This study has two objectives: to examine the relationship between managerial sentiment and corporate investment and to examine the relationship between investment and firm value. We use a sample of Taiwanese firms and find that an optimal level of investment that maximizes a firm's value does exist and that it depends upon the quality of the investment opportunities. In addition, the empirical results show that when firms have valuable (nonvaluable) investment opportunities, managerial optimism (pessimism) makes overinvestment (underinvestment) more likely. Interestingly, the overinvestment (underinvestment) phenomenon for optimistic (pessimistic) managers differs significantly between valuable project and nonvaluable project firms.
The purpose of this article is to compare and evaluate statistical models for corner solution applications of censored regression. Generally, a double-hurdle model is better than one-part model. Cragg (1971) suggests a double-hurdle model, the truncated normal model, is widely used. However, the lognormal model is easy to have the economic interpretation than the Cragg's model. We use the simulation and empirical data to test both the models. The results show that the lognormal model is usually more robust than the truncated normal model. While the lognormal model has valuable application in censored data, its potential usefulness should not be overlooked.
The accuracy of the content should not be relied upon and primary sources of information should be considered for any verification. KKG Publications shall not be liable for any costs, expenses, proceedings, loss, actions, demands, damages, expenses and other liabilities directly or indirectly caused in connection with given content. This article may be utilized for research, edifying, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly verboten.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.