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https://ssrn.com/abstract=3202916; We show that banker bonuses cannot be understood exclusively as incentive contracts, but also incorporate a significant risk sharing dimension between bank shareholders and bank employees. This contrasts with the conventional view whereby diversified shareholders fully insure risk averse employees. However, financial frictions imply that shareholder value is concave in a bank's cash reserves---making shareholders effectively risk averse. The optimal contract between shareholders and employees then involves some degree of risk sharing. Using extensive payroll data on 1.26 million bank employee years in the Austrian, German, and Swiss banking sectors, we show that the structure of bonus pay within and across banks is compatible with an economically significant risk sharing motive, but difficult to rationalize based on incentive theories of bonus pay only.

Organizational adaptation is a ubiquitous concept in management and organization research. Discussed in many theories, sometimes under different labels, the notion of adaptation is related to organizations being congruent to the environments within which they operate. Research traditions including behavioral theory (see A Behavioral Theory of the Firm), contingency theory as explained in Differentiation and Integration in Complex Organizations, population ecology (see The Population Ecology of Organizations), institutional theory elaborated upon in both Institutionalized Organizations: Formal Structure as Myth and Ceremony and The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields, resource dependence (see The External Control of Organizations: A Resource Dependence Perspective), and evolutionary economics (see An Evolutionary Theory of Economic Change) all address organizational adaptation—though they emphasize different mechanisms. Adaptation, regarded as the strategic choice that organizations make, is often contrasted with the notion of selection driven by the determinism imposed by environments (see Organizational Adaptation: Strategic Choice and Environmental Determinism). Adaptive organizations are those able to obtain congruence both within organizations, reflected as congruence in internal functions and strategies, and across organizations, reflected as congruence with the needs of the external environment. The form of adaptation is also particularly relevant. Organizational theory has viewed adaptation as a state, an ability, and as a process. States of adaptation discuss stability and adaptation at points in time. From states of adaptation, adaptive abilities determine the impetus of adaptation and the locus of adaptation within organizations. Finally, the adaptation processes describe the means by which organizations adapt the challenges they face in negotiating new positions within environments. Research has covered each of these areas and this bibliography is organized primarily around forms of adaptation to elaborate on the ways in which it has been viewed by organizational theory and strategy scholars. The variety of research traditions examining organizational adaptation have not always aligned, however. Each research tradition seeks to explain different outcomes, from survival to performance to change. Thus, across theoretical traditions, the adaptation construct has been measured in many different ways—and often not as congruence with the external environment. In fact, this represents one of the challenges that recent research has identified as holding back progression of the adaptation research agenda. As such, this bibliography concludes with a section on issues in adaptation research in order to assist researchers in pursuing research in this area.

We measure the impact of bank capital requirements on corporate borrowing and expansion. We use French loan-level data and take advantage of the transition from Basel I to Basel II. While under Basel I the capital charge was the same for all firms, under Basel II, it depends in a predictable way on both the bank's model and the firm's risk. We exploit this two-way variation to empirically estimate the sensitivity of bank lending to capital requirement. This rich identification allows us to control for firm-level credit demand shocks and bank-level credit supply shocks. We find very large effects of capital requirements on bank lending: A 1 percentage point decrease in capital requirement leads to an increase in loan size by about 5%. At the firm level, borrowing also responds strongly although a bit less, consistent with some limited between-bank substitutability. Investment and employment also increase strongly. Overall, because the transition to Basel II led to an average reduction by 2 percentage points of capital requirements, we estimate that the new regulation led, in France, to an increase in average loan size by 10%, an increase in aggregate corporate lending by 1.5%, an increase in aggregate investment by 0.5%, and the creation or preservation of 235,000 jobs.

Despite the increasing importance of multiple priors in various domains of economics and the significant theoretical advances concerning them, choice-based incentive-compatible multiple-prior elicitation largely remains an open problem. This paper develops a solution, comprising a preference-based identification of a subject’s probability interval for an event, and two procedures for eliciting it. The method does not rely on specific assumptions about subjects’ ambiguity attitudes or probabilistic sophistication. To demonstrate its feasibility, we implement it in two incentivized experiments to elicit the multiple-prior equivalent of subjects’ cumulative distribution functions over continuous-valued sources of uncertainty. We find a predominance of non-degenerate probability intervals among subjects for all explored sources, with intervals being wider for less familiar sources. Finally, we use our method to undertake the first elicitation of the mixture coefficient in the Hurwicz α-maxmin EU model that fully controls for beliefs.

