I introduced the role of text information in P2P lending in previous post. Human can express their opinion and purpose by complex tone. The punctuation usually represents the full logic of human thinking in written language, which reflects people’s education level. Written Chinese words are strung together one after another. There are no gaps or spaces indicating breaks between words. We can find it natural to pick out words in the Chinese sentence despite this, but different combination of words can be various of meaning. A recent published paper studies the role of punctuation in P2P lending. Chen et al. (2017) employed data from Renrendai.com, a leading P2P platform in China. Continue reading
Beginning from the first P2P platform in 2007, Chinese P2P lending market has become the largest in the world. Up to now, the industry has been experiencing big changes for regulation. How does policy change impact on this nascent market in China? Continue reading
I reviewed a large number of papers which focus on the borrower’s side in P2P lending. Few papers discuss lender’s choice and his/her welfare. A key challenge for personal investors in P2P lending marketplaces is the effective allocation of their money across different loans by accurately assessing the credit risk of each loan. Thus, how lenders screen borrowers’ profile and make portfolios to maximise their utility? Continue reading
Natural language is a huge source of data about complex phenomena, but it is difficult to qualify and measure. With development of computer science, the method of textual analysis enables the researcher to include large amounts of textual information and systematically identify its properties, such as the frequencies of most used keywords by locating the more important structures of its communication content. The measures contain readability, similarity, sentiment and so on. Continue reading
We discussed few papers focused on US Market, and the main data were Prosper and Lendingclub. We introduce a recent paper regarding Chinese P2P market place. Beginning from the first P2P platform in 2007, the Chinese P2P lending market has developed rapidly. Unlike US market, there is no official credit information related to an individual borrower provided through any agency. As a result, it is difficult for lenders to obtain comprehensive information about borrowers, resulting in a severe hazard of information asymmetry. Continue reading
Think about if you are a peer lender who wants to invest money in a P2P platform, what portfolios you could choose? How to spread the risk from various loans with different credit level? Whether wealth lenders have better knowledge to hedge the risk? Continue reading
Juanjuan and Zhang,Peng Liu proposed a study regarding herding bahaviour in P2P market by using data from Prosper.com and found evidence regarding lenders’ herding behaviour. Well-funded borrower listings tend to attract more funding, lenders engage in active observational learning (rational herding) and follow-up analysis shows that rational herding beats irrational herding in predicting loan performance.
Zaiyan Wei and Mingfeng Lin discovered market mechanisms of which business model is better for both lenders and borrowers by using data from Prosper.com. The main finding is that under platform-mandated posted prices, loans are funded with higher probability, but the pre-set interest rates are higher than borrowers’ starting interest rates and contract interest rates in auctions, and loans funded under posted prices are more likely to default. Continue reading
Sarah Miller proposed a research which provides such evidence by exploiting an unanticipated change in the amount of information visible in Prosper.com for loans to measure the impact of lender information on loan outcomes. The main finding is that accessing more information of borrowers improved the screening performed by exsisting lenders and attracted new lenders who were better at screening loan applicants and earned higher returns.