Peer-to-peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision-making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real-world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations.

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The long-standing severe power shortage in China has provoked much debate on whether China should further promote market-oriented electricity reform. The present paper addresses this issue by analyzing the impacts of deregulation of the electricity generation sector and retailing activities on other sectors, the macroeconomy and electricity users. A counterfactual scenario analysis is used based on a simplified computable general equilibrium framework.We find that deregulation can significantly improve the efficiency of electricity production, increase employment and enhance household welfare. These nontrivial findings can help to resolve many controversies about governmental intervention during China's economic transition. Our findings have two implications relating to policy feasibility and applicability; that is, competition in the electricity retail market should be phased in, and the necessary arrangements for unemployment in incumbent firms should be considered.