Newcomers are important for the survival of online communities, but their contributions often receive negative reactions and comments from established members. Online communities realize that such negativity can take a toll on newcomers and harm the creation of user-generated content. We study a novel intervention aimed at reducing hostility toward newcomers: a “newcomer nudge” that informs community members when they are interacting with a newcomer’s post and asks them to be more lenient toward its creator. Taking advantage of granular data from a large deal-sharing community and a natural experiment, we use a difference-in-differences approach and find robust evidence that the newcomer nudge induced members to write 46% more responses per day with 10% fewer negative words during the first two days after a deal was published. Our results show that the nudge-induced change in behavior toward newcomers increased newcomer retention. However, we also observe that before the nudge, newcomers’ second posts received more net votes (upvotes minus downvotes) than their first posts. After the nudge, newcomers’ subsequent posts were less popular than their first posts, which indicates that the nudge interrupted newcomers’ learning curve by suppressing helpful feedback.

Political activism positively affects firm innovation. Firms that support more politicians, politicians on Congressional committees with jurisdictional authority over the firms’ industries and politicians who join those committees innovate more. We employ instrumental variables estimation and a natural experiment to show a causal effect of political activism on innovation. The results are consistent with the hypothesis that political activism is valuable because it helps reduce political uncertainty, which, in turn, fosters firm innovation. Also consistent with this hypothesis, we show that politically active firms successfully time future legislation and set their innovation strategies in expectation of future legislative changes.Keywords: political contributions, innovation, investment policy, policy uncertainty

In credit markets, screening algorithms discriminate between good-type and bad-type borrowers. This is their raison d’être. However, by doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, race) and the rest of the population. In this paper, we show how to test (1) whether there exists a statistical significant difference in terms of rejection rates or interest rates, called lack of fairness, between protected and unprotected groups and (2) whether this difference is only due to credit worthiness. When condition (2) is not met, the screening algorithm does not comply with the fair-lending principle and can be qualified as illegal. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, and improved for the benefit of protected groups.

Real and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and noncorporate business equity—compose an investment class worth an estimated $84 trillion in the U.S. alone. Furthermore, private values can affect pricing in many other financial markets, such as that for sustainable investments. This paper introduces the research on real assets and private values that can be found in this special issue. It also reviews recent advances and highlights new research directions on a number of topics in the real assets space that we believe to be particularly important and exciting.

International audience; In this article, we tackle the problem of a market maker in charge of a book of options on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable. More precisely, when volatility is modeled using a classical stochastic volatility model—e.g. the Heston model—the problem faced by an option market maker is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios. In order to illustrate our findings, numerical examples are provided.

In over-the-counter markets for assets, such as bonds and securitizations, large volumes can be split into smaller pieces and gradually sold to several final investors with the intermediation of multiple dealers. This paper proposes a model to study this process, called asset dissemination. A dealer buys several units of an asset from a customer and then sells some units to the dealer’s customers and to a second dealer, who sells to the second dealer’s customers and to a third dealer, and so on. The extent of dissemination is measured by the number of dealers involved and the total customer demand served. We show that asymmetric information on customer demand hinders both dimensions of dissemination. We also study how the quantity to disseminate and the dealers’ funding costs impact dissemination and the prices and quantities in interdealer transactions. This paper was accepted by Gustavo Manso, finance.

Assortment selection is one of the most important decisions faced by retailers. Most existing papers in the literature assume that customers select at most one item out of the offered assortment. While this is valid in some cases, it contradicts practical observations in many shopping experiences, both in online and brick-and-mortar retail, where customers may buy a basket of products instead of a single item. In this paper we incorporate customers' multi-item purchase behavior into the assortment optimization problem. We consider both uncapacitated and capacitated assortment problems under the so-called Multivariate MNL (MVMNL) model, which is one of the most popular multivariate choice models used in the marketing and empirical literature. We first show that the traditional revenue-ordered assortment may not be optimal. Nonetheless, we show that under some mild conditions, a certain variant of this property holds (in the uncapacitated assortment problem) under the MVMNL model---that is, the optimal assortment consists of revenue-ordered local assortments in each group. Finding the optimal assortment is still computationally expensive as the revenue thresholds for different groups cannot be computed separately. We show that the optimization problem under MVMNL is NP-complete even in the setting where there is no interaction among the product categories. Motivated by this result, we develop FPTAS for several variants of (capacitated and uncapacitated) assortment problems under MVMNL. Our analysis reveals that disregarding customers' multi-item purchase behavior in assortment decisions can indeed have a significant negative impact on a retailer’s profitability, demonstrating its practical importance in retail. In particular, we show that our proposed algorithm can improve a retailer's expected total revenues (compared to some benchmark policies that do not properly take into account the impact of customer's multi-item choice behavior in assortment decisions) by around 5-7% for the uncapacitated problems, and around 10-54% for the capacitated problems, both of which are quite significant.

International audience; The paper proposes a novel point-wise entropy approach to measure the time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. We cast our approach in a Bayesian framework and assume that market agents update their beliefs to incoming signals based on a prior information set. By exploiting the distribution rather than the time-series properties of information signals, our method is able to construct univariate signal-specific, but also composite proxies of information loss, with the latter being more efficient in reducing misleading effects and interpretation errors. As an empirical illustration, we construct information loss proxies for the US equity market from several mainstream information signals and find that the majority of information loss indicators can influence investors’ attention, which then intermediates the impact of information signals on market outcomes. Finally, we show that, by relying on composites rather than univariate proxies, market agents can diversify and thus reduce their information losses when interpreting signals associated with the same underlying event.

