Reuters’ ASSET4 database on the CSR practices of 4,279 firms from 49 countries over 2003–2015 and employing a hierarchical linear model, we find that the national cultural dimension of individualism is positively associated with firm-level CSR practices. We further find that income inequality at the country level and board diversity and corporate transparency at the firm level link individualism to CSR practices. Moreover, both between and within countries, we find a positive association between firm-level CSR practices and firm value, with two firm-level channels—cost of equity and bankruptcy probability—linking CSR practices to firm value. Finally, we find that the positive association between firm-level CSR practices and firm value is stronger in more individualistic countries.

Increased availability of alcohol might harm individuals if they have time-inconsistent preferences and consume more than planned before. We study this idea by examining the credit behavior of low-income households around the expansion of the opening hours of retail liquor stores during a nationwide experiment in Sweden. Consistent with store closures serve as commitment devices, expanded operating hours led to higher alcohol consumption and greater consumer credit demand, default, and negative consequences in the labor market. Our calculation shows that the effects of alcohol consumption on indebtedness could amount to 3.2 times the expenditure on alcohol.

This paper studies how the durability of assets affects financing. We show that more durable assets require larger down payments making them harder to finance, because durability affects the price of assets and hence the overall financing need more than their collateral value. Durability affects technology adoption, the choice between new and used capital, and the rent versus buy decision. Constrained firms invest in less durable, otherwise dominated assets and buy used assets. More durable assets are more likely to be rented. Economies with weak legal enforcement invest more in less durable assets and are net importers of used assets.

The money management firm is an ideal place to study both asset pricing and corporate finance questions. There are few corporations that allow the level of transparency that a mutual fund firm offers – these firms offer a window into human decision making that is hard to match elsewhere. Consequently, the area offers a unique and rich opportunity for new and innovative research. Historically, researchers have ignored these opportunities because the literature on money management has inconsistently applied the rational expectations equilibrium concept. When applied consistently, the rational expectations equilibrium approximates the observed equilibrium in the money management space at least as well as it does in the stock market. Just as the application of the rational expectations equilibrium transformed the stock pricing literature, the consistent application of this paradigm to the money management literature has the potential to transform the money management literature.

We construct and analyze the equilibrium of a model of delegated portfolio management in which money managers signal their investment skills via fund transparency. To lower the costs of transparency, high-skill managers rely on their performance to separate from low-skill managers over time. In contrast, medium-skill managers rely on transparency to separate, especially when it is difficult for investors to tell them apart through performance alone. Low-skill managers mimic high-skill managers in opaque funds, hoping to replicate their performance and compensation. The model yields several novel empirical predictions that contrast transparent and opaque funds.

We study how owners trade off the costs and benefits of establishing a board in a historical setting, where boards are optional and their role can be identified from cross-sectional differences in authority allocation across the general meeting, the board, and management. We find that boards arise when numerous small shareholders own a sizeable fraction of equity, thereby aggravating collective action problems. Boards monitor but also mediate among large and small shareholders as voting caps limit blockholders' influence and help to ensure that small owners' interests are represented on the board. In some _rms, boards arise mainly to advise.

Analytical research suggests mandatory periodic reporting disciplines disclosure and encourages timely voluntary disclosure. We examine this hypothesis using the shock to financial reporting quality experienced by former Arthur Andersen clients after they were forced to switch auditors. Consistent with the confirmatory role of mandatory reporting, we find that former Andersen clients increase disclosure following the switch. They increase forecasting frequency and enhance forecasting precision and specificity. They also show less return concentration around earnings announcements in bad-news quarters, consistent with timelier release of bad news (Roychowdhury and Sletten 2012). We supplement our main findings with a battery of tests to rule out the role of alternative shocks in our results. Our findings demonstrate complementarity between financial reporting quality and voluntary disclosures.

Model-Free Mispricing Factors

We identify model-free mispricing factors and relate them to global stock prices and investor beliefs. The factors are model-free as they measure variation in the relative prices of assets with the same cash flows. We design three factors to reflect the beliefs and capital flows of important clientele: investors in United States (US), developed, and emerging stock markets; and individuals and institutions. Together the three factors capture most (52%) of the systematic variation in price premiums of individual securities. These factors strongly predict US, developed, and emerging market stock returns. They also explain most (58%) of the variation in US stocks’ valuation ratios. Further evidence suggests that stock mispricing is related to investor overreaction to long-term cash flow news and limits to arbitrage.

Bank Branch Density and Bank Runs

Bank branch density, defined as the number of bank branches to total deposits, has significantly declined over the past decade, fueled by a confluence of branch closings and the almost doubling of deposits between 2016 and 2022. During this period, banks with low branch density benefited from large deposits inflows, leading to even lower density. But the virtuous cycle of deposits growth in these banks stopped spinning when investors became wary about their financial health. Stock prices of banks with low branch density plummeted during the 2023 Banking Crisis as these banks experienced larger outflows of uninsured deposits. Our results suggest that digital banking enabled banks to grow faster and attract uninsured deposits, but those large deposits inflows took the form of “hot money” that changed its course when economic conditions worsened.

