HBS Scholarly Articles

We examine which independent directors are held accountable when investors sue firms for financial- and disclosure-related fraud. Investors can name independent directors as defendants in lawsuits, and they can vote against their re-election to express displeasure over the directors' ineffectiveness at monitoring managers. In a sample of securities class-action lawsuits from 1996 to 2010, about 11% of independent directors are named as defendants. The likelihood of being named is greater for audit committee members and directors who sell stock during the class period. Named directors receive more negative recommendations from Institutional Shareholder Services (ISS), a proxy advisory firm, and significantly more negative votes from shareholders than directors in a benchmark sample. They are also more likely than other independent directors to leave sued firms. Overall, shareholders use litigation along with director elections and director retention to hold some independent directors more accountable than others when firms experience financial fraud.

Under fairly general assumptions, expected stock returns are a linear combination of two accounting fundamentals―book to market and ROE. Empirical estimates based on this relation predict the cross section of out-of-sample returns in 26 of 29 international equity markets, with a highly significant average slope coefficient of 1.05. In sharp contrast, standard factor-model-based proxies fail to exhibit predictive power internationally. We show analytically and empirically that the importance of ROE in forecasting returns depends on the quality of accounting information. Overall, a tractable accounting-based valuation model provides a unifying framework for obtaining reliable proxies of expected returns worldwide.

We provide among the first empirical evidence consistent with recent macro global-game crisis models, which show that the precision of public signals can coordinate crises (e.g., Angeletos and Werning, 2006; Morris and Shin, 2002, 2003). In these models, self-fulfilling crises (independent of poor fundamentals) can occur only when publicly disclosed signals of fundamentals have high precision; poor fundamentals are the sole driver of crises only in low precision settings. We find evidence consistent with this proposition for 68 currency and systemic banking crises in 17 countries from 1983 to 2005. We exploit a key publicly disclosed signal of fundamentals that drives financial markets, namely accounting data, and find that pre-crisis accounting signals of fundamentals are significantly lower only in low precision countries.

This paper offers guidance and shares collective wisdom for accounting Ph.D. students who will be entering the academic job market. It is divided into two sections. The first offers subjective advice on the dissertation process — from choosing a topic to surviving the inevitable self-doubt — from my personal experience and the experiences of other former job candidates. The second section mostly focuses on factual components of the job market, providing details that will be useful to candidates before they begin the search. It concludes with subjective advice on how to make the job hunt more enjoyable. Both sections are organized chronologically and attempt to be comprehensive, beginning with choosing a dissertation topic and adviser, and concluding with the decision to accept an offer.

Do differences in countries' accounting standards affect global investment decisions? We explore this question by examining how accounting distance, the difference in the accounting standards used in the investor's and the investee's countries, affects the asset allocation decisions of global mutual funds. We find that investors tend to underweight investees with greater accounting distance. Using the mandatory adoption of International Financial Reporting Standards (IFRS) as an event that changed the accounting standards of various country-pairs, we examine how two sources of changes in accounting distance—(i) change due to IFRS adoption of the investee and (ii) change due to IFRS adoption in the investor's country—affect global portfolio allocation decisions. We find that the tendency to underinvest in investees with greater accounting distance significantly weakens when accounting distance is reduced either from an investee's IFRS adoption or from IFRS adoption in the investor's country. The latter finding holds despite the fact that IFRS adoption in the investor's country had no impact on the accounting standards under which the investee firms present their financial information; the only change is in the investor's familiarity with these standards. This suggests that differences in accounting standards affect investor demand by imposing greater information-processing cost on those less familiar with the reporting standards.

This paper examines the determinants and consequences of hedge fund activism with a focus on activist directors, i.e., those directors appointed in response to demands by activists. Using a sample of 1,969 activism events over the period 2004–2012, we identify 824 activist directors. We find that activists are more likely to gain board seats at smaller firms and those with weaker stock price performance. Activists remain as shareholders longer when they have board seats, with holding periods consistent with conventional notions of “long-term” institutional investors. As in prior research, we find positive announcement-period returns of around 4–5% when a firm is targeted by activists, and a 2% increase in return on assets over the subsequent one to five years. We find that activist directors are associated with significant strategic and operational actions by firms. We find evidence of increased divestiture, decreased acquisition activity, higher probability of being acquired, lower cash balances, higher payout, greater leverage, higher CEO turnover, lower CEO compensation, and reduced investment. With the exception of the probability of being acquired, these estimated effects are generally greater when activists obtain board representation, consistent with board representation being an important mechanism for bringing about the kinds of changes that activists often demand.

