This study explores the interplay between public measures adopted by the U.S. government to combat COVID-19 and the performance of the American hospitality industry. The recent global pandemic is a natural experiment for exploring the role of government interventions and their direct impact on hospitality stock returns in the U.S. financial market. Overall, our findings show that most of the government interventions were associated with a negative response in the returns of the hospitality industry, a response that became more negative as the COVID-19 pandemic evolved. Similar patterns were also detected for other industries such as entertainment and transportation that are closely related to hospitality. The findings we document are fundamental to understanding the trends and fluctuations in hospitality stocks in the current crisis and any similar crisis in the future.

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A vast body of academic literature deals with banks’ optimal loan allocations. The general approach to solving this problem is to assume borrowers’ portfolios as given. Although this assumption is reasonable in the corporate sector, the situation differs radically in the mortgage markets, where borrowers are unobservable and banks’ screening capacity is tightly limited. We propose a novel dynamic model that assumes potential mortgage takers arrive randomly and sequentially at a bank. In a simulation, we show that the effect of a more stringent level of perceived risk on a bank’s expected net income can be positive or negative. Remarkably, if both level of wealth inequality and screening capacity are low, a more severe level of perceived risk can decrease a bank’s expected net income. In this situation, regulators should be particularly careful about increasing regulation in the form of a lower loan-to-value ratio.

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Following the information revolution, enormous numbers of companies and individuals transfer by messages via social networks and/or in companies' communication networks.These messages, however, are subject to modifications as they spread through the network. This study calculates the distortion of a message as it spreads in a social network, and compares a global measurement of the distortion in random, scale free and small world networks. We create a mathematical model to describe the distortion process, and we tested it using a simulation of different types of networks. We show that scale-free networks are the least sensitive to distortion of information compared to random or small world networks.

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