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The purpose of this study is to investigate (1) how managers respond to stock price crashes, (2) why they respond and (3) how their responses affect shareholders.

This study employs a panel regression with various firm-level controls and firm- and year-fixed effects. The sample is comprised of 101,532 firm-year observations with 11,727 unique firms from 1950 to 2019. Using mutual fund flow redemption pressure as an exogenous variable to stock price crashes, the paper provides further evidence of the causality of documented findings.

Management becomes more focused on improving transparency, raising investment efficiency, reducing agency conflicts and regaining the trust of shareholders by investing in social capital and employee welfare. These actions increase firm value. This study also suggests that management undertakes these actions out of concern for their tenure of employment.

The catalysts of stock price crashes are well documented, but much less is known about what happens following stock price crashes. This study provides more insights into the understanding of corporate crisis management practices following adverse events.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.