The European response to COVID-19 has revealed an inconvenient truth. Despite having integrated public health concerns across all its policies – be it agriculture, consumer protection, or security –, the Union cannot directly act to save people’s lives. Only member states can do so. Yet when they adopted unilateral measures to counter the spread of the virus, those proved not only ineffective but also disruptive on vital supply chains, by ultimately preventing the flow of essential goods and people across the Union. These fragmented efforts in tackling cross-border health threats have almost immediately prompted political calls for the urgent creation of a European Health Union. Yet this call raises more questions than answers. With the aim to offer a rigorous and timely blueprint to decision-makers and the public at large, this Special Issue of the European Journal of Risk Regulation contextualizes such a new political project within the broader constitutional and institutional framework of EU public health law and policy. By introducing the Special, this paper argues that unless the envisaged Health Union will tackle the root causes of what prevented the Union from effectively responding to COVID-19 – the divergent health capacity across the Union –, it might fall short of its declared objective of strengthening the EU’resilience for cross-border health threats.

We study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts produce forecasts of short-term and long-term earnings and choose how much information to collect about each horizon to minimize their expected forecasting error, net of information acquisition costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information collection strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. Using a large sample of analysts' forecasts at various horizons and novel measures of their exposure to abundant data (e.g., social media data), we provide empirical support for this prediction, which implies that data abundance can impair the quality of long-term forecasts.

Under International Financial Reporting Standards, managers can use two approaches to increase the estimated fair value of goodwill in order to justify not recognizing impairment: (1) make overly optimistic valuation assumptions, and (2) increase future cash flow forecasts by inflating current cash flows. Because enforcement constrains the use of optimistic valuation assumptions, we hypothesize that enforcement influences the relative use of these two choices. We test this hypothesis by comparing a sample of 1,958 firms from 36 countries that are likely to delay recognizing goodwill impairment (suspect firms) to a sample of control firms. First, we find that firms in high‐enforcement countries use a higher discount rate to test goodwill for impairment than firms in low‐enforcement countries. We also find a more positive association between discount rate and upward cash flow management for suspect firms than for control firms. This result is consistent with suspect firms substituting optimistic valuation assumptions with inflated current cash flows. Second, we find that, relative to control firms, suspect firms exhibit higher upward cash flow management in high‐enforcement countries than in low‐enforcement countries. Third, we show that suspect firms in high‐enforcement countries are more likely to eventually impair goodwill.

How do banks remunerate risk managers and what are the implications for risk-taking? Studying 127 German banks during the years 2003 to 2007, we show that risk managers' remuneration is positively aligned with performance-linked pay in front offices (FOs). When bonuses in FOs increase by one Euro, the bonus of a risk manager increases by 13.6 to 33.5 Cents, depending on the risk manager’s seniority. Risk-sharing among employees or labor market competition do not explain this finding. Banks with more aligned incentive pay between risk management and FOs during the years before the crisis of 2008-2009 performed better in the crisis.

We consider a firm that faces the following pricing and leadtime disclosure decision. Either, (1) offer the same price to all arriving customers and disclose no leadtime information, in which case customers know only an expected leadtime, or, (2) vary the price dynamically based on the current inventory status, and disclose leadtime information dynamically. The second option allows the firm to offer a price discount for a long leadtime, and to offer a short leadtime when it has a large inventory. In order to determine which pricing and leadtime disclosure policy attains higher maximum profit, we formulate and solve a Markov decision problem for each policy and compare the two solutions. The comparison suggests that, typically, there is a critical inventory level, above which static pricing and no leadtime disclosure yields higher profit, and, below that level, dynamic pricing and leadtime disclosure yields a higher profit. We also identify certain situations where the static policy leads to profits at least as large as those under the dynamic policy, for any state, and we examine the price differences between the two policies. Finally, the structure of the solution is similar to the situation where the system manager can pause the production process, except that now this extra lever partly mitigates the superiority of the static policy.

Brands and advertisers often tap into the crowd to generate ideas for new products and ad creatives by hosting ideation contests. Content evaluators then winnow thousands of submitted ideas before a separate stakeholder, such as a manager or client, decides on a small subset to pursue. We demonstrate the information value of data generated by content evaluators in past contests and propose a proof-of-concept machine learning approach to efficiently surface the best submissions in new contests with less human effort. The approach combines ratings by different evaluators based on their correlation with the past stakeholder choices, controlling for submission characteristics and textual content features. Using field data from a crowdsourcing platform, we demonstrate that the approach improves performance by identifying nonlinear transformations and efficiently reweighting evaluator ratings. Implementing the proposed approach can affect the optimal assignment of internal experts to ideation contests. Two evaluators whose votes were a priori equally correlated with sponsor choices may provide substantially different incremental information to improve the model-based idea ranking. We provide additional support for our findings using simulations based on a product design survey.

