Advertising is a critical competitive tool that shapes interactions among firms in the product market. Using third-party tracked data on advertising outlet costs, I find that a nontrivial portion of public firms, even those with intense advertising activities, do not disclose advertising expenses in their financial statements, indicating significant disclosure discretion. I further use product category-level advertising data to develop a firm-specific measure of advertising rivalry. I predict and find that advertising rivalry is negatively associated with the likelihood of disclosing advertising expenses. This negative association is more pronounced when firms advertise on less trackable media outlets or have more mature products. These findings suggest that firms consider their advertising expenses proprietary and that concerns about advertising competition discourage the disclosure of advertising expenses.

Over the last decades, firms have been incorporating digital technologies into their operations, a process known as digitalization. Nevertheless, understanding the link between digitalization and firm performance remains challenging. We propose a new firm-level measure of digital intensity based on textual analysis of business descriptions and quarterly earnings calls. To overcome endogeneity, we use two quasi-natural experiments: the COVID-19 pandemic and shocks involving suppliers affected by U.S. natural disasters. Non-technological firms with higher pre-shock digital intensity experience higher abnormal returns, higher profitability, and higher revenue growth during the shocks. The supply chain is one of the areas through which digitalization contributes to significantly mitigate the effects of these shocks, thereby enhancing firm resilience.

This paper provides the first study on how management structure influences hedge fund performance and risk. We document that hedge funds less tied to traditional assets often choose solo management structures. Solo-managed funds outperform team-managed funds, exhibit better skills in market return, volatility, and crisis timing, and demonstrate greater activity in beta management, but have higher idiosyncratic and tail risks. They are also less likely to be liquidated, with fund flows less performance sensitive. Using a sample of switched funds, we find that fund performance, assets, and risk correlate with the management structure switching decision.

We find that disclosing bank-specific information reallocates systemic risk, but whether it mitigates systemic bank runs depends on the nature of information disclosed. Disclosure reveals banks’ resilience to adverse shocks and shifts systemic risk from weak to strong banks. Yet, only disclosure of banks’ exposure to systemic risk can mitigate systemic bank runs because it shifts systemic risk from more vulnerable banks to those less vulnerable. Disclosure of banks’ idiosyncratic shortfalls of funds does not differentiate such exposure, rendering the resultant reallocation of systemic risk ineffective in mitigating systemic runs.

We examine how financial disclosure policy affects a firm manager's strategy to innovate within a two‐period bandit problem featuring two production methods: an old method with a known probability of success, and a new method with an unknown probability. Exploring the new method in the first period provides the manager with decision‐useful information for the second period, thus creating a real option that is unavailable under exploiting the old known production method. Voluntary disclosure of the firm's financial performance provides the manager with another option to potentially conceal initial failure from the market. The interaction of these two options determines the manager's incentive to explore. In equilibrium, a myopic manager who cares about the interim market price may over‐ or under‐explore compared to the optimal exploration strategy that maximizes firm value. Our analysis shows that firms operating in an environment with voluntary disclosure early in the trial stage and mandated requirement later are most motivated to explore, while firms subject to early mandated disclosure and late voluntary disclosure are least likely to do so. We also provide empirical predictions about the link between the disclosure environment and the intensity and efficiency of corporate innovation.

Joining the debate on the banking sector's impact on the real economy, this study examines the impact of banks' market power on local businesses' financial reporting quality. Based on the market power hypothesis and the information-based hypothesis, we propose four ways the banking market could affect the financial reporting quality. The proposed mechanisms suggest that borrowers and bank lenders face increased market power by implementing different earnings management and monitoring practices. Our documentary evidence suggests that since the banking market deregulation, restrictions on inter- and intra-state banking and branching have been removed, with banks gaining more power and the market becoming more consolidated. Using a large sample of U.S. listed firms from 1995 to 2019, we find a favourable impact of bank market power on corporate financial reporting quality, primarily driven by heightened monitoring by banks with greater market power, supporting the monitoring-stringent conjecture. In addition, this positive relationship is more pronounced among firms heavily reliant on local banks. Our results are robust to a rich set of tests, such as using alternative measurements for financial reporting quality and bank market power, including macroeconomic factors, and considering drastic changes in the bank market structure. We also address the endogeneity concerns and test the robustness of our key findings in a loan syndication setting. Our research suggests that facing increased bank market concentration and power, firms must pay additional attention to their financial reporting, which is widely used to access external finance.

The firm‐level bullwhip effect is the amplification of demand uncertainty along a supply chain—that is, fluctuations in production (for manufacturing firms) or purchases from suppliers (for retailers or wholesalers) in a firm tend to be greater than its demand fluctuations. We predict that the bullwhip ratio (a proxy for the bullwhip effect) amplifies the relation between demand uncertainty and cost structure. We expect this amplifying effect because the bullwhip ratio determines the extent to which demand uncertainty translates into uncertainty in production or purchases, which, in turn, affects cost structure. Using data from public US firms over the 1990–2020 period, we find results consistent with our prediction. Specifically, we find that both the negative relation between demand uncertainty and cost elasticity in the manufacturing sector and the positive relation between the two in the retail/wholesale sectors are stronger for firms with higher bullwhip ratios. We contribute to the literature on cost structure by highlighting the important role of the bullwhip effect in cost structure decisions.

This study examines the relationship between government subsidies and income smoothing using a sample of US‐listed firms. We find that subsidized firms smooth their earnings more aggressively than their unsubsidized peers. This finding is consistent with the reasoning that subsidized firms bear higher political costs and have more incentives to smooth earnings to avoid public attention. In addition, smoothing by subsidized firms is more pronounced when the subsidies are granted through non‐tax‐related channels than through tax‐based channels, and the positive association between government subsidies and income smoothing is stronger for firms under higher public scrutiny and with less transparent information environments. Further analysis shows that smoothing by subsidized firms serves mainly to obfuscate earnings and that subsidized firms that smooth earnings tend to continue receiving subsidies in the future. Overall, our results help explain the role of government subsidies in shaping firms' accounting and disclosure choices.

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). The standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair, and thus, is conservative. The analytical inference involves estimating multiple functional quantities that requires several tuning parameters. In this paper, we propose two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. In particular, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.