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Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

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Analysts are recognized for their expertise in predicting industry growth, yet little is known about whether CEOs learn from analysts’ insights to guide investment decisions. Focusing on conglomerates where CEOs are underinformed about segment growth opportunities, we find that CEOs learn industry insights from analysts to adjust internal capital allocation. The extent of learning increases when analysts have closer proximity to CEOs or expertise in segments where CEOs face larger internal knowledge gaps. CEOs likely learn from analysts through private communications, as the insights learned are not yet publicly available, difficult to replace with other sources, and persistently impactful for firms. CEOs also exploit conference calls as another way to learn from analysts. As a result, learning analysts’ insights enhances firm value. We employ brokerage mergers/closures as a quasi-experiment to address endogeneity concerns. Overall, our study provides novel evidence on a learning channel through which analysts add value to firms.