Going Overboard? On Busy Directors and Firm Value

Abstract The literature disagrees on the link between so-called busy boards (where many independent directors hold multiple board seats) and firm performance. Some argue that busyness certifies a director’s ability and that such directors are value enhancing. Others argue that “over-boarded” directors are ineffective and detract from firm value. We find evidence that (1) the disparate results in prior work stem from differences in both sample composition and empirical design, (2) on balance the results suggest a negative association between board busyness and firm performance, and (3) the inclusion of firm fixed effects dramatically affects the conclusions drawn from, and the explanatory power of, multivariate analyses. We also explore alternative empirical definitions of what constitutes a busy director and find that commonly used proxies for busyness perform well relative to more complex alternatives. Highlights ► The disparate busy director findings result from different samples and methodology. ► Including firm fixed effects results in a constant negative relation. ► The common busy director definition is as informative as more intense alternatives

Geographical Coarsegraining of Complex Networks

We perform the renormalization-group-like numerical analysis of geographically embedded complex networks on the two-dimensional square lattice. At each step of coarsegraining procedure, the four vertices on each $2 \times 2$ square box are merged to a single vertex, resulting in the coarsegrained system of the smaller sizes. Repetition of the process leads to the observation that the coarsegraining procedure does not alter the qualitative characteristics of the original scale-free network, which opens the possibility of subtracting a smaller network from the original network without destroying the important structural properties. The implication of the result is also suggested in the context of the recent study of the human brain functional network.Comment: To appear in Phys. Rev. Let

An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Big Survival Data

The Cox model, which remains as the first choice in analyzing time-to-event data even for large datasets, relies on the proportional hazards assumption. When the data size exceeds the computer memory, the standard statistics for testing the proportional hazards assumption can no longer b e easily calculated. We propose an online up dating approach with minimal storage requirement that up dates the standard test statistic as each new block of data becomes available. Under the null hypothesis of proportional hazards, the proposed statistic is shown to have the same asymptotic distribution as the standard version if it could be computed with a super computer. In simulation studies, the test and its variant based on most recent data blocks maintain their sizes when the proportional hazards assumption holds and have substantial power to detect different violations of the proportional hazards assumption. The approach is illustrated with the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program. The proposed test promptly identified deviation from the proportional hazards assumption that was not captured by the test based on the entire data