We analyze firm-level analyst forecasts during the COVID crisis. First, we describe expectations dynamics about future corporate earnings. Downward revisions have been sharp, mostly focused on 2020, 2021 and 2022, but much less drastic than the lower bound estimated by Gormsen and Koijen (2020). Analyst forecasts do not exhibit evidence of over-reaction: As of mid-May, forecasts over 2020 earnings have progressively been reduced by 16%. Longer-run forecasts, as well as expected “Long-Term Growth” have reacted much less than short-run forecasts, and feature less disagreement. Second, we ask how much discount rate changes explain market dynamics, in an exercise similar to Shiller (1981). Given forecast revisions and price movements, we estimate an implicit discount rate going from 10% in mid-February, to 13% at the end of March, back down to their initial level in mid-May. We then decompose discount rate changes into three factors: changes in unlevered asset risk premium (0%), increased leverage (+1%) and interest rate reduction (-1%). Overall, analyst forecast revisions explain most of the decrease in equity values between January 2020 and mid May 2020, but they do not explain shorter term stock market movements.

We measure how shareholders value a firm's ethical actions via an experiment. Our findings are threefold. First, the "selfish investor hypothesis'' is strongly rejected. Participants are willing to pay $ .7 more for buying a share in a firm giving one more dollar per share to charities. Symmetrically, a firm that makes profits by exercising a negative externality of $1 on a charity is valued $.9 less than a similar company with no externality. The scaling of non-pecuniary preferences is linear: doubling the size of a social externality doubles its impact on willingness to pay. Second, the data show that whether investors are pivotal or not with regard to the ethical actions of the firm does not affect their willingness to pay. Third, when participants make investment decisions on behalf of a third party (delegation), their generosity level remains similar. Our results appear to be compatible with a utility model where non-pecuniary benefits are conditional on stock holding.

We estimate the causal effect of state-mandated business closures on economic and health outcomes in the context of the COVID-19 crisis in the US. We first show that business closures lead to a substantial drop in sales, earnings, and market values for affected firms. We then exploit sectoral variations in exposure to these restrictions across areas within the same state, and show that locking down 10% of the labor force is associated with a significant contraction in employment, but allows to reduce COVID-19 weekly infection and death rates by respectively 0.023 and 0.0015 percentage points. The findings translate into 24,000 saved lives for a cost of $115 billion. Finally, our empirical analysis suggests that the cost per life saved associated to business closures could have been significantly reduced if restrictions had targeted areas with intense workplace interactions

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.

Research Summary In this study, we replicate and expand Hawn et al.’s study (Strategic Management Journal, 2018, 39, 949–976) that used Dow Jones Sustainability World Index (DJSI) events to measure variations in firms' Corporate Social Responsibility (CSR)‐activism and examined their effect on a firm's stock price. We use DJSI events to capture variations in firms' CSR visibility, holding CSR activism constant by restricting our analyses to CSR‐equivalent firms. First, we find similar results on stock price (i.e., no impact) and on trading volumes. Second, because professional market participants pay more attention to CSR‐oriented firms and use visible cues such as DJSI events, we study and find that additions to DJSI lead to more analysts following a firm, and that continuations on the DJSI lead to an increase in equity being held by long‐term investors. Managerial Summary We replicate and complement a recent study that showed that the events of a firm's addition, continuation, or deletion in a major sustainability index (DJSI) had little impact on stock market reactions (Hawn et al. Strategic Management Journal, 2018, 39, 949–976). We redefine the comparison set that was previously used and compare across observationally equivalent firms in terms of CSR orientation in order to isolate the visibility effect of DJSI events. We find that these events do not significantly impact stock price and trading volumes. However, we expand the analysis and find that sustainability events attract more attention from financial analysts and lead to an increase in the percentage of shares held by long‐term investors indicative of a trend that professional investors pay more attention to CSR‐visible firms over time.

We examine how the U.S. Food and Drug Administration Amendments Act (FDAAA) of 2007, which requires additional disclosures regarding clinical trial results, impacts information asymmetry between the disclosing pharmaceutical firm and capital market participants, the general public, academics, and practitioners. We document a reduction in information asymmetry in capital markets. We also document an increase in adverse event and product problem complaint reports filed against the pharmaceutical firms to the FDA and a higher number of drug and medical device recalls for affected firms after the FDAAA enactment. Finally, cross-sectional analyses suggest that the increase in FDA complaint reports and recalls after the FDAAA enactment was more prominent in firms with a higher bid-ask spread decrease. Taken together, our results suggest that the FDAAA has some benefits for both investors and consumers.