Exchange Rates and Asset Prices in a Global Demand System

Using international holdings data, we estimate a demand system for financial assets across 36 countries. The demand system provides a unified framework for decomposing variation in exchange rates, long-term yields, and stock prices; interpreting major economic events such as the European sovereign debt crisis; and estimating the convenience yield on US assets. Macro variables and policy variables (i.e., short-term rates, debt quantities, and foreign exchange reserves) account for 55 percent of the variation in exchange rates, 57 percent of long-term yields, and 69 percent of stock prices. The average convenience yield is 2.15 percent on US long-term debt and 1.70 on US equity.

Miner Collusion and the Bitcoin Protocol

Bitcoin users can offer fees to the miners who record transactions on the blockchain. We document the blockchain rarely runs at capacity, even though there appears to be excess demand and higher fee orders are not always prioritized. We show this is inconsistent with competitive mining, but is consistent with miners exercising market power. If users believe that only high fee transactions will be executed expeditiously then we show how strategic capacity management can be used to increase fee revenue. Using a novel data set, we present evidence consistent with strategic capacity management. We show that mining pools facilitate collusion, and estimate that they have extracted least 300 million USD a year in excess fees by making processing capacity artificially scarce.

From Profit to Purpose: Firms as Private Providers of Public Goods

This paper studies how firms allocate their CSR expenditures to shed light on the potential social effects of corporate contributions to public goods. We use a novel dataset on the CSR expenditures of all large publicly traded firms in India over the period 2015-2019, which includes detailed information on the projects and social causes firms invest in. Using textual analysis methods, we construct an index of the technological proximity of firms' industries to social causes to capture the extent to which firms use their production processes for CSR projects. We find that firms spend more on causes they have a comparative advantage in. Seen through the lens of a large theoretical literature on CSR desirability, this suggests that firms' CSR spending is potentially welfare-increasing in our context.

Automated Exchange Economies

The canonical mechanism for financial asset exchange is the limit-order book. In decentralized blockchain ledgers (DeFi), costs and delays in appending new blocks to the ledger render a limit-order book impractical. Instead, a ``pricing curve'' is specified (e.g., the "constant product pricing function") and implemented using smart contracts deployed to the ledger. We develop a framework to study the equilibrium properties of such markets. Our framework provides new insights into how informational frictions distort liquidity provision in DeFi markets.

New News is Bad News

An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty – changes in the distribution of news text – through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors.

Admissible Surplus Dynamics and the Government Debt Puzzle

Is it possible to reconcile the procyclical Government surplus dynamics with the ‘safe asset status’ of sovereign Debt? In an arbitrage-free market, if the aggregate debt value satisfies a transversality condition that rules out ‘bubbles’, then it should equal the present value of future government surpluses. This relation seems to fail when the surplus process is calibrated to historical data in the US (Jiang, Lustig, van Nieuwerburgh, and Xiolan (2022)). However, we show that when the government issues only safe bonds in an incomplete but arbitrage-free market, then not all surplus processes are admissible in the sense that they are consistent with both the dynamic budget constraint and a transversality condition. We propose a class of admissible surplus processes that matches empirical properties of US government spending and tax claims without generating a ‘debt valuation puzzle.’

Carbon Transition Risk and Net-Zero Portfolios

Net-zero portfolios are becoming a popular vehicle to align investors’ incentives with climate scenarios. We show that the decision and timing to divest companies from NZ portfolios has a strong economic implication for their stock returns. This divestment process is captured in our new, forward-looking measure, distance-to-exit (DTE), which measures the distance in years until company gets excluded from the NZ portfolio. We show that companies with greater values of DTE have higher valuation ratios and higher expected returns consistent with the idea that DTE captures transition risk. The effect is robust to different specifications of divestment hierarchy and holds after controlling for alternative measures of transition risk, such as emission levels, their growth, and intensity. Overall, we conclude that institutional investors’ pressure is already priced in stock returns.

Value Without Employment

Young firms' contribution to aggregate employment has been underwhelming. However, we show that a similar trend is not apparent in their contribution to aggregate sales or stock-market capitalization, implying that these firms have exhibited a high ratio of average-to-marginal revenue-product-of-labor. We study the implications of a gradual shift in the average-to-marginal revenue-product-of-labor within a model of dynamic firm heterogeneity. We show that this shift provides a) a unified explanation for several facts related to the decline in ``business dynamism'', and b) a possible explanation for why large declines in young-firm employment can have only a moderate effect on aggregate output.

Do Startups Benefit from Their Investors’ Reputation? Evidence from a Randomized Field Experiment

We analyze a field experiment conducted on AngelList Talent, a large online search platform for startup jobs. In the experiment, AngelList randomly informed job seekers of whether a startup was funded by a top-tier investor and/or was funded recently.

We find that the same startup receives significantly more interest when information about top-tier investors is provided. Information about recent funding has no effect.

The effect of top-tier investors is not driven by low-quality candidates and is stronger for earlier-stage startups. The results show that venture capitalists can add value passively, simply by attaching their names to startups.

