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.
Purpose
Recent studies in the accounting literature have investigated the economic consequences of R&D capitalization. Discretionary R&D capitalization for target beating can be characterized as a firm signaling private information on its future economic benefits or as opportunistic earnings management. R&D capitalization also has an impact on a firm’s marginal costs and product market competition. The purpose of this paper is to address how firms choose R&D levels for the purpose of meeting or beating their earnings targets and how this influences sequential product market competition.
Design/methodology/approach
The authors study this issue in a stylized game-theoretic model where R&D choices of a firm are not only strategically made but also used to convey proprietary information to its rival. The model provides a rationale for a firm distorting its R&D level to earn more profits and meet its earnings target.
Findings
The equilibrium result indicates that before the realization of common cost shock, a firm can influence the output of its accounting system (i.e. meeting an earnings target) through adjusting its R&D choices. This firm will overinvest in R&D, and this will give an opportunity to create some reserves to be used later to earn a higher profit and reach the earnings target.
Originality/value
This paper contributes to the research on real earnings management in terms of how R&D capitalization affects a firm’s R&D choices by influencing the output of its accounting system through adjusting its R&D choices and the strategic impact of those choices.
Total-factor energy efficiency (TFEE) is widely used to measure energy efficiency under the data envelopment analysis (DEA) framework, but the efficiencies obtained from DEA are structurally biased upward, and thus TFEE tends to overestimate energy efficiency. This research thus applies the bootstrapped DEA approach to correct the bias of TFEE. Using a dataset consisting of 30 provinces of China in the period 2016–2019, the bootstrapped-based test supports technology with variable returns to scale. The biased-corrected TFEE also indicates that energy consumption on average can be scaled down by 42.36%, rather than the biased value of 19.4%. The bootstrapped clustering partitions provinces into three groups: Cluster 1, with Guizhou as the representative medoid, includes half of the superior coastal provinces in terms of actual energy consumption and TFEE and half of the competitive inland provinces, whereas Cluster 3 outperforms Cluster 2 in terms of TFEE, but the actual energy consumption is higher, with Shandong and Hebei as the representative medoids, respectively. Lastly, empirical results imply that the northeast and central regions need more government attention and resources to practice sustainable development and improve TFEE.
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.