Research Summary We explore the performance consequences of the simultaneous pursuit of multiple objectives in organizations. Taking advantage of a unique dataset covering both the objectives pursued and performance outcomes, we test the hypothesis that is the cornerstone of multiple objectives theory: performance on a given metric increases when it is pursued as an objective but decreases with the number of other objectives pursued simultaneously. We find overall support for this hypothesis, which holds for most, but not all, objectives. We further unpack the link between multiplicity of objectives and performance, investigating the moderating effects of organization design choices. This study suggests that multiple objectives impose a cost on organizations, but also provide a benefit of alleviating tradeoffs in achieving higher performance in multiple dimensions. Managerial Summary Most organizations simultaneously follow multiple goals, rather than focus on a single, well‐defined objective. For example, manufacturing firms often concurrently strive to decrease costs, increase revenues, and enhance margins. We study the consequences of such pursuit for firm performance. We show that explicitly setting objectives plays an important role in driving performance improvements. We also show that performance on any given dimension decreases with the number of other, simultaneously, followed goals. This regularity holds across different types of organizations, from simple to complex. Finally, we show that setting goals in multiple dimensions can play a beneficial role in forcing firms to actively manage tradeoffs inherent in their strategic choices. Our findings point to how managers could balance the costs and benefits of multiple objectives.

We document that since 1997, the rate of startup formation has precipitously declined for firms operated by U.S. PhD recipients in science and engineering. These are supposedly the source of some of our best new technological and business opportunities. We link this to an increasing burden of knowledge by documenting a long-term earnings decline by founders, especially less experienced founders, greater work complexity in R&D, and more administrative work. The results suggest that established firms are better positioned to cope with the increasing burden of knowledge, in particular through the design of knowledge hierarchies, explaining why new firm entry has declined for high-tech, high-opportunity startups.

In the face of rising income inequality (Acemoglu & Autor, 2011; Atkinson, Piketty, & Saez, 2011; Piketty, 2014; World Economic Forum, 2014), one recent proposal is to provide consumers with information about the income inequality across those involved in the production of each good, at the point of purchase. This has been shown to depress overall inequality (Hill, 2020), though its impact depends crucially on whether people are willing to pay more for goods whose production involves less income inequality. Here we investigate this largely unexplored empirical question through an incentivised, behavioural choice experiment on a representative sample of the English population. We find that a large majority are willing to pay significantly more for goods associated with less inequality. How much more people are willing to pay varies with political leaning and increases with the extent of the inequality reduction, but is positive across the political spectrum and for all studied inequality differences. Moreover, it is typically higher when inequality is reported in more intuitive and informa- tive formats. Our results bode well for the effectiveness of product-level inequality information provision as a tool for moderating income inequality, promising impacts even in markets where all goods involve relatively high inequality levels and potential participation across the political spectrum.

We investigate how Activist Short Sellers (AShSs) expose publicly listed firms in an increasingly popular form of “research reports” openly denouncing alleged frauds, flawed business models, accounting irregularities, and wrongdoings. We focus on six AShSs that issued research reports that often led to a strong negative market reaction. Our empirical analysis exploits both qualitative and quantitative methods for a comprehensive dataset of 383 research reports targeting 171 unique firms, and three first-hand interviews with AShSs. Drawing on Aristotle’s rhetoric, we first examine how AShSs use narratives in striving to convince other investors that the target firms are overvalued. Specifically, we search the documents produced by AShSs for stylized narratives related to credibility-based (ethos), emotions-based (pathos), and logic-based (logos) rhetorical strategies. To assess the impact of these strategies, we examine the extent to which the AShSs’ rhetorical strategies resonate in 3,665 press articles. As expected, the press often refers to logos-based arguments. Interestingly, the press also brings up frequently pathos-based and ethos-based statements. Considering the importance of the press in shaping investors’ opinions, our study points to AShSs’ narratives playing a major role in policing financial markets. Theoretically, we show that AShSs, as dissenting market participants, produce narratives that go beyond the language of formal rationality – as they strive to reveal new information and frame it persuasively, in order to destabilize the extent of trustworthiness surrounding target firms.

This paper explores the effects of online customer ratings on debt capacity. Using a large sample of Parisian restaurants, we find a positive and economically significant relation between customer ratings and bank debt. We use the locally exogenous variation in customer ratings resulting from the rounding of scores in regression discontinuity tests to establish causality. Customer ratings affect financial policy through a reduction in cash flow risk and higher resilience to demand shocks. Restaurants with good ratings use their extra debt capacity to invest in tangible assets. Finally, favorable online ratings relax credit constraints mostly for moderately constrained restaurants.

