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191.
This paper investigates how large family shareholders and institutional block-holders jointly influence informed trading and firm valuation in the Hong Kong stock market. It combines market microstructure research with studies on the governance roles of multiple block-holders and finds that institutional block-holders rely on their relative controlling power vis-à-vis family owners to mitigate problems associated with informed trading. They also use their ownership rights to improve the structure of informed trading. However, these governance roles are predominantly exercised by pressure-resistant institutional block-holders. Informed trading reduces firm valuation, while an improvement in its structure increases valuation. Therefore, the governance roles of controlling families and pressure-resistant institutional block-holders may have different implications in terms of investors’ perceptions of private information risk. 相似文献
192.
This paper examines the development of effective boards in venture capital (VC)-backed initial public offerings. It argues
that VC-backed IPOs suffer from two sets of agency costs which are related to principal–agent and principal–principal relationships
between the founders and members of the VC syndicate. Using a unique sample of 293 entrepreneurial IPOs in the UK it shows
that VC syndicates invest in relatively more risky firms. VC-backed IPOs have more independent boards than IPOs with no VC
involvement, with board independence being higher in syndicated VC-backed firms. These results are consistent with assumption
that these governance factors are used to mitigate agency costs associated with VC involvement in IPO firms. We also find
that in syndicated IPOs there is a higher equity presence of passive private equity firms investing alongside VC firms. 相似文献
193.
As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets. 相似文献