Research summary:This study proposes that CEOs may undertake intensive acquisition activities to increase their social recognition and status after witnessing their competitors' winning CEO awards. Using a sample of U.S. S&P 1,500 firm CEOs, we find that CEOs engage in more intensive acquisition activities in the period after their competitors won CEO awards (i.e., postaward period), compared to the preaward period. Moreover, this effect is stronger when focal CEOs themselves had a high likelihood of winning CEO awards. Our findings also show that acquisitions by focal CEO firms in the postaward period realize lower announcement returns compared to acquisitions by the same CEOs in the preaward period. Managerial summary:Each year a few CEOs receive CEO awards from business media and CEOs who receive such awards become instant celebrities, that is, superstar CEOs. This study explores how superstar CEOs' competitors react to not winning CEO awards. We find that superstar CEOs' competitors undertake more intensive acquisition activities in the postaward period compared to the preaward period. This is particularly true for competitors who were close, yet did not win CEO awards. In addition, acquisitions by superstar CEOs' competitors are associated with lower announcement returns in the postaward compared to the preaward period. These findings collectively indicate that acquisitions may be used as a channel for superstar CEOs' competitors to elevate their own social status, but at a cost to shareholders.

Abstract-This paper presents MARS, a novel multilevel full-chip gridless routing system. The multilevel framework with recursive coarsening and refinement allows for scaling of our gridless routing system to very large designs. The downward pass of recursive coarsening builds the representations of routing regions at different levels while the upward pass of iterative refinement allows a gradually improved solution. We introduced a number of efficient techniques in the multilevel routing scheme, including resource reservation, graph-based Steiner tree heuristic and history-based iterative refinement. We compared our multilevel framework with a recently published three-level routing flow [1]. Experimental results show that MARS helps to improve the completion rate by over 10%, and the runtime by 11 7 .Index Terms-Design automation, routing optimization methods, very large scale integration (VLSI).

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