To help a firm reduce inefficiencies associated with equipment capacity planning, we propose a dual-mode equipment procurement (DMEP) framework. DMEP combines dual-source (i.e., a less-expensive-but-slower base mode and a faster-but-more-expensive flexible mode) procurement with option contracts in three layers: a contract negotiation layer, where the firm chooses the best combination of lead time and price for each mode from the supply contract menu; a capacity reservation layer, where the firm reserves total equipment procurement quantities from the two supply modes before the planning horizon starts; and an execution layer, where the firm orders equipment from the two supply modes based on the updated demand information. We first investigate the execution layer as a dynamic dual-source capacity expansion problem with demand backlogging and demonstrate that the optimal policy lacks structure even under the simplest setting. Thus, we propose a heuristic solution for the execution-layer problem, which also serves as a building block for the other two layers. Through numerical analysis, we quantify the value of the added flexibility of DMEP for the firm. The DMEP framework has been implemented at Intel Corporation and has resulted in savings of tens of millions of dollars for one process technology.

We analyze a finite-horizon dynamic pricing model in which demand at each period depends on not only the current price but also past prices through reference prices. A unique feature but also a significant challenge in this model is the asymmetry in reference price effect, which implies that the underlying optimization problem is nonsmooth and no standard optimization methods can be applied. We identify a few key structural properties of the problem, which enable us to develop strongly polynomial-time algorithms to compute the optimal prices for several plausible scenarios. We complement our exact algorithms by proposing an approximation heuristic and provide an upper bound on the optimal objective value. Finally, we conduct numerical experiments to study the optimal price path and demonstrate the value of dynamic pricing when demands are seasonal. We further compare numerically one of the exact algorithms with the heuristic and offer managerial suggestions. This paper was accepted by Yinyu Ye, optimization.

ABSTRACT We exploit an exogenous shock to analyst coverage as a result of brokerage house mergers and closures to examine whether financial analysts influence the tax‐planning activities of the firms they cover. Using a difference‐in‐differences design, we find that, on average, firms affected by broker mergers and/or closures experience a reduction in their GAAP (cash) effective tax rates (ETR) of 2.5 percent (2.6 percent), relative to control firms, translating into average tax expense (cash tax) savings of $34 ($35) million. The treatment effect is more pronounced among firms with lower pre‐event analyst coverage. To explore how analysts affect tax planning, we further document that the treatment effect is greater among firms that lose an analyst who provided an implied ETR forecast in the past, suggesting that analysts influence tax planning via their tax‐specific research efforts. In addition, we find that after merger/closure, weakly governed firms increase their use of aggressive tax strategies, and financially distressed firms experience a larger reduction of cash effective tax rates, relative to control firms. Overall, we provide evidence that a shock to analyst coverage sufficiently changes the cost‐benefit trade‐off of tax planning.

ABSTRACT This study investigates whether companies engage in audit opinion shopping activities by exerting influence over an audit firm's decision to switch the engagement partner (“partner‐level opinion shopping”) in the Chinese setting, where the identities of engagement partners are publicly disclosed. Adopting the empirical framework developed by Lennox [2000], we show evidence that companies successfully engage in partner‐level opinion shopping. Further, partner‐level opinion shopping is more likely to be successful if a company is economically important to an audit firm, and it is less likely to be successful if the audit firm is formed as a partnership rather than a corporation. We also find that companies successfully engaging in partner‐level opinion shopping exhibit significantly lower earnings quality. Finally, we directly compare audit records between incoming and outgoing partners and find that, for companies that successfully improve audit opinions after partner switching, incoming partners have a significantly higher propensity to issue clean opinions than their outgoing counterparts.

When reporting after-tax non–generally accepted accounting principles (GAAP) earnings, firms are required to adjust for the tax effects of exclusions. Since 2010, the Securities and Exchange Commission (SEC) has issued and updated compliance and disclosure interpretations (C&DIs), which specifically require firms to disclose the tax effects of exclusions. We assemble a detailed, hand-collected data set of S&P 1500 firms’ disclosures to provide the first large-sample evidence on the reporting of the tax effects of non-GAAP exclusions. We find three key results. First, echoing the SEC’s concern, a significant proportion of non-GAAP reporting firms do not follow the C&DI guidelines (i.e., they do not disclose the tax effects of exclusions). Second, among firms that disclose the tax effects of exclusions, we find that managers strategically select the tax rates applied to exclusions to achieve after-tax earnings targets. Third, manager-reported non-GAAP earnings are less persistent for future operating earnings and cash flows relative to non-GAAP earnings calculated by applying various benchmark tax rates to exclusions. This evidence suggests that managers’ strategic behavior in selecting the tax rates applied to exclusions pollutes reported non-GAAP earnings and reduces their usefulness for predicting future performance. Overall, our results shed light on a specific channel through which firms use non-GAAP reporting to meet or beat earnings expectations. This paper was accepted by Brian Bushee, accounting. Funding: N. (X.) Chen appreciates financial support from the University of Houston. T. Shevlin acknowledges financial support from the Paul Merage School of Business at the University of California–Irvine. P.-C. Chiu acknowledges the financial support received from the Hong Kong Research Grant Council [Grant RGC14522716]. Supplemental Material: Data files and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4433 .