Complexity in Factor Pricing Models

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best asset pricing models (in terms of expected out-ofsample performance) have an extremely large number of factors (more than the number of training observations or base assets). Our empirical findings verify the theoretically predicted “virtue of complexity” in the cross-section of stock returns and find that the best model combines tens of thousands of factors. We also derive the feasible HansenJagannathan (HJ) bound: The maximal Sharpe ratio achievable by a feasible portfolio strategy. The infeasible HJ bound massively overstates the achievable maximal Sharpe ratio due to a complexity wedge that we characterize.

News and Networks: Using Text Analytics to Assess Bank Networks During COVID-19 Crisis

We study the "interconnectedness" of stress-tested banks by analyzing their financial news coverage. Following a text-to-network approach and using the COVID-19
pandemic as an external shock, we examine how bank networks behave during stress periods. We propose a measure of systemic risk that employs text-based eigenvector centrality, a relative measure of influence in a network. We show that this measure complements other traditional systemic risk measures, delivers more stable systemic risk rankings, and explains movements in financial variables. Our results showcase the value of soft information in the context of systemic risk.

SPACS’ DIRECTORS NETWORK: CONFLICTS OF INTEREST, COMPENSATION, AND COMPETITIONS

In 2010-2021, 972 SPACs raised $271 billion and hired 4,056 directors to facilitate mergers with private firms. We show theoretically and empirically that entrant SPACs inefficiently front-run the deal flow by hiring incumbent SPACs’ directors. Incumbent SPAC’s lower compensation and longer time to liquidation decrease directors’ compensation from the entrant SPAC but increase the chance for the conflict of interest to emerge. Empirically, higher pay by the entrant SPAC increases the chance that a director misallocates the target, hurting the returns of the incumbent SPAC’s investors. Our welfare analysis provides conditions when banning concurrent SPACs’ board membership is desirable.

The Unequal Economic Consequences of Carbon Pricing

This paper studies how carbon pricing affects emissions, economic aggregates and inequality. Exploiting institutional features of the European carbon market and high-frequency data, I identify a carbon policy shock. I find that a tighter carbon pricing regime leads to a significant increase in energy prices, a persistent fall in emissions and an uptick in green innovation. This comes at the cost of a temporary fall in economic activity, which is not borne equally across society: poorer households lower their consumption significantly while richer households are less affected. Not only are the poor more exposed because of their higher energy share, they also experience a larger fall in their income. These indirect, general-equilibrium effects turn out to be quantitatively important. My results suggest that targeted fiscal policy can reduce the economic costs of carbon pricing without compromising emission reductions.

Sentiment Inequality, Relative Performance of Firms, and the Stock Market

This study shows that sentiment inequality, defined as the consumer sentiment difference between the high and low-income groups, is indicative of the future performance of high-end product firms compared to low end product firms. High market beta firms, which tend to sell high-end products, have relatively higher cash flows following sentiment inequality increases, and low market beta firms, which tend to sell low-end products, have relatively higher cash flows following sentiment inequality decreases. These differences are not sufficiently priced in stock prices, and a trading strategy that uses knowledge on changes in sentiment inequality yields annual alphas in the range of 12%-16%, depending on whether the strategy is run unconditionally or conditional on the sentiment level in the economy. As a case study, we provide evidence of how changes in sentiment inequality play out in the relative performance of fast-food versus casual dining firms. As a final set of results, the paper shows that changes in sentiment inequality are a leading indicator of systematic changes. When sentiment inequality increases, the market value-weighted return in the following months tends to increase, while the VIX index tends to decrease.

Fundamentals of Perpetual Futures

Perpetual futures—swap contracts that never expire—are by far the most popular derivative traded in cryptocurrency markets, with more than $100 billion traded daily. Perpetuals provide investors with leveraged exposure to cryptocurrencies, which does not require rollover or direct cryptocurrency holding. To keep the gap between perpetual futures and spot prices small, long position holders periodically pay short position holders a funding rate proportional to this gap. The funding rate incentivizes trades that tend to narrow the futures-spot gap. But unlike fixed-maturity futures, perpetuals are not guaranteed to converge to the spot price of their underlying asset at any time, and familiar no-arbitrage prices for perpetuals are not available, as the contracts have no expiry date to enforce arbitrage. Here, using a weaker notion of random-maturity arbitrage, we derive no-arbitrage prices for perpetual futures in frictionless markets, and no-arbitrage bounds for markets with trading costs. These no-arbitrage prices provide a useful benchmark for perpetual futures and simultaneously prescribe a strategy to exploit divergence from these fundamental values. Empirically, we find that deviations of crypto perpetual futures from no-arbitrage prices are considerably larger than those documented in traditional currency markets. These deviations comove across cryptocurrencies, and diminish over time as crypto markets develop and become more efficient. A simple trading strategy generates large Sharpe ratios even for investors paying the highest trading costs on Binance, which is currently the largest crypto exchange by volume

The Value of Arbitrage

This paper studies the social value of closing price differentials in financial markets. We show that arbitrage gaps exactly correspond to the marginal social value of executing an arbitrage trade.

Moreover, arbitrage gaps and price impact measures are sufficient to compute the total social value from closing an arbitrage gap. Theoretically, we show that, for a given arbitrage gap, the total social value of arbitrage is higher in more liquid markets.

We compute the welfare gains from closing arbitrage gaps for covered interest parity violations and several dual-listed companies. The estimated social value of arbitrage varies substantially across applications.

