How signaling and search costs affect information asymmetry in P2P lending:the economics of big data

In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.

Engineering DNA Materials for Sustainable Data Storage Using a DNA Movable-Type System

DNA molecules are green materials with great potential for high-density and long-term data storage.However,the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards,limiting its practical applications.Here,we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing.In this system,these pre-generated DNA fragments,referred to herein as“DNA movable types,”are used as basic writing units in a repetitive way.The process of data writing is achieved by the rapid assembly of these DNA movable types,thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis.With this system,we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding,thereby demonstrating the feasibility of this system.Through its repetitive usage and biological assembly of DNA movable-type fragments,this system exhibits excellent potential for writing cost reduction,opening up a novel route toward an economical and sustainable digital data-storage technology.

China's Economic Growth:The“Two-Dimensional Driving Effect”of Data Factors

Data factors have become one of the five essential production factors,but their role in economic growth has always been ambiguous.Starting from AI technologies,this paper establishes an endogenous growth model of data factors affecting economic growth,constructs the generation path and value path of data factors,and estimates the value of new data factors at the provincial level in China from 1999 to 2018 accordingly.Based on theoretical analyses and empirical tests,it clarifes that data factors have a“two-dimensional driving effect”on China's economic growth,that is,data factors can drive growth both directly through its own economic growth effect and indirectly by promoting technological progress.Furthermore,this paper makes three extended discussions,aiming to make a trial study on the impacts of local government big data transaction platforms on data factors and their growth effects,discuss whether it is possible to reduce the uncertainties of local economic policy based on the nature of data factors,and make a preliminary survey of the output elasticity of data factors between 1999 and 2018.

FUZZY GENETIC HYBRID ALGORITHM FOR MARKET TIMING PREDICTION OF STOCK INVESTMENT

Market timing prediction of stock investment is an important decision problem with uncertainty and risk in the financial activity.An algorithm for market timing prediction of stock investment is proposed in this paper.Considering the close relationship in the stock market and the economic data,we find the correlation of synthetical economic data and the equity returns with the help of the combination of fuzzy logic and genetic algorithm.Finally,the application of stock market is included to test the effectiveness of the algorithm.