THE FAMILY OWNERSHIP AND FIRM PERFORMANCE IN CHINA:Evidence from Shenzhen Stock Exchange

The family ownership structure is widespread at present and substantial family corporations exist now all over the world, especially in Asia where a strong sense of family exists. However, whether the family ownership structure can improve company performance is still controversy. To find out how family ownership management structure affects corporations in China is the main objective of this thesis. This thesis investigates why family companies perform different further. The analysis in this paper is conducted by selecting sample from Shenzhen Small and Medium Enterprise Board from 2009 to 2013. Both accounting measures and market measure are used to examine the company performance. In the empirical part, the correlation between family ownership and company performance is demonstrated. Besides, relations between characteristics of family enterprises and company performance are illustrated. The results imply that family ownership structures have positive influences in company governances in China. Family companies perform better than nonfamily companies, which is similar to most prior studies. Further analysis indicates that correlations in family CEOs and family company performances are negative. And family companies, with the multiple large shareholder structure, have worse performance than without it. These two results are opposite to previous empirical studies. However, the ratio of family holdings has no effects on the family company performance. In short, family ownership structure is an efficient management structure in China.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Credit Risk Management of P2P Network Lending

This article first studies the literature of P2P online loans, including online loans, credit risk factors and models, and summarizes the current status of P2P and credit risk assessment management in China. Based on the loan data of domestic P2P lending platforms, this paper conducts an empirical study on credit risk assessment. This study uses random forest importance assessment and logistic regression classification for credit risk assessment to identify loan targets with higher probability of default and improve overall loan quality. This research used 10,930 loan data, based on 26 fields, and finally selected 20 model variables to participate in credit risk quantification through feature structure and feature analysis. The final modelling test results show that the model screening accuracy rate is 73.3%, indicating that this model has a good performance in the credit risk quantification of borrowers