The Effect of Trade Secrecy on the Design of Loan Syndicates and Contracts

I examine how proprietary information in the form of trade secrets affects lending syndicate composition and contracting. Trade secrets derive economic value from exclusivity, and as such impose information risks and exacerbate agency conflicts in debt contracting. Using the staggered adoption of the Uniform Trade Secrets Act as a source of plausibly exogenous variation in borrowers’ reliance on trade secrecy, along with a text measure to identify borrowers with trade secrets, I document that trade secrecy shapes syndicate composition by increasing the probability of relationship lending between the lead arranger and the borrower, the lead arranger and syndicate participants, and the participants and the borrower. Further, I find that the lead arrangers retain a larger share of the loan and form syndicates with fewer overall lenders and more lead arrangers in lieu of syndicate participants. Next, I show that institutional lenders are more likely to fund borrowers with trade secrets. Finally, I document that lenders relax securitization requirements and require higher loan spreads. Collectively, this study shows that trade secrecy creates information risk and agency conflicts that have a first-order effect on the design of lending syndicates.

We study dealers’ liquidity provision in the currency market. We show that at times when dealers’ intermediation capacity is constrained their cost of liquidity provision increases disproportionately relative to dealer-provided volume. As a result, the elasticity of dealers’ liquidity provision weakens by at least 80% relative to periods when they are unconstrained. We identify constrained periods based on leverage ratios, Value-at-Risk measures, credit default spreads, and debt funding costs.

We interpret our novel empirical findings within a parsimonious model that sheds light on the key mechanisms of how liquidity provision by dealers tends to weaken when intermediary constraints are tightening.

We study strategic disclosure timing by correlated firms in the presence of risk-averse investors. Risk premia rise before disclosures, drop when disclosures occur, and then begin rising again. Disclosures are always good news, but disclosures that are only moderately good news induce clustering of disclosures by other positively correlated firms, because a disclosure by any firm reduces the values to others of keeping their disclosure options alive. We present empirical evidence that firms
strategically time disclosures as predicted by the model.

This paper studies how a climate shock is transmitted through the financial system. Our empirical strategy combines data on climate and banking with El Nino, a natural experiment producing quasi-random variation in US climate. El Nino generates heterogeneous changes in lending across counties, which aggregate at the bank level. We quantify the effects of El Nino on the demand and supply of credit and implement a LASSO analysis to identify the characteristics of banks resilient to this shock. Our findings show that supply factors induce the lending reduction and banks with lower physical capital are more resilient to El Nino.

We show retail investors are highly responsive to changes in trading commission fees. Using a triple-difference research design around the removal of fees for retail investors on the international retail broker platform, eToro, we show investors responded by trading approximately 30% more frequently, in smaller order sizes, and increasing portfolio turnover. Removing fees also spurred retail investors to reallocate their portfolios and diversify. Retail investors’ gross return performance did not significantly change around the fee removal despite trading more often, but retail investors earned significantly higher returns on a net basis after accounting for fees incurred in the pre-period. Finally, using demographic information, we show removing fees disproportionately affected inexperienced investors with lower deposit amounts and lesser technological sophistication both by expanding the extensive margin of investors and changing trading activity for the intensive margin of investors. Together, our results suggest commission fees play an influential role as a speed bump for retail investor participation, trading activity, and diversification.

To confront the challenge that disaster risk is “dark matter” in finance, we construct an objective measure of disaster risk, which is able to predict half of GDP crashes in a sample of 20 advanced economies between 1870 and 2021. Despite this significant predictability, we find no supportive, and often contradictory, evidence of higher predicted disaster risk being associated with a higher equity premium, volatility, or dividend/price ratio of the equity market index; higher corporate bond spreads, or higher term spreads. Our results suggest that the subjective disaster risk mirrored by asset prices lags objective disaster risk by two years.

Active equity funds care about fund size, swayed by fund flows responding to primitive economic fluctuations. Funds hedge against common-flow shocks by tilting their portfolios toward low-flow-beta stocks, while retail investors tilt theirs toward the opposite. In equilibrium, common-flow shocks earn a risk premium, leading to a multi-factor asset-pricing model like the ICAPM, even with myopic agents using naive asset-pricing models. Empirically, fund flows obey a strong factor structure with the common component earning a risk premium, and funds actively hedge against fund flow shocks —more aggressively so when flow-hedging motives rocket following natural disasters and unexpected trade-war announcements.

We introduce a class of interpretable tree-based models (P-Trees) for analyzing (unbalanced) panel data, with iterative and global (instead of recursive and local) split criteria. We apply Ptree to split the cross section of asset returns under no arbitrage, generating a stochastic discount factor model and effective test portfolios for asset pricing. P-Trees capture nonlinear feature interactions, accommodate time-series splits, and allows interactions between macroeconomic states and asset characteristics. In an empirical study of U.S. equities, data-driven cutpoints in P-Trees reveal that long-run reversal, volume volatility, and industry-adjusted market equity interact to drive cross-sectional return variations, and that inflation constitutes the most critical regime-switching. P-Trees consistently outperform known observable and latent factor models for pricing individual asset and portfolio returns, while delivering profitable and transparent trading strategies utilizing characteristic interactions. Notably, factor portfolios from P-Trees generate a monthly risk-adjusted alpha of 2.13% and an annualized Sharpe ratio of 1.71. The methodology is broadly applicable for building trees with multi-period leaves and economic restrictions as split criteria to guard against overfitting and improve model performance.

