Data mining enables us to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive adva...Data mining enables us to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field, it is a specially used in portfolio management, fraud detection, payment prediction, loan risk analysis, mortgage scoring, determining transaction manipulation, determining financial risk management, determining customer profile and foreign exchange market. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining in financial applications.展开更多
Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sam...Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sample size. A widely used approach for reducing dimensionality is based on multi-factor models. Although it has been well studied and quite successful in many applications, the quality of the estimated covariance matrix is often degraded due to a nontrivial amount of missing data in the factor matrix for both technical and cost reasons. Since the factor matrix is only approximately low rank or even has full rank, existing matrix completion algorithms are not applicable. We consider a new matrix completion paradigm using the factor models directly and apply the alternating direction method of multipliers for the recovery. Numerical experiments show that the nuclear-norm matrix completion approaches are not suitable but our proposed models and algorithms are promising.展开更多
Scenario approach is a widely used tool in portfolio risk management,however,it often runs into dilemma when determining the distribution of asset returns with insufficient information,which will be used to simulate t...Scenario approach is a widely used tool in portfolio risk management,however,it often runs into dilemma when determining the distribution of asset returns with insufficient information,which will be used to simulate the scenarios.Also the quality of generated scenarios are not guaranteed even when the distribution of asset returns is known exactly.A set-valued scenario approach was proposed by Zhu,et al.(2015)as a possible remedy.As a necessary supplement of the results proposed by Zhu,et al.(2015),this paper theoretically investigates the convergent property of the numerical solution based on the set-valued scenario approach under the condition that the underlying distribution is known.展开更多
文摘Data mining enables us to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field, it is a specially used in portfolio management, fraud detection, payment prediction, loan risk analysis, mortgage scoring, determining transaction manipulation, determining financial risk management, determining customer profile and foreign exchange market. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining in financial applications.
基金supported by National Natural Science Foundation of China(Grant Nos.10971122,11101274 and 11322109)Scientific and Technological Projects of Shandong Province(Grant No.2009GG10001012)Excellent Young Scientist Foundation of Shandong Province(Grant No.BS2012SF025)
文摘Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sample size. A widely used approach for reducing dimensionality is based on multi-factor models. Although it has been well studied and quite successful in many applications, the quality of the estimated covariance matrix is often degraded due to a nontrivial amount of missing data in the factor matrix for both technical and cost reasons. Since the factor matrix is only approximately low rank or even has full rank, existing matrix completion algorithms are not applicable. We consider a new matrix completion paradigm using the factor models directly and apply the alternating direction method of multipliers for the recovery. Numerical experiments show that the nuclear-norm matrix completion approaches are not suitable but our proposed models and algorithms are promising.
基金partially supported by the National Natural Science Foundation of China under Grant Nos.71471180,61170107,71571062the National Natural Science Foundation of Hebei Normal University under Grant No.L2011Z12
文摘Scenario approach is a widely used tool in portfolio risk management,however,it often runs into dilemma when determining the distribution of asset returns with insufficient information,which will be used to simulate the scenarios.Also the quality of generated scenarios are not guaranteed even when the distribution of asset returns is known exactly.A set-valued scenario approach was proposed by Zhu,et al.(2015)as a possible remedy.As a necessary supplement of the results proposed by Zhu,et al.(2015),this paper theoretically investigates the convergent property of the numerical solution based on the set-valued scenario approach under the condition that the underlying distribution is known.
