• Does capital market drive corporate investment efficiency? Evidence from equity lending supply

      Tsai, H.-J.; Wu, Yuliang; Xu, B. (2021-08)
      The increased equity lending supply (ELS) in the equity loan market, available for short sellers to borrow, exposes a firm to greater short selling threats. Considering short sellers’ strong incentives to uncover firm-specific information and monitor managers, we hypothesize that short selling threats, proxied by ELS, enhance corporate investment efficiency. We find that ELS significantly reduces managerial tendencies to underinvest (overinvest) especially for firms prone to underinvest (overinvest). The effect of ELS on investment efficiency is stronger for firms with higher information asymmetry and weaker corporate governance, confirming short sellers’ role in mitigating information and agency costs. However, short selling risk weakens the effect of ELS. Our evidence is robust to endogeneity checks and suggests that corporate investment can be driven by a particular capital market condition: the amount of lendable shares in the equity loan market.
    • Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework

      Mousavi, Mohammad M.; Quenniche, J.; Xu, B. (2015-12)
      Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.