Key Words: CART, Cross-Sectional Returns, Interpretable AI, Latent Factor, Machine Learning.

We present a novel finding that high macroeconomic uncertainty is associated with greater accumulation of physical capital, despite a contemporaneous

reduction in investment. To reconcile this evidence, we show that high un-

certainty predicts a persistent decrease in the utilization and depreciation of

existing capital, which dominates the investment slowdown. We construct and

estimate a general-equilibrium model to explain our novel findings alongside the

existing evidence on the relationship between uncertainty, economic growth,

and asset prices. In the model, precautionary saving is achieved by lowering utilization, instead of increasing investment. Lower utilization persistently

decreases depreciation, conserving capital for the future, and simultaneously

discourages new investment. This channel amplifies stock price exposure to

uncertainty risks, especially for rms with more flexible utilization, which we

confirm in the data. We further show the importance of our mechanism to generate a negative impact of uncertainty shocks in an extended New-Keynesian

framework

Governments around the world have gone on a massive fiscal expansion in response to the GFC and Covid crises, increasing government debt to levels not seen in 75 years. How will this debt be repaid? What role do conventional and unconventional monetary policy play? We investigate debt sustainability in a New Keynesian model with an intermediary sector, realistic fiscal and monetary policy, endogenous convenience yields, and substantial risk premia. During a large economic crisis, increased government spending and lower tax revenue lead to a large rise in government debt and raise the risk of future tax increases. Quantitative easing (QE) contributes to lowering the debt/GDP ratio and reducing the risk of future tax increases. QE is state- and duration-dependent: while a temporary QE policy deployed in a crisis stimulates aggregate demand, permanent QE crowds out investment and lowers long-run output.

We study the performance of collateralized loan obligations (CLOs) to understand the market imperfections giving rise to these vehicles and their corresponding economic costs. CLO equity tranches earn positive abnormal returns from the risk-adjusted price differential between leveraged loans and CLO debt tranches. Debt tranches offer higher returns than similarly rated corporate bonds, making them attractive to banks and insurers that face risk-based capital requirements. Temporal variation in equity performance highlights the resilience of CLOs to market volatility due to their closed-end structure, long-term funding, and embedded options to reinvest principal proceeds.

Deviations from a policy rule underpin empirical identification of monetary policy shocks. We cast light on how deviations arise by analyzing internal policy deliberations of the Federal Open Market Committee (FOMC). We show that policymakers’ beliefs about higher-order moments of economic distributions— specifically perceptions of uncertainty and skewness—significantly impact policy stance beyond economic forecasts typically used in rule estimates. To capture those otherwise unobservable decision-making features, we construct text-based proxies for policymakers’ uncertainty, sentiment, and policy stance from the FOMC meeting transcripts over the 1987–2015 period. Heightened uncertainty generally amplifies the policymakers’ response to the macroeconomy. However, while an increased uncertainty about the real economy drives an easier stance, inflation uncertainty leads to more hawkishness. We show that policymakers’ inflation uncertainty is associated with their skewed beliefs about rising inflation, which do not materialize in our sample. The results depart from the certainty equivalence arising in classic monetary models and contrast with the frequently-referenced conservatism in policymaking under uncertainty. Instead, the evidence suggests that policymakers act aggressively to avoid low-probability costly outcomes which are endogenous to their policy actions.

We study how firm characteristics are correlated with stock price levels by measuring the longterm discount rates (defined as the internal rate of return) of anomaly portfolios over a long horizon. Utilizing a simple novel non-parametric estimation methodology, which proxies ex-ante equity payout expectations with ex-post realizations, we reveal that the patterns of long-term discount rates are out-of-line with the average short-term holding period returns for multiple prominent anomalies. The set of stylized facts uncovered correspondingly shed new light on the mechanisms underlying various asset-pricing anomalies. Moreover, they indicate that long-term discount rates better characterize firms’ equity financing cost than short-term expected returns; with a representative example, we demonstrate how structural models that posit a tight connection between the two could imply counterfactual patterns in price levels.

We investigate whether various asset pricing models could hold in an efficient market. Assuming decade-old information should be priced correctly, we test whether a model assigns zero alpha to investment strategies that use only such information. The CAPM passes this test, but prominent multifactor models do not. Multifactor betas may help capture expected returns on mispriced stocks, but persistence in those betas distorts the stocks’ implied expected returns after prices correct. Such effects are strongest in large-cap stocks, whose multifactor betas are the most persistent. Hence, prominent multifactor models distort expected returns, absent mispricing, for the largest, most liquid stocks.

The empirical ability of stock characteristics to predict excess returns challenges the notion of a trade-off between systematic risk and expected return. We measure individual stocks’ exposures to all latent common factors using a novel high-dimensional method. These latent factors appear to earn negligible risk premia despite explaining essentially all of the common time-series variation in stock returns. We use machine learning methods to construct out- of-sample forecasts of stock returns based on a wide range of characteristics. A zero-cost beta-neutral portfolio that exploits this predictability but hedges all undiversifiable risk delivers a Sharpe ratio above one with no correlation with any systematic factor, thus rejecting the key prediction of the arbitrage pricing theory.