We propose an activity-generating theory of regulation. When courts make errors, tort litigation becomes unpredictable and as such imposes risk on firms, thereby discouraging entry, innovation, and other socially desirable activity. When social returns to activity are higher than private returns, it may pay the society to generate some information ex ante about how risky firms are and to impose safety standards based on that information. In some situations, compliance with such standards should entirely preempt tort liability; in others, it should merely reduce penalties. By reducing litigation risk, this type of regulation can raise welfare.

Buying a product has never been easier. Consumers can shop online, over the phone or via mail order, from home or on the go, and if they want to experience touch and feel, they can also visit a “real” store. Often, one and the same retailer offers several of these options, and multichannel retailing has become common in most product categories. By offering several channels, retailers are trying to reach more consumer segments and create synergies, with stores acting as billboards for the brand, catalogs providing enticing reminders to buy and the Internet providing an ever-present storefront. But synergies do not arise automatically. Different channels can also cannibalize one another, and it is not always easy to predict which effects will prevail. A recent study took a closer look at the interplay among different retail channels and showed that the short-term effects of store openings can be very different from the long-term sales impact.

The authors propose a conceptual framework to explain whether and when the introduction of a new retail store channel helps or hurts sales in existing direct channels. A conceptual framework separates short- and long-term effects by analyzing the capabilities of a channel that help consumers accomplish their shopping goals. To test the theory, the authors analyze a unique data set from a high-end retailer using matching methods. The authors study the introduction of a retail store and find evidence of cross-channel cannibalization and synergy. The presence of a retail store decreases sales in the catalog but not the Internet channel in the short run but increases sales in both direct channels over time. Following the opening of the store, more first-time customers begin purchasing in the direct channels. These results suggest that adding a retail store to direct channels yields different results from adding an Internet channel to a retail store channel, as previous research has indicated.

We study the frequency of restatements by foreign firms listed on U.S. exchanges. We find that the restatement rate of U.S. listed foreign firms is significantly lower than that of comparable U.S. firms and that the difference depends on the firm's home country characteristics. Foreign firms from countries with a weak rule of law are less likely to restate than are firms from strong rule of law countries. While the lower rate of restatements can represent an absence of errors, it can also indicate a lack of detection and disclosure of errors and irregularities. We infer the magnitude of detection and disclosure by associating the frequency of restatements with the quality of the firm's internal reporting system. We find that only U.S. firms and foreign firms from strong rule of law countries show a positive association between restatement frequency and internal control weaknesses. Firms from weak rule of law countries show no significant association. We interpret these findings as home country enforcement affecting firms' likelihood of detecting and reporting existing accounting irregularities. This suggests that for U.S. listed foreign firms, less frequent restatements can be a signal of opportunistic reporting rather than a lack of accounting errors and irregularities.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant performance gains over established algorithms. These include studying algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

In 2019, Germany passed the Digital Healthcare Act, which, among other things, created a “Fast-Track” regulatory and reimbursement pathway for digital health applications in the German market. The pathway explicitly provides for flexibility in how researchers can present evidence for new digital products, including the use of real world data and real-world evidence. Against this backdrop, the Digital Medicine Society and the Health Innovation Hub of the German Federal Ministry of Health convened a set of roundtable discussions to bring together international experts in evidence generation for digital medicine products. This Viewpoint highlights findings from these discussions with the aims of (1) accelerating and stimulating innovative approaches to digital medical product evaluation, and (2) promoting international harmonisation of best evidentiary practices. Advancing these topics and fostering international agreement on evaluation approaches will be vital to the safe, effective, and evidence-based deployment and acceptance of digital health applications globally.

Standard theories of corporate ownership assume that because markets are efficient, insiders ultimately bear all agency costs that they create and therefore have a strong incentive to minimize conflicts of interest with outside investors. We argue that if equity is overvalued, however, mispricing offsets agency costs and can induce a controlling shareholder to list equity. Higher valuations may support listings associated with greater agency costs. We test the predictions that follow from this idea on a sample of publicly listed subsidiaries in Japan. Subsidiaries in which the parent sells a larger stake and subsidiaries with greater scope for expropriation by the parent firm are more overpriced at listing, and minority shareholders fare poorly after listing as mispricing corrects. Parent firms often repurchase subsidiaries at large discounts to valuations at the time of listing and experience positive abnormal returns when repurchases are announced.

