This paper develops a stochastic learning model in which agents choose a limited number of partners to play size-dependent minimum-effort games. The payoff for each agent in this interaction depends on the minimum of the efforts of her partners and herself, and is increasing with the size of her...

This paper develops a model in which agents choose a limited number of partners to play a coordination game. We introduce a hybrid revision protocol, in which an agent may revise only her action, only her links or both her action and links. We show that risk-dominant strategy profiles will be...

Reasonable layout of logistics centers in the ASEAN countries has important practical significance for improving logistics network efficiency and reducing logistics transportation costs. This paper based on the current status of the ASEAN logistics network and the characteristics of the...

We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance estimators and verify their reduced bias and mean...

Gagnon and Goyal (2017) develop an elegant model to understand the interaction between community and markets. One key argument is that, among others, whether markets and social ties are substitutes or complements plays a decisive role: markets undermine social ties in the case of substitutes and...

We examine how political corruption affects firms' accounting choices. We hypothesize and find that firms headquartered in corrupt districts manipulate earnings downwards, relative to firms headquartered elsewhere. Our finding is robust to alternative corruption measures, restatement-based...

We investigate how language similarity between acquirers and targets affects post-M&A performance using a large sample of M&A transactions in China. We hypothesize and find that acquirers achieve higher post-M&A operating performance when they acquire targets that share the same native...