We propose a model of strategic debt renegotiation in which businesses are sequentially interconnected through their liabilities. This financing structure, which we refer to as a debt chain, gives rise to externalities, as a lender’s willingness to provide concessions to its privately-informed borrower depends on how the lender’s own liabilities are expected to be renegotiated. We highlight how government interventions that aim to prevent default waves should account for these private renegotiation incentives and their interlinkages. In particular, we contrast the consequences of targeted subsidies vs. debt reduction programs following economic shocks such as a pandemic or financial crisis.

We develop new nonparametric methodology for estimating conditional as- set pricing models using deep neural networks, by employing time-varying conditional information on alphas and betas carried by firm-specific characteristics. The method first applies cross-sectional deep learning, period-by-period, to estimate spontaneous conditional expected returns, defined as the conditional expectation of asset returns given characteristics and factor realizations. We additionally estimate the long-term expected return as the predicted mispricing component and the product of the estimated risk exposures times the price of risk, where local kernel smoothing is applied to capture the return dynamics that arise from time-varying alphas and betas. Contrary to many applications of neural networks in economics, we can open the “black box” and provide an economic interpretation of the successful predictions obtained from neural networks, by decomposing the neural predictors a risk based and mispricing component. We formally establish the asymptotic theory of the deep-learning estimators, which apply to both in-sample fit and out-of-sample predictions. Empirically, we find a large, time varying mispricing component, and that the mispricing component is slowly decaying over time, but not monotonically. Mis- pricing tends to be high during times of high market volatility which is linked to periods of economic turmoil. Finally, we also illustrate the “double-descent- risk” phenomena associated with over- parametrized predictions, which justifies the use of over-fitting machine learning methods.

Big data technologies change the way in which data and human labor combine to create knowledge. Is this a modest technological advance or a data revolution? Using hiring and wage data from the investment management sector, we estimate firms' data stocks and the shape of their knowledge production functions. Knowing how much production functions have changed informs us about the likely long-run changes in output, in factor shares, and in the distribution of income, due to the new, big data technologies. Using data from the investment management industry, our results suggest that the labor share of income in knowledge work may fall from 29% to 21%. The change associated with big data technologies is two-thirds of the magnitude of the change brought on by the industrial revolution.

Adverse selection is known in theory to lead to inefficiently low credit provision, yet empirical estimates of the resulting welfare losses are scarce. This paper leverages a randomized experiment conducted by a large fintech lender to estimate welfare losses arising from selection in the market for online consumer credit. Building on methods from the insurance literature, we show how exogenous variation in interest rates can be used to estimate borrower demand and lender cost curves and recover implied welfare losses. While adverse selection leads to large equilibrium price distortions, we find only small overall welfare losses, particularly for high-credit-score borrowers.

This paper investigates how interbank credit exposures affect financial stability. Policy makers often see such exposures as undermining stability by exacerbating cascading losses through the financial system. I develop a model that features a trade-off between cascading losses and risk-sharing. In contrast to previous studies I find that reducing interbank connectivity may destabilize the financial system via the bank-run channel. This is because it decreases the risk-sharing benefits of interbank connectivity. A bank-run model features two islands that are connected via a long term debt claim. Varying the size of this claim (interbank connectivity), I study how the decision to `run on the bank' is affected. I run a simulation of the model, calibrated to the U.S. banking system between 1997-2007. I find that large bankruptcy costs are required to trump the risk-sharing benefits of interbank credit exposures

We introduce a new text-mining methodology that extracts information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of terms via predictive screening, 2) assigning prediction weights to these words via topic modeling, and 3) aggregating terms into an article level predictive score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we study one of the most actively monitored streams of news articles in the financial system - the Dow Jones Newswires - and show that our supervised text model excels at extracting return-predictive signals in this context. Information in newswires is assimilated into prices with an inefficient delay that is broadly consistent with limits-to-arbitrage (i.e., more severe for smaller and more volatile firms) yet can be exploited in a real-time trading strategy with reasonable turnover and net of transaction costs.

We study how plan sponsors choose investment management firms from their opportunity set when delegating $1.6 trillion in assets between 2002 and 2017. Two factors play an influential role in choice: pre-hiring returns, and pre-existing personal connections between personnel at the plan (or consultant advising the plan), and the investment management firm. Post-hiring returns for chosen firms are significantly lower than those for unchosen firms. The post-hiring returns of firms with relationships are, at best, indistinguishable from those without relationships, and often significantly worse. While relationships are conducive to asset gathering by investment managers, they do not appear to generate commensurate benefits for plan sponsors via higher gross returns or lower fees.

We find that common ownership leads VCs to stifle competition among startups, but only in limited circumstances. Our evidence is from pharmaceutical startups, where common ownership is widespread. We examine how a startup responds after seeing a competitor make progress on a related drug project. If the two startups share a common VC, the lagging startup is less likely to advance its own project and obtain VC funding, which reduces competition between the startups. These anticompetitive effects, however, are limited to concentrated product markets, technologically similar projects, early-stage projects, and VCs with larger equity stakes and less-diversified portfolios.

