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Resource Adjustment Costs, Cost Stickiness, and Value Creation in Mergers and Acquisitions*

Contemporary Accounting Research 2021 38(3), 2264-2301
We examine whether resource adjustment costs, such as installation and disposal costs for fixed assets, or hiring and firing costs for employees, impede value creation in mergers and acquisitions (M&A). We focus on M&A deals because they are major corporate investment decisions. As a proxy for adjustment costs, we use a firm‐level measure of cost stickiness. We predict that acquirers with high adjustment costs have less flexibility in restructuring resources following the acquisition and will find it more costly to merge the target firm's operations. Consistent with this prediction, we find that the acquirer's adjustment costs are negatively associated with abnormal returns around the acquisition announcement. Additionally, adjustment costs are also negatively associated with deal synergies. Relatedly, we find that acquirers with high adjustment costs purchase targets with high adjustment costs. In accordance with this finding, we show that acquirers with high adjustment costs purchase intangible‐intensive targets. Collectively, our results highlight the important implication of adjustment costs in M&A deals for managers and capital market participants.

Cost Structure, Operating Leverage, and CDS Spreads

The Accounting Review 2021 96(5), 79-105
We provide evidence that credit investors do not fully impound the implications of firms' cost structure (or operating leverage) when pricing credit default swaps. Information about firms' cost structure is not disclosed and needs to be estimated. Furthermore, the performance implications of firms' cost structure depend on the expected macroeconomic conditions. We focus on the debt market because of the strong emphasis of this market on downside risk. To measure expected aggregate macroeconomic conditions, we employ the change in the anxious index (AI), which is the probability of a decline in real GDP provided by the SPF—the survey of professional forecasters. We find that the interaction between the firm's cost structure and change in AI predicts one-quarter-ahead CDS spreads. Portfolio-level analysis confirms this result.