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The significance of integrating reliability into logistics performance has been established [The Logistics Performance Index and Its Indicators, World Bank International Trade and Transport Departments, (2010)]. Hence, as a response to the work by the World Bank, the present article aims to evaluate the performance index Rb,d of logistics systems as the probability that a specified demand d can be distributed successfully through multistate arc capacities from the source to the destination under the constraint that the total distribution cost should not exceed the cost limitation b. This article provides a pioneering approach for a straightforward computation of the performance index Rb,d. The proposed algorithm is a hybrid between the polynomial time capacity‐scaling algorithm, which was presented by Edmonds and Karp [JACM 19 (1972)], and the decomposition algorithm, which was presented by Jane and Laih [IEEE (2008)]. Currently, the proposed approach is the only algorithm that can directly compute Rb,d. An illustration of the proposed algorithm is presented. The results of the computational experiments indicate that the presented algorithm outperforms existing algorithms. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

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This study examines herding behavior in six Asia-Pacific stock markets, namely, Taiwan, China, South Korea, Singapore, Hong Kong, and Japan, during a period of turmoil, particularly the latest subprime mortgage crisis. We find no evidence of herding in the stock markets of Singapore and Hong Kong, and only partial evidence of herding in the stock markets of South Korea and Japan. However, we document significant evidence of herding in the stock markets of Taiwan and China. This result suggests that herding is more pronounced in developing nations, where markets are dominated by individual investors and where daily price limits are observed. Furthermore, we empirically verify and visually corroborate the comparative co-movement behavior between markets with respect to the "home bias reflection hypothesis" and the degree of market openness. Finally, we find apparent herding behavior in a rising market state and during post-crisis period.

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This paper investigates the use of the representativeness heuristic and herding recommendation by experts in Taiwanese investment magazines. We reveal that high-performing experts exhibit less use of representativeness heuristics and more use of herding stock recommendations than poor performers do. Representativeness and herding recommendations increase when sentiments are pessimistic and during bull markets. In general, more representative stocks yield lower abnormal returns and higher return comovement. However, for high-performing experts, recommendations with some degree of representativeness have a higher abnormal return than do recommendations with no representativeness. In addition, stock return comovement caused by herding recommendations catches more market-level news than does stock return comovement that is caused by representativeness.