We show that incompleteness in health insurance has a substantial effect on mortality among cancer patients, and we identify the channels through which this effect occurs. As theory predicts, high-wealth patients draw on that wealth (using credit markets) to fund out-of-pocket costs, while low-wealth patients rely on the implicit insurance provided by bankruptcy and other legal institutions to bear out-of-pocket costs. However, wealth matters for mortality: After instrumenting home equity wealth using house price variation, we find that high-wealth patients are substantially more likely to perform treatment, which prolongs survival. Thus, the implicit insurance provided by bankruptcy and other laws is insufficient to induce life-saving health choices. Finally, and surprisingly, we find that modest-wealth households, whose assets would be completely protected in bankruptcy, nonetheless exhaust their wealth in order to cover out-of-pocket costs. This finding suggests important limits to the role of bankruptcy as a form of implicit insurance.

We show that inWe study the effect of hedge fund activism on corporate environmental behaviors. Using plant-chemical level data from the EPA, we find that activism campaigns are associated with a 17 percent drop in emissions for chemicals at plants of targeted firms. Campaigns are associated with changes across a wide range of chemicals, including those emitted into the air, water, and ground and those that are harmful to humans. Evidence suggests this change in environmental behavior stems from a drop in production rather than an increase in abatement activities. The net effect on environmental efficiency is positive, with emissions falling by 8 percent per unit of output. Overall, our findings highlight the idea that the benefits of activism are not necessarily confided to shareholders, but may also extend to other stakeholders (e.g., the local community) affected by firms' emissions.

"Factors in prominent asset pricing models are positively autocorrelated. We derive a transformation that turns an autocorrelated factor to a \time-series efficient"" factor. Time-series efficient factors earn significantly higher Sharpe ratios than the original factors and contain all the information found in the original factors. Momentum strategies profit from the same predictable variation in factor premiums as time-series efficient factors. An asset pricing model with time-series efficient factors, such as an efficient Fama-French five-factor model, therefore prices momentum. "

Finance seminar

Finance seminar

Finance seminar

Discounts and Shoplifting in Self-Checkout Shopping - Finance seminar

We examine the effect of lender credit information sharing on first-time borrowers’ loan take-up and default decisions using a pair of natural field experiments. Upon receiving credit warnings after taking out a loan, borrowers’ default likelihood decreases by 6%, suggesting an improvement in repayment effort. Upon receiving the same information after loan approval but before take-up, borrowers are 3% more likely to take out the loan, suggesting that credit reporting allows them to establish a credit history. Default likelihood is comparable between the two experiments, implying that credit reporting has little effect on borrowers’ adverse selection.

The talk will briefly highlight ongoing research focused on assessing aggregate disadoption, the impact of category layouts, and fund allocation by individual investors. It will then focus on a method which combines theory (intuition) and data (the empirical relations among variables) to "automatically" build a model linking the variables.

This paper investigates the effect of market power on dynamic pricing in the presence of inventories. Specifically, we analyze how automotive dealerships adjust prices to inventory levels under varying degrees of market power. First, we show that inventory fluctuations create scarcity rents for cars that are in short supply. Our empirical results show that a dealership moving from a situation of inventory shortage to a median inventory level lowers transaction prices by about 0.6% ceteris paribus, corresponding to 37% of dealers' average per vehicle profit margin or $165 on the average car. Then, we show that dealer's ability to adjust prices in response to inventory depends on their market power, i.e., the quantity of substitute inventory in their selling area. Specifically, we show that the slope of the price-inventory relationship (higher inventory lowers prices) is significantly steeper when dealers find themselves in a situation of high rather than low market power.

We analyze the effect of media coverage on corporate voluntary disclosure. Specifically, we examine a model where a journalist, who covers a firm, reports noisy information which may be disclosed voluntarily also by the firm's manager. We show that, in equilibrium, media coverage crowds out the manager's voluntary disclosure, the manager delegates to the journalist some information provision, and investors are more confident with the journalist's reports about low values. Next, we analyze a setting of a sequential provision of information, where we examine the manager's response to news reports. We show that the manager may withhold information that is better than was reported by the journalist but respond to the news report with a disclosure of information that is less favorable.

We incorporate structural modellers into the economy they model. Using the traditional moment-matching method, they ignore policy feedback and estimate parameters using a structural model that treats policy changes as zero probability (or exogenous) "counterfactuals." Estimation bias occurs since the economy.s actual agents, in contrast to model agents, understand policy changes are positive probability endogenous events guided by the modellers. We characterize equilibrium bias. Depending on technologies, downward, upward, or sign bias occurs. Potential bias magnitudes are illustrated by calibrating the Leland (1994) model to the Tax Cuts and Jobs Act of 2017. Regarding parameter identi.cation, we show the traditional structural identifying assumption, constant moment partial derivative sign, is incorrect for economies with endogenous policy optimization: The correct identifying assumption is constant moment total derivative sign accounting for estimation-policy feedback. Under this assumption, model agent expectations can be updated iteratively until the modellers' policy advice We analyze the effect of media coverage on corporate voluntary disclosure. Specifically, we examine a model where a journalist, who covers a firm, reports noisy information which may be disclosed voluntarily also by the firm's manager. We show that, in equilibrium, media coverage crowds out the manager's voluntary disclosure, the manager delegates to the journalist some information provision, and investors are more confident with the journalist's reports about low values. Next, we analyze a setting of a sequential provision of information, where we examine the manager's response to news reports. We show that the manager may withhold information that is better than was reported by the journalist but respond to the news report with a disclosure of information that is less favorable.

