我国企业高管股权激励与研发投资——基于内生性视角的研究

本文以2002—2009年我国上市公司为研究对象,探讨了股权分置改革前后我国企业管理层股权激励对研发投资的影响,并在此基础上检验了管理层股权激励的内生性。研究发现,高管股权激励存在内生性,在控制了内生性之后,股改前股权激励与研发投资之间存在倒U形曲线关系;股改后股权激励对研发投资具有显著的正向影响。本文结果表明应该将研发投资作为股权激励方案的激励条件之一;此外,合理的安排股东与管理层之间的股权配置比例是保证企业有效进行研发投资、提升企业自主创新能力的必要手段。本文的结论深化了我们对股权激励内生性的理解,并为我国企业更好地实施管理层股权激励和企业自主创新战略提供了理论支持和实证证据。     

Stock market openness and analyst forecast bias

Biases in analysts’ forecasts can be reduced not only through regulation but also through market mechanisms. In 2014, China launched the Shanghai-Hong Kong Connect program, which opened part of its domestic equity market to foreign investors. The implementation of this program provides a quasi-natural experimental setting to explore whether stock market openness plays a governance role in brokerage firms and minimizes their affiliated analysts' forecast biases. We find that the participation of foreign institutional investors mitigates the forecast biases of affiliated analysts. We also show that these analysts exert more significant effort by conducting more site visits. Our findings suggest that market liberalization can help improving the quality of analysts’ forecasts.     

Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study

In finance, the use of newspaper-based uncertainty measures has grown exponentially in recent years. For instance, a growing number of researchers have used the newspaper-based U.S. economic policy uncertainty (EPU) index suggested in Baker et al. (2016) as a predictor in their model to forecast the variable of interest out-of-sample. Likewise, inspired by the approach suggested in Baker et al. (2016), several other newspaper-based uncertainty measures have been introduced, such as indices measuring geopolitical risk (GPR) and monetary policy uncertainty (MPU). This study evaluates the relative out-of-sample predictive power afforded by more than fifty different newspaper-based uncertainty measures with regards to predicting excess returns on the S&P 500 index one-month ahead using data from 1985$m$1 through 2020$m$12. Our predictive model accounts for salient data features, namely, predictor endogeneity and persistence. Furthermore, we evaluate the evidence of conditional as well unconditional predictive ability as outlined in Giacomini and White (2006), and also explore whether any identified level of gains from a statistical viewpoint lead to gains from an economic viewpoint. We find that newspaper-based uncertainty measures linked with certain components of the equity market volatility (EMV) tracker suggested in Baker et al. (2019) help improve the accuracy of one month ahead point predictions relative to the benchmark the most. In contrast, EPU, GPR and MPU indices, which are more frequently used by researchers are much less successful.