上市公司临时报告对资本市场信息传递的影响

以中国资本市场上2000-2009年的上市公司为研究样本, 文章探讨了上市公司临时报告对资本市场信息环境的影响, 发现了临时公告对资本市场信息环境的积极提升作用. 实证结果表明, 上市公司临时报告提高了股票价格中的公司 个体层面的信息含量, 具体表现为: 临时报告降低了股票价格的同步性, 增加了收益率的异质波动性, 增加了信息交易的概率. 以是否具有研发行为、外部分析师数量的多少、所处地区市场化发展程度的大小,作为公司所处信息环境的代理变量, 文章发现在信息不对称程度较高的上市公司中, 临时报告提高股票价格信 息含量的作用更加明显. 实证检验还发现, 临时报告使得上市公司信息传递更加平稳, 降低了股票价格暴跌的风险; 临时报告提高了盈余信息反应到股价中的速度和程度, 提高了盈余反应系数, 降低了盈余公告后的漂移程度.     

员工股权激励能够降低股价崩盘风险吗?

股权激励是上市公司重要的治理机制，高管和员工构成股权激励的两大激励对象.然而，现有研究多关注高管股权激励的治理作用，却普遍忽略了非高管层员工.利用2007-2017年的A股上市公司数据，从股价崩盘风险的视角，考察了员工股权激励的治理作用.研究发现，员工股权激励能够显著降低股价崩盘风险.作用机制分析表明，员工股权激励可通过改善公司信息环境、降低公司经营风险两种途径降低股价崩盘风险.进一步分析还发现，产权性质并不影响员工股权激励对股价崩盘风险的降低作用.文章发现的员工股权激励对股价崩盘风险的治理作用，能够为规范上市公司信息披露、维护股票市场稳定等相关政策的完善提供参考.     

股价信息含量对企业资源配置效率影响实证研究

以中国上市公司为研究样本,采用股价波动非同步性方法度量股价信息含量,并基于面板数据回归模型对股价信息含量与资源配置效率的因果关系进行了实证检验.实证结果表明:股价信息含量与企业的投资过度行为呈显著负相关,即股票价格中包含的特质信息能起到抑制企业投资过度行为,增加资源配置效率的作用;高信息含量股价也能显著提升企业未来期间的经营绩效水平.因此上市公司股价信息含量增加能对资源优化配置产生正向促进作用.     

Choosing the Number of Conditioning Events in Judgemental Forecasting

One method for judgemental forecasting involves the use of decomposition; i.e. estimating the conditional means of an unknown quantity of interest for a finite number of conditioning events, and weighting these estimated conditional means by the estimated marginal probabilities of the corresponding conditioning events. In this paper we investigate how the level of decomposition (i.e. the number of conditioning events) affects the precision of the resulting forecast. Previous analyses assume that key parameters (the informativeness of the decomposition, and the precision of estimation for the conditional means and the marginal probabilities) remain constant as the number of conditioning events increases. However, this assumption is unreasonable, and for some parameters mathematically impossible; the values of these parameters are likely to change significantly even for small numbers of conditioning events. Therefore, we introduce models for how these key parameters may depend on the level of decomposition. We then investigate the implications of these models for the precision of the resulting forecast. In particular, we identify cases in which decomposition is never desirable, always desirable, or desirable only near the optimal number of conditioning events. This second case was not observed previously. We focus throughout on the situation likely to be of greatest interest in practice; namely, the behaviour of decomposition for relatively small numbers of conditioning events.© 1997 John Wiley & Sons, Ltd.