This paper examines the returns of asset acquirers when sellers have different statuses. We find that private sellers create lower returns for acquirers and receive higher premiums than public sellers. Both private equity and private operating sellers generate lower returns for acquirers than public sellers, but their relative gain differences are not significantly different. In addition, the gain difference cannot be explained by acquirer characteristics, sample selection effects, or means of payments, but it increases with sellers’ director ownership. We examine alternative theories to explain our results. While we do not find supportive evidence for the synergy creation and information symmetry hypothesis, we find ample evidence for the manager discretion hypothesis.

We develop a model of many-to-one matching markets in which agents with multiunit demand aim to maximize a cardinal linear objective subject to multidimensional knapsack constraints. The choice functions of agents with multiunit demand are therefore not substitutable. As a result, pairwise stable matchings may not exist and even when they do, may be highly inefficient. We provide an algorithm that finds a group-stable matching that approximately satisfies all the multidimensional knapsack constraints. The novel ingredient in our algorithm is a combination of matching with contracts and Scarf’s Lemma. We show that the degree of the constraint violation under our algorithm is proportional to the sparsity of the constraint matrix. The algorithm, therefore, provides practical constraint violation bounds for applications in contexts, such as refugee resettlement, day care allocation, and college admissions with diversity requirements. Simulations using refugee resettlement data show that our approach produces outcomes that are not only more stable, but also more efficient than the outcomes of the Deferred Acceptance algorithm. Moreover, simulations suggest that in practice, constraint violations under our algorithm would be even smaller than the theoretical bounds. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.