The Role of Fair-Value Adjustments - Finance Seminar

Data Breach Disclosure and Insider Trading - Finance Seminar

Insurers as Asset Managers and Systemic Risk - Finance Seminar

The effect of stock liquidity on the firm’s investment and production

The Economics of Factor Timing - Finance Seminar

Blockholder Heterogeneity, Multiple Blocks, and the Dance Between Blockholders

Distorted advice in the mortgage market - Finance Seminar

Options on US equities typically expire on the third Friday of each month, implying that either four or five weeks elapse between two consecutive expiration dates. We find that options that are held from one expiration date to the next achieve significantly lower returns when there are four weeks between expiration dates. The average difference in returns ranges from 12 basis points per week for delta-hedged put portfolios to 89 basis points for straddles (6.5% and 58.5% annualized, respectively). We find consistent results from an alternative price-based measure of mispricing. We perform multiple tests to examine the risk of our option portfolios and do not find any underlying risk patterns that can potentially explain our results. We therefore argue that the mispricing we identify is due to investor inattention to the exact expiration date, and provide further supporting evidence based on earnings announcements and price patterns closer to maturity. Our results survive a series of robustness tests and are unlikely to be driven by transaction costs. Overall, our evidence points to a potential strong behavioral bias among option traders.

Text data is inherently high-dimensional, which makes machine learning regularization techniques natural tools for its analysis. Text is often selected by journalists, speechwriters, and others who cater to an audience with limited attention. We develop an economically-motivated high dimensional selection model that can improve machine learning from text in particular and from sparse counts data more generally. Our highly scalable approach to modeling coverage selection is especially useful in cases where the cover/no-cover choice is separate or more interesting than the coverage quantity choice. We apply this framework to option-implied volatility (VIX) prediction using newspaper coverage, and find that it substantially improves out-of-sample fit relative to alternative state-of-the-art approaches. This advantage increases with the sparsity of the text.

We use the IPO setting to provide evidence that accounting measures of valuation uncertainty combine with short-sales constraints to generate significant equity market mispricing. The IPOs that we predict to be most susceptible to overpricing in the immediate aftermarket have first-day returns of +47% and lockup expiration returns of −9%. Our detailed analysis of securities lending market data confirms that these IPOs experience severe short-sales constraints that peak around the lockup expiration. Our paper both explains the anomalous pricing of IPOs and highlights the importance of valuation uncertainty and short-sales constraints in explaining equity mispricing.

This paper studies mutual fund voting in proxy contests using a comprehensive sample of voting records over the period 2008 – 2015, taking into account selective targeting by activists. We find that firm, fund, and event characteristics generate substantial heterogeneity among investors in their support for the dissident, including their reliance on proxy advisors. Notably, active funds are significantly more pro-dissident than passive funds, and we uncover evidence consistent with a large unobserved fund "inherent stance" that cannot be explained by observable fund or event characteristics. In particular, we document a positive correlation between the propensity for targeting by activists and pro-activist voting by mutual funds, both based on the observables and unobservables. This finding suggests that a relatively pro-activist shareholder base is a key factor driving activists' selection of targets.

We study the causal effect of trading on stock market liquidity. We exploit episodes of sensational news (exogenous to the market) that distract retail investors. On “distraction days” we find that trading activity, liquidity, and volatility all decline among stocks owned predominantly by retail investors. These findings, complemented by additional tests, establish that retail investors contribute to liquidity by serving both as noise traders and as liquidity providers. They also identify adverse selection as an important driver of illiquidity, thereby countervailing recent work that assigns a leading role to inventory risk or questions the usefulness of adverse selection measures.

This paper examines the effect of disclosure harmonization on the market for corporate control. For identification, we exploit a major regulatory change; the implementation of the Transparency Directive of 2004 (TPD), a legislation aimed at harmonizing information requirements for security issuers across the European Union (EU). We find that the TPD is followed by a substantial decrease in the number of control acquisitions. This pattern is concentrated in countries with higher pre-levels of takeover activity and lower pre-levels of acquisition costs. Consistent with the decrease of takeover activity being related to an increase in acquisition costs, we find that, under the TPD, targets’ takeover premiums increase and acquirers’ stock returns around the announcement decrease. Finally, additional tests suggest that these patterns are likely driven by mandatory disclosure of shareholders’ holdings (rather than by provisions related to issuers’ financial reporting). Overall, our evidence suggests that the disclosure harmonization introduced by the TDP decreased differences in takeover activity across EU countries. However, rather than stimulating less active takeover markets, the directive could have slowed down more dynamic markets; an effect likely driven by the higher acquisition costs associated with tighter ownership disclosure rules.