Consistent with salience theories of choice, we find that managers overreact to salient risks. We study how managers respond to the occurrence of a hurricane event when their firms are located in the neighborhood of the disaster area. We find that the sudden shock to the perceived liquidity risk leads managers to increase the amount of corporate cash holdings, even though the real liquidity risk remains unchanged. Such an increase in cash holdings is only temporary. Over time, the perceived risk decreases, and the bias disappears. This bias is costly for shareholders because it leads to higher retained earnings and negatively impacts firm value by reducing the value of cash. We examine alternative explanations for our findings. In particular, we find only weak evidence that the possibility of risk learning or regional spillover effects may influence our results.

This paper investigates the determinants of value and growth investing in a large administrative panel of Swedish residents over the 1999-2007 period. We document strong relationships between a household's portfolio tilt and the household's financial and demographic characteristics. Value investors have higher financial and real estate wealth, lower leverage, lower income risk, lower human capital, and are more likely to be female than the average growth investor. Households actively migrate to value stocks over the life-cycle and, at higher frequencies, dynamically offset the passive variations in the value tilt induced by market movements. We verify that these results are not driven by cohort effects, financial sophistication, biases toward popular or professionally close stocks, or unobserved heterogeneity in preferences. We relate these household-level results to some of the leading explanations of the value premium.

This article comments on the judgment no 12193 rendered by the Sezioni Unite of the Corte di Cassazione on 8 May 2019, where the recognition and registration, in Italy, of a foreign parental order inscribing the nonbiological parent as the children’s legal father were denied on the ground that they violated the prohibition of surrogacy under Italian law, which was considered to be of public policy. It scrutinises this judgment in two steps. First, it criticises the court’s methodology in the construction of the notion of public policy both in general and with particular regard to the surrogacy ban. Second, it examines whether the best interests of the children involved were sufficiently taken account of, and it finds that they were not. It concludes that, contrary to what the law prescribes, this judgment failed to give voice to the children born via surrogacy abroad and living with parents of the same sex.

We study retirement incentives with an augmented reduced form option value model à la Stock and Wise (1990). We propose methodological extensions to better reflect the respective incentives faced by singles and couples. Our results show that a more comprehensive modeling of couples’ incentives leads to very different patterns of retirement incentives – particularly for women. We apply the new indicators to data from the Survey of Health, Ageing and retirement in Europe in Belgium and find two key results. First, contrary to several previous studies, we obtain a positive signed income effects. Second, we find very different retirement incentives for men and women, with little flexibility in the retirement decision for men and substantially more flexibility for women as a function of financial incentive and household composition.

This is a pre-print of an article published in Computational Economics. The final authenticated version is available online at: https://doi.org/10.1007/s10614-019-09879-x In this paper, we develop a very flexible and comprehensive state-space framework for modeling time series data. Our research extends the simple canonical model usually employed in the literature, into a panel-data time-varying parameters framework, combining fixed (both common and country-specific) and varying components. Under some specific circumstances, this setting can be understood as a mean-reverting panel time-series model, where the mean fixed parameter can, at the same time, include a deterministic trend. Regarding the transition equation, our structure allows for the estimation of different autoregressive alternatives, and include control instruments, whose coefficients can be set-up either common or idiosyncratic. This is particularly useful to detect asymmetries among individuals (countries) to common shocks. We develop a GAUSS code that allows for the introduction of restrictions regarding the variances of both the transition and measurement equations. Finally, we use this empirical framework to test for the Feldstein–Horioka puzzle in a 17-country panel. The results show its usefulness for solving complexities in macroeconomic empirical research.

We examine whether a firm’s decision to disclose non-financial proprietary information depends on peer disclosures of similar information. Using a sample of 5,035 unique clinical trials by U.S. pharmaceutical firms over the 2007-2014 period, we find that the firm is less likely to disclose its own clinical trial results if peers have published clinical trial results pertaining to the same medical condition. Conditional on disclosing clinical trial results, the firm is also less likely to disclose the trial results on time when peers have disclosed their clinical trial results. Our cross-sectional tests suggest that proprietary costs of disclosure play an important role in the relation between peer disclosures and the firm’s own disclosure. In particular, the negative relation is more pronounced when proprietary costs of disclosure are higher. Taken together, our findings provide new evidence on the interplay between peer and own disclosures of non-financial proprietary information.

Do the rich always get richer by investing in a cryptocurrency for which new coins are issued according to a proof-of-stake (PoS) protocol? We answer this question in the negative: Without trading, the investor shares in the cryptocurrency are martingales that converge to a well-defined limiting distribution and, hence, are stable in the long run. This result is robust to allowing trading when investors are risk neutral. Then, investors have no incentive to accumulate coins and gamble on the PoS protocol but weakly prefer not to trade. This paper was accepted by Kay Giesecke, finance.

