Yanbin Tu

Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending

Online Peer-to-Peer (P2P) lending has emerged recently. This micro loan market could offer certain benefits to both borrowers and lenders. Using data from the Lending Club, which is one of the popular online P2P lending houses, this article explores the P2P loan characteristics, evaluates their credit risk and measures loan performances. We find that credit grade, debt-to-income ratio, FICO score and revolving line utilization play an important role in loan defaults. Loans with lower credit grade and longer duration are associated with high mortality rate. The result is consistent with the Cox Proportional Hazard test which suggests that the hazard rate or the likelihood of the loan default increases with the credit risk of the borrowers. Finally, we find that higher interest rates charged on the high-risk borrowers are not enough to compensate for higher probability of the loan default. The Lending Club must find ways to attract high FICO score and high-income borrowers in order to sustain their businesses.

Seller heterogeneity in electronic marketplaces

Most online auction studies focus on the buyer side of the marketplace. To bridge the imbalance gap, we investigate seller behavior in this research. Specifically emphasized on seller heterogeneity, an area that has not been well studied in the electronic marketplace literature, this study classifies sellers into new and experienced seller groups and compares their auction performance based on three outcome measures: success rates, ending prices, and feedback ratings. We find that the performance of new sellers is worse than that of experienced sellers in all three measures, namely, lower auction success rates, lower auction ending prices, less likely to receive positive feedback ratings and more likely to receive negative feedback ratings. We also show the dynamics of these auction performances while new sellers evolve into experienced ones. Our empirical analysis reveals structural differences in auction success and price determinants between new and experienced sellers. That is, the determining factors for auction success and ending prices are significantly different between the two seller groups. We also identify several key factors for non-positive (i.e. neutral or negative) feedback ratings received by the two groups and find no significant difference between these factors for both new and experienced sellers. We believe our findings in this study have significant implications to the online auction house and sellers, and contribute towards building more effective and efficient electronic marketplaces. We studied seller heterogeneity in electronic marketplaces.We discovered new sellers worse than experienced sellers in three outcome measures.We demonstrated auction performance dynamics when sellers evolve with experiences.

Cross-Border E-Commerce: A New Driver of Global Trade

In this chapter, we explore cross-border e-commerce (CBEC) as a new driver of international trade. We adopt a revised i-based N-OLI framework for CBEC. Using China as a case study, we discuss the rapid growth, structure, export/import models, and infrastructure and environment of China’s CBEC. We attribute China’s CBEC success to the important role of e-commerce giants such as Alibaba.com and JD.com, the designation of CBEC pilot cities, supportive governmental policies, and big capital inflows. Four factors are seen to create future opportunities for all stakeholders of global CBEC, especially for merchants: the push from governments, the rise of middle class in developing countries, technological improvements, and more SMEs adopting CBEC. On the other hand, cultural differences, customer trust, logistics, payment, and legal and regulatory barriers are among the biggest challenges facing CBEC. Lastly, we outline several recommendations for foreign firms planning on entering into China’s market by means of CBEC.

After Auction's Complete: What Will Buyers Do Next? - A Case Study of Feedback Rating at eBay

Thiscasestudyaimstoinvestigatebuyers’post-purchasebehavioronfeedbackratings.Fromthedata collectedfromeBay,thestatisticalanalysisshowsthattheaveragetimelengththatbuyersposttheir feedbackafterauctionscompletionis15.5days.Newsellersandexperiencedsellershavedifferent chancestoreceivefeedback.Newsellersaremorelikelytoreceivenegativefeedbackoverpositive feedback.Thedistributionofthefeedbacktypes(negative,neutralandpositive)doesnotmatchthat oftheirassociatedmonetaryvolumes.ThiscasestudyalsodemonstratesthatinexperiencedeBay buyersaremorelikelytopostnegativefeedbackratingsthanexperiencedones.Newandusedproducts attractdifferentratingsinthethreefeedbacktypes.Withwordcloudandwordfrequencyanalysis, theauthorsidentifycommonissuesassociatedwitheachofthethreetypesoffeedback.Thepaper alsodiscussesthemanagerialimplicationsandrecommendationsbasedonthesefindings. KEyWoRDS eBay, e-Marketplace, Feedback Rating, Online Auction, Post-Purchase Behavior

Dynamic transaction environment and market efficiency of electronic marketplaces

The objective of this paper is to model the online dynamic transaction environment, analyse the behaviour of buyers and sellers, and explore how online dynamic features affect market efficiency in electronic marketplaces. We find that under certain conditions the online dynamic environment can help increase the transaction probability, average used product quality level, market size, and total social welfare in electronic marketplaces. Using eBays auction data, we empirically test the efficiency of electronic markets by comparing the auction process, outcome, and post-purchase evaluation between new products and used products. Our empirical findings suggest that the electronic marketplaces for used IT products such as Nintendo game consoles and TI calculators are as efficient as the new products. We contribute to the literature by demonstrating the dynamic electronic marketplace environment can improve market efficiency, and show empirical measurement results of market efficiency for electronic marketplaces.