I examine the extent to which the FASB’s agenda determination is a function of the contemporaneous preferences of its primary constituents: auditors, preparers, and financial statement users. Using the FASB’s consultation with the FASAC as a lens through which to view constituent preferences, I find evidence that from 1982 to 2001 influence on FASB agenda decisions is concentrated among “Big N” audit firms, whereas from 2002 to 2006 the preferences of financial constituents appear most significant. Across both periods, I find no evidence of significant preparer influence in agenda formation, which is in contrast to their documented role in later stages of standard setting. Collectively, the results contribute to our understanding of the influence of constituents in standard setting and highlight a shift in that influence over time.

Agent-based modeling is a longstanding but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions over time. ABMs are especially useful when investigating group behavior or evolutionary processes and can uniquely reveal non-linear dynamics and emergence—the process whereby local interactions aggregate into often surprising collective phenomena, such as spatial segregation and relational homophily. We review several illustrative ABMs, describe the strengths and limitations of this method, and address two misconceptions about ABMs: reductionism and “you get out what you put in.” We also offer maxims for good and bad ABMs, give practical tips for beginner modelers, and include a list of resources and other models. We conclude with a 7-step guide to creating your own model.

This chapter reviews academic research on the connections between agglomeration and innovation. We first describe the conceptual distinctions between invention and innovation. We then describe how these factors are frequently measured in the data and some resulting empirical regularities. Innovative activity tends to be more concentrated than industrial activity, and we discuss important findings from the literature about why this is so. We highlight the traits of cities (e.g., size, industrial diversity) that theoretical and empirical work link to innovation, and we discuss factors that help sustain these features (e.g., the localization of entrepreneurial finance).

We model spatial clusters of similar firms. Our model highlights how agglomerative forces lead to localized, individual connections among firms, while interaction costs generate a defined distance over which attraction forces operate. Overlapping firm interactions yield agglomeration clusters that are much larger than the underlying agglomerative forces themselves. Empirically, we demonstrate that our model's assumptions are present in the structure of technology and labor flows within Silicon Valley and its surrounding areas. Our model further identifies how the lengths over which agglomerative forces operate influence the shapes and sizes of industrial clusters; we confirm these predictions using variations across patent technology clusters.

Because consumer reviews leverage the wisdom of the crowd, the way in which they are aggregated is a central decision faced by platforms. We explore this "rating aggregation problem" and offer a structural approach to solving it, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. Applying this to restaurant reviews from Yelp.com, we construct an adjusted average rating and show that even a simple algorithm can lead to large information efficiency gains relative to the arithmetic average.

Firms increasingly use both mass-media advertising and targeted personal selling to successfully promote products and brands in the marketplace. In this study, we jointly examine the effect of mass-media advertising and personal selling in the context of U.S. presidential elections, where the former is referred to as the “air war” and the latter the “ground game.” Specifically, we look at how different types of advertising―candidate own ads vs. outside ads―and personal selling―in the form of utilizing field offices―affect voter preferences. Further, we ask how these various campaign activities affect the outcome of elections through their diverse effects on various types of people. We find that personal selling has a stronger effect among partisan voters, while candidate's own advertising is better received by non-partisans. We also find that personal selling accounted for the Democratic victories in the 2008 and 2012 elections, and that advertising was critical only in a close election, such as the one in 2004. Interestingly, had the Democrats received more outside advertising in 2004, the election would have ended up in a 269-269 tie. Our findings generate insights on how to allocate resources across and within channels.

Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of domain experience are optimal for algorithm-augmented performance, due to the interplay between two countervailing forces—ability and aversion. Although domain experience can increase performance via increased ability to complement algorithmic advice (e.g., identifying inaccurate predictions), it can also decrease performance via increased aversion to accurate algorithmic advice. Because ability developed through learning by doing increases at a decreasing rate, and algorithmic aversion is more prevalent among experts, we theorize that algorithm-augmented performance will first rise with increasing domain experience, then fall. We test this by exploiting a within-subjects experiment in which corporate information technology support workers were assigned to resolve problems both manually and using an algorithmic tool. We confirm that the difference between performance with the algorithmic tool versus without the tool was characterized by an inverted U-shape over the range of domain experience. Only workers with moderate domain experience did significantly better using the algorithm than resolving tickets manually. These findings highlight that, even if greater domain experience increases workers’ ability to complement algorithms, domain experience can also trigger other mechanisms that overcome the positive ability effect and inhibit performance. Additional analyses and participant interviews suggest that, even though the highest experience workers had the greatest ability to complement the algorithmic tool, they rejected its advice because they felt greater accountability for possible unintended consequences of accepting algorithmic advice.