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Venture Capital in China : = Portfolio Firm Performance, Investment Efficiency, and Partner Selection.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Venture Capital in China :/
其他題名:	Portfolio Firm Performance, Investment Efficiency, and Partner Selection.
作者:	Ji, Qiong.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:	Dissertations Abstracts International84-09B.
標題:	Epistemology. -
電子資源:	click for full text (PQDT)
ISBN:	9798374478297

Venture Capital in China : = Portfolio Firm Performance, Investment Efficiency, and Partner Selection.
Ji, Qiong.

Venture Capital in China :Portfolio Firm Performance, Investment Efficiency, and Partner Selection. - 1 online resource (164 pages)

Source: Dissertations Abstracts International, Volume: 84-09, Section: B.

Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2019.

Includes bibliographical references

This thesis adds some pieces to the puzzle by deepening our understanding of the relationship among Venture Capital firms, portfolio firms, and their syndicated partners. The thesis comprises three papers that investigate roles of Venture Capital experience in enhancing portfolio firm performance and investment efficiency, as well as the factors that influencing syndicated partner selection in Mainland China.The first paper (Chapter 3) studies how does Venture Capital experience influence post-IPO performance and growth of portfolio firms. The results show that Venture Capital experience has significant positive effects on long-run performance measures, even after controlling for Venture Capital selectivity. In addition, results show that these firms have significantly higher probability of allocating ownership to senior executives than nonVenture Capital-backed firms and are able to better align the interests of the executives and the shareholders. Overall, the study confirms the positive role of institutional investors in favour of portfolio firm performance and governance outcomes.The second paper (Chapter 4) examines the direct and indirect link between Venture Capital and investment efficiency. The results provide strong and robust evidence that Venture Capital enhances portfolio firms' investment efficiency. Further tests show that the impact of Venture Capital ownership on corporate investment does not vary with portfolio firm's financial constraints nor with the local financial market development. This result is in line with the monitoring hypothesis and confirms that institutional investor play a significant role in improving corporate value. Finally, our results show that information asymmetry plays a mediating role on Venture Capital and investment efficiency, meaning that Venture Capital enhances portfolio firm efficiency via the indirect effect of information asymmetry on investment inefficiency, as well as directly reducing under-investment.The third paper (Chapter 5) uses Neural Network method to analyse factors impacting partnering decisions in Venture Capital syndication. This is the first time that machine learning technique is applied to build a prediction model of partnering decisions in Venture Capital studies. The results show that the approach successfully predicts partner selection decision with a prediction accuracy of 99.32%. By using rare events logistic regression, we find that partner VCs' extra industry experience is a valuable signal to infer its ability. Further, the combination of extra industry experience and joint history or reciprocation strongly increase the probability of cooperation. Moreover, when the portfolio firms are split into different stages, it is found that the signal strength is higher in later stages than in earlier stages. Last, signal matters more if the partner is not backed by the government.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798374478297Subjects--Topical Terms:

671538
Epistemology.
Index Terms--Genre/Form:

554714
Electronic books.

Venture Capital in China : = Portfolio Firm Performance, Investment Efficiency, and Partner Selection.
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500 $a Advisor: Ding, Xiaoming.
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504 $a Includes bibliographical references
520 $a This thesis adds some pieces to the puzzle by deepening our understanding of the relationship among Venture Capital firms, portfolio firms, and their syndicated partners. The thesis comprises three papers that investigate roles of Venture Capital experience in enhancing portfolio firm performance and investment efficiency, as well as the factors that influencing syndicated partner selection in Mainland China.The first paper (Chapter 3) studies how does Venture Capital experience influence post-IPO performance and growth of portfolio firms. The results show that Venture Capital experience has significant positive effects on long-run performance measures, even after controlling for Venture Capital selectivity. In addition, results show that these firms have significantly higher probability of allocating ownership to senior executives than nonVenture Capital-backed firms and are able to better align the interests of the executives and the shareholders. Overall, the study confirms the positive role of institutional investors in favour of portfolio firm performance and governance outcomes.The second paper (Chapter 4) examines the direct and indirect link between Venture Capital and investment efficiency. The results provide strong and robust evidence that Venture Capital enhances portfolio firms' investment efficiency. Further tests show that the impact of Venture Capital ownership on corporate investment does not vary with portfolio firm's financial constraints nor with the local financial market development. This result is in line with the monitoring hypothesis and confirms that institutional investor play a significant role in improving corporate value. Finally, our results show that information asymmetry plays a mediating role on Venture Capital and investment efficiency, meaning that Venture Capital enhances portfolio firm efficiency via the indirect effect of information asymmetry on investment inefficiency, as well as directly reducing under-investment.The third paper (Chapter 5) uses Neural Network method to analyse factors impacting partnering decisions in Venture Capital syndication. This is the first time that machine learning technique is applied to build a prediction model of partnering decisions in Venture Capital studies. The results show that the approach successfully predicts partner selection decision with a prediction accuracy of 99.32%. By using rare events logistic regression, we find that partner VCs' extra industry experience is a valuable signal to infer its ability. Further, the combination of extra industry experience and joint history or reciprocation strongly increase the probability of cooperation. Moreover, when the portfolio firms are split into different stages, it is found that the signal strength is higher in later stages than in earlier stages. Last, signal matters more if the partner is not backed by the government.
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