Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration,which makes it easy for one party of the transaction to obtain a high trust v...Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration,which makes it easy for one party of the transaction to obtain a high trust value in a short time,and brings many disadvantages,uncertainties and even attacks.To solve this problem,a transaction frequency based trust is proposed in this study.The proposed method is composed of two parts.The first part is built on the classic Bayes analysis based trust modelswhich are ease of computing for the E-commerce system.The second part is the transaction frequency module which can mitigate the potential insecurity caused by one participating entity gaining trust in a short time.Simulations show that the proposed method can effectively mitigate the self-promoting attacks so as to maintain the function of E-commerce system.展开更多
The promotion of both market fairness and efficiency has long been a goal of securities market regulators worldwide.Accelerated digital disruption and abusive trading behaviors,such as the GameStop mania,prompt regula...The promotion of both market fairness and efficiency has long been a goal of securities market regulators worldwide.Accelerated digital disruption and abusive trading behaviors,such as the GameStop mania,prompt regulatory changes.It is unclear how this“democratization”of trading power affects market fairness as economies cope with pandemic-driven shifts in basic systems.Excessive speculation and market manipulation undermine the quality of financial markets in the sense that they cause volatil-ity and increase the pain of bubble and crash events.Thereby,they weaken public confidence in financial markets to fulfill their roles in proper capital allocation to irrigate the real economy and generate value for society.While previous studies have mostly focused on market efficiency,our study proposes a tool to improve market fairness,even under periods of stress.To encourage value generation and improve market quality,we advance a graduated Non-Value-Added Tax that we implement in an agent-based model that can realistically capture the properties of real-world financial markets.A profitable transaction is taxed at a higher rate if it does not enhance the efficiency measured by deviation from fundamentals.When an agent locks in profit not supported by fundamentals but driven by trend-following strategies,the generated profit is taxed at various rates under the Non-Value-Added Tax regime.Unlike existing financial transaction taxes,the non-value-added tax is levied on profit rather than on price or volume.We show that the proposed tax encourages profitable trades that add value to the market and discourages valueless profit-making.It significantly curtails volatility and prevents the occurrence of extreme market events,such as bubbles and crashes.展开更多
针对通过零售交易数据进行客户分群时传统方法未考虑商品的价值问题,提出用RFM(recency frequency monetary)表达交易数据的方法,该方法将客户购买的商品和商品类别组成一棵RFM购买树(recency frequency monetary purchase tree,RFMPT)...针对通过零售交易数据进行客户分群时传统方法未考虑商品的价值问题,提出用RFM(recency frequency monetary)表达交易数据的方法,该方法将客户购买的商品和商品类别组成一棵RFM购买树(recency frequency monetary purchase tree,RFMPT).提出基于RFM购买树的快速聚类算法(based recency frequency monetary purchase tree clustering,BRFMPTC),把购买树构建为Cover Tree(CT)索引结构,利用CT结构快速选择k个密度最大的购买树作为中心,将其他对象划分到距它最近的类中心.实验结果表明,在距离加权下,BRFMPTC算法较传统算法在整体上能产生质量更高的聚类结果,性能得到较大提升.展开更多
文摘Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration,which makes it easy for one party of the transaction to obtain a high trust value in a short time,and brings many disadvantages,uncertainties and even attacks.To solve this problem,a transaction frequency based trust is proposed in this study.The proposed method is composed of two parts.The first part is built on the classic Bayes analysis based trust modelswhich are ease of computing for the E-commerce system.The second part is the transaction frequency module which can mitigate the potential insecurity caused by one participating entity gaining trust in a short time.Simulations show that the proposed method can effectively mitigate the self-promoting attacks so as to maintain the function of E-commerce system.
文摘The promotion of both market fairness and efficiency has long been a goal of securities market regulators worldwide.Accelerated digital disruption and abusive trading behaviors,such as the GameStop mania,prompt regulatory changes.It is unclear how this“democratization”of trading power affects market fairness as economies cope with pandemic-driven shifts in basic systems.Excessive speculation and market manipulation undermine the quality of financial markets in the sense that they cause volatil-ity and increase the pain of bubble and crash events.Thereby,they weaken public confidence in financial markets to fulfill their roles in proper capital allocation to irrigate the real economy and generate value for society.While previous studies have mostly focused on market efficiency,our study proposes a tool to improve market fairness,even under periods of stress.To encourage value generation and improve market quality,we advance a graduated Non-Value-Added Tax that we implement in an agent-based model that can realistically capture the properties of real-world financial markets.A profitable transaction is taxed at a higher rate if it does not enhance the efficiency measured by deviation from fundamentals.When an agent locks in profit not supported by fundamentals but driven by trend-following strategies,the generated profit is taxed at various rates under the Non-Value-Added Tax regime.Unlike existing financial transaction taxes,the non-value-added tax is levied on profit rather than on price or volume.We show that the proposed tax encourages profitable trades that add value to the market and discourages valueless profit-making.It significantly curtails volatility and prevents the occurrence of extreme market events,such as bubbles and crashes.
文摘针对通过零售交易数据进行客户分群时传统方法未考虑商品的价值问题,提出用RFM(recency frequency monetary)表达交易数据的方法,该方法将客户购买的商品和商品类别组成一棵RFM购买树(recency frequency monetary purchase tree,RFMPT).提出基于RFM购买树的快速聚类算法(based recency frequency monetary purchase tree clustering,BRFMPTC),把购买树构建为Cover Tree(CT)索引结构,利用CT结构快速选择k个密度最大的购买树作为中心,将其他对象划分到距它最近的类中心.实验结果表明,在距离加权下,BRFMPTC算法较传统算法在整体上能产生质量更高的聚类结果,性能得到较大提升.