Unrealized gains and losses on available-for-sale securities (AFSGL) are included in Other Comprehensive Income (OCI) and directly affect shareholders’ equity but are not included in earnings. We investigate whether unrealized AFSGL help predict future earnings and whether analysts and investors incorporate the information conveyed by unrealized AFSGL in a timely manner. We conduct our investigation on a sample of banks because unrealized AFSGL are material in the banking industry. First, we show that unrealized AFSGL are material and help in predicting next period realized AFSGL and future earnings change. Second, we document that financial analysts are slow to react to unrealized AFSGL and update their forecasts after AFSGL are realized in earnings. Third, we find that investors are also slow to react to unrealized AFSGL and do so only after AFSGL are included (realized) in earnings and after financial analysts update their forecasts. We document an annual difference of 5% in future abnormal returns between banks in the top and bottom quintiles of past unrealized AFSGL. A zero-cost trading strategy that relies on public information about unrealized AFSGL generates a sizeable monthly alpha that ranges between 1.8% and 1.9%.

We propose a new model of interdealer trading. Dealers trade together to reduce their inventory holding costs. Core dealers share these costs efficiently and provide liquidity to peripheral dealers, who have heterogeneous access to core dealers. We derive predictions about the effects of peripheral dealers' connectedness to core dealers and the allocation of aggregate inventories between core and peripheral dealers on the distribution of interdealer prices, the efficiency of interdealer trades, and trading costs for the dealers' clients. For instance, the dispersion of interdealer prices is higher when fewer peripheral dealers are connected to core dealers or when their aggregate inventory is higher.

An important challenge for many firms is to identify the life transitions of its customers, such as job searching, expecting a child, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firms opportunities to be more relevant to their customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely to be job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, we use the latent states of the HMM to fuse cross-sectional survey responses to a job-seeking status question with longitudinal user activity data, resulting in a partially HMM. Thus, in some time periods, and for some users, we observe a direct measure of the true job-seeking status. We demonstrate that the proposed model can predict not only which users are likely to be job seeking at any point in time but also what activities on the platform are associated with job search and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 29% increase in profits of a targeting campaign relative to the approach that was used by the social network platform. This paper was accepted by Juanjuan Zhang, marketing.

Automakers, including Toyota and General Motors, were recently caught by the U.S. regulator for deliberately hiding product defects in an attempt to avoid massive recalls. Interestingly, regulators in the United States and United Kingdom employ different policies in informing consumers about potential defects: the U.S. regulator publicly announces all ongoing investigations of potential defects to provide consumers with early information, whereas the UK regulator does not. To understand how these different announcement policies may affect cover-up decisions of automakers, we model the strategic interaction between a manufacturer and a regulator. We find that, under both countries’ policies, the manufacturer has an incentive to cover up a potential defect when there is a high chance that the defect indeed exists and it may inflict only moderate harm. However, if there is only a moderate chance that the defect exists, only under the U.S. policy does the manufacturer have an incentive to cover up a potential defect with significant harm. We show that the U.S. policy generates higher social welfare only for very serious issues for which both the expected harm and recall cost are very high and the defect is likely to exist. We make four policy recommendations that could help mitigate manufacturers’ cover-ups, including a hybrid policy in which the regulator conducts a confidential investigation of a potential defect only when it may inflict significant harm. This paper was accepted by Vishal Gaur, operations management.

How do resolution frameworks affect the private restructuring of distressed banks? We model a distressed bank’s shareholders and creditors negotiating a restructuring given asymmetric information about asset quality and externalities onto the government. This yields negotiation delays used to signal asset quality. We find that strict bail-in rules increase delays by worsening informational frictions and reducing bargaining surplus. We characterize optimal bail-in rules for the government. We then consider the government’s possible involvement in negotiations. We find this can lead to shorter or longer delays. Notably, the government may gin from committing not to partake in negotiations.

We provide a new methodology to empirically investigate the respective roles of systematic and idiosyncratic skewness in explaining expected stock returns. Using a large number of predictors, we forecast the cross-sectional ranks of systematic and idiosyncratic skewness which are easier to predict than their actual values. Compared to other measures of ex ante systematic skewness, our forecasts create a significant spread in ex post systematic skewness. A predicted systematic skewness risk factor carries a significant risk premium that ranges from 7% to 12% per year and is robust to the inclusion of downside beta, size, value, momentum, profitability, and investment factors. In contrast to systematic skewness, the role of idiosyncratic skewness in pricing stocks is less robust. Finally, we document how the determinants of systematic and idiosyncratic skewness differ.

We study the effect of dividend taxes on the payout and investment policies of publicly listed firms. To do so, we exploit a unique setting in Switzerland where, following the corporate tax reform of 2011, some but not all firms were suddenly able to pay tax-exempt dividends to their shareholders. Using a difference-in-differences specification, we show that treated firms swiftly and permanently increase their dividend payout by around 30% compared to control firms after the tax cut. When studying the effect of agency conflicts, we show that the impact on the payout is less pronounced for firms in which the controlling shareholders have more voting rights than cash-flow rights. We find a significant positive abnormal stock return after the announcement of the payment of a tax-exempt dividend. However, reducing dividend taxes does not boost investment. This is due to a significant drop in retained earnings and to the fact that equity issuances do not surge after the tax cut. Our evidence is consistent with models where the marginal source of finance is retained earnings, and inconsistent with the neoclassical theory of dividend taxation.

© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. There is a discrepancy between CAPM-implied and realized returns. Using the CAPM in capital budgeting - as recommended in textbooks - should thus have real effects. For instance, low beta projects should be valued more by CAPM users than by the market. We test this hypothesis using M&A data and show that bids for low-beta private targets entail lower bidder returns. We provide further support by testing several ancillary predictions. Our analyses suggest that using the CAPM when valuing targets leads to valuation errors (relative to the market's view) corresponding on average to 12% to 33% of the deal values.

Both in the United States and in the Euro area, bank supervision is the joint responsibility of local and central/federal supervisors. I study how such a system can optimally balance the lower inspection costs of local supervisors with the ability of the central level to internalize cross-border or interstate externalities. The model predicts that centralised supervision endogenously increases market integration and cross-border externalities, further strengthening the need for centralised supervision. This complementarity implies that, for some parameterizations of the model, the economy can be trapped in a local supervision equilibrium with low supervision and integration. In such a case, the forward-looking introduction of a centralized supervisory architecture achieves a superior equilibrium. JEL Classification: D53, G21, G28, G33, G38, L51

Firm political contributions are associated with lower credit default swap spreads for contributing firms. To address endogeneity, we employ novel instruments and use a set of exogenous events on campaign contribution restrictions: (a) the passage of the Bipartisan Campaign Reform Act (BCRA) that banned soft money contributions, (b) the Federal Election Commission decision to interpret the BCRA less strictly, (c) the partial reversal of the BCRA and, (d) the McConnell v. FEC Supreme Court decision, which upheld the BCRA. Overall, the evidence suggests that political contributions are valued by credit market participants.

Research Summary: Firms and nongovernmental organizations (NGOs) often collaborate to establish new supply chains. With a formal model, we analyze how NGOs can alleviate market failures and improve supplier economic inclusion while strategically interacting with firms. We account for the specific goals of the NGO and the need to induce collaboration between firms and their suppliers. The analysis reveals a “valley of frustration,” when NGO efforts benefit all actors but only marginally the firm. We also show that more powerful firms might prefer to internalize NGO functions, while firms with lower bargaining power and higher investment requirements are better off collaborating with NGOs. Finally, we study NGOs‐firms matching patterns and find that firms with higher bargaining power match with NGOs holding stronger capabilities. Managerial Summary: This article analyzes interactions between firms and nongovernmental organizations (NGOs) aiming to improve the economic inclusion of suppliers or to promote the adoption of specific (e.g., sustainable) practices. For firm executives, this study shows the constraints and benefits associated with working with NGOs, the conditions under which integration of NGO functions is preferable as well as the types of NGOs that offer better prospects for a successful collaboration. For NGO executives, it highlights the need to provide enough economic incentives to firms and suppliers alike to ensure their collaboration and the trade‐offs associated with this constraint, in particular, if NGO capabilities are limited. Overall, the study provides a comprehensive understanding of how NGO activities can influence value creation in a vertical value chain.

The correlation across US states in house price growth increased dramatically between 1976 and 2000. This paper shows that the contemporaneous geographic integration of the US banking market, via the emergence of large banks, was a primary driver of this phenomenon. To this end, we first theoretically derive an appropriate measure of banking integration across state pairs and document that house price growth correlation is strongly related to this measure of financial integration. Our IV estimates suggest that banking integration can explain up to one third of the rise in house price correlation over the period.

We examine the relation between the presence of U.S. government as a major customer and a supplier firm’s loan contract terms, using major corporate customers as a benchmark. We find that firms with major government customers are associated with fewer covenants and a lower likelihood of having performance pricing provisions in their loan contracts. In contrast, we do not find such associations for firms with major corporate customers. Further, we find no evidence that the existence of major government customers is related to the supplier firm’s loan spread, security, or maturity. We conjecture that lenders benefit from the stricter monitoring of the government as a major customer and thus use fewer covenants and performance pricing provisions when lending to firms with major government customers than when lending to those with major corporate customers. We provide evidence consistent with this conjecture.

In this chapter, we first review the two main approaches to categories (understood from a cognitive perspective) found in the current literature. The ecological and the goal-based approaches to categories vary in terms of premises (prototypes vs. ideals), processes (central tendency and family resemblance vs. conceptual combination and goal achievement), and economic and institutional consequences. We then present three modes of reconciliation between the two approaches (intra-audience, inter-audience and as a function of field evolution) and integrate them more broadly into the institutional literature. Finally, in the last section, we expose fruitful areas for future research: we account for the domain validity of each approach; emphasize ignored research avenues at the field, organizational and individual levels; and finally make stronger connections between category research and strategy research.