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互联网法拍对不良资产定价的影响研究 =Research on the Influence of Internet Auction on the Pricing of Non-performing Assets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	互联网法拍对不良资产定价的影响研究 =
其他題名:	Research on the Influence of Internet Auction on the Pricing of Non-performing Assets.
作者:	Shi, Jinhua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2021
面頁冊數:	95 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-11, Section: A.
附註:	Advisor: Shao, Benjamin;Yan, Hong.
Contained By:	Dissertations Abstracts International82-11A.
標題:	Finance.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28415420
ISBN:	9798728269304

互联网法拍对不良资产定价的影响研究 =Research on the Influence of Internet Auction on the Pricing of Non-performing Assets.
Shi, Jinhua.

互联网法拍对不良资产定价的影响研究 =Research on the Influence of Internet Auction on the Pricing of Non-performing Assets. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 95 p.

Source: Dissertations Abstracts International, Volume: 82-11, Section: A.

Thesis (Ph.D.)--Arizona State University, 2021.

This item must not be sold to any third party vendors.

本文对不良资产法拍定价的实践操作进行分析，选取一线、二线部分城市的住宅为主要研究对象，以主流互联网拍卖平台上真实成交的司法拍卖案例为样本，样本覆盖上海、杭州、苏州、南京等国内一二线城市主流司法拍卖资产，时间跨度为2017年至2020年。本文选取影响不良法拍资产定价的主要因素有：资产面积、位置、税费承担方式、参拍人数、加价轮数、租约情况等，以上述主要因素为自变量因素，以不良法拍资产价格偏离指数及参拍人数作为因变量。回归模型分别采用线性回归和二阶段最小二乘法，把资产面积、位置、税费承担方式、参拍人数、加价轮数、租约情况作为核心变量，研究各个因素对于不良资产拍卖成交价格的影响程度。本次所采用的回归分析中，由于研究的因果关系涉及因变量和两个以上自变量，因此在研究过程中选取六个核心变量，在系统梳理大量样本数据的基础上，利用数理统计方法建立因变量与自变量之间的回归模型，通过线性回归方法研究六个核心变量与因变量之间的关系，并通过二阶段最小二乘法来剔除核心区域、税费承担方式、租约情况对参拍人数的影响，最终得出核心变量对因变量的影响。经研究发现，位置、资产面积、税费承担方式、租约情况对△p呈负向影响，参拍人数、加价轮数对△p呈正向影响。.

ISBN: 9798728269304Subjects--Topical Terms:

183252
Finance.
Subjects--Index Terms:

Judicial Auction

互联网法拍对不良资产定价的影响研究 =Research on the Influence of Internet Auction on the Pricing of Non-performing Assets.
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300 $a 95 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-11, Section: A.
500 $a Advisor: Shao, Benjamin;Yan, Hong.
502 $a Thesis (Ph.D.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a 本文对不良资产法拍定价的实践操作进行分析，选取一线、二线部分城市的住宅为主要研究对象，以主流互联网拍卖平台上真实成交的司法拍卖案例为样本，样本覆盖上海、杭州、苏州、南京等国内一二线城市主流司法拍卖资产，时间跨度为2017年至2020年。本文选取影响不良法拍资产定价的主要因素有：资产面积、位置、税费承担方式、参拍人数、加价轮数、租约情况等，以上述主要因素为自变量因素，以不良法拍资产价格偏离指数及参拍人数作为因变量。回归模型分别采用线性回归和二阶段最小二乘法，把资产面积、位置、税费承担方式、参拍人数、加价轮数、租约情况作为核心变量，研究各个因素对于不良资产拍卖成交价格的影响程度。本次所采用的回归分析中，由于研究的因果关系涉及因变量和两个以上自变量，因此在研究过程中选取六个核心变量，在系统梳理大量样本数据的基础上，利用数理统计方法建立因变量与自变量之间的回归模型，通过线性回归方法研究六个核心变量与因变量之间的关系，并通过二阶段最小二乘法来剔除核心区域、税费承担方式、租约情况对参拍人数的影响，最终得出核心变量对因变量的影响。经研究发现，位置、资产面积、税费承担方式、租约情况对△p呈负向影响，参拍人数、加价轮数对△p呈正向影响。.
520 $a This dissertation analyzes the practical operations of non-performing assets auction pricing by selecting the first- and second-tier cities as the main subjects of study and taking the real judicial auction cases on the mainstream Internet auction platform as the sample. The data covers Shanghai, Hangzhou, Suzhou, Nanjing and other first- and second-tier cities and includes their major judicial auction assets, and the time period spans from 2017 to 2020. This study considers the following main factors that may affect the pricing of non-performing auction assets: size, core area, taxpayer, number of bidders, turns, contract, etc. These main factors are modelled as independent variables, and the price deviation of non-performing auction assets as dependent variables. The regression model uses linear regression and two-stage least square method, and takes size, core area, taxpayer, number of bidders, turns, and contract as the core variables to study their influence on the auction price deviation of non-performing assets.In the regression analysis, because the causality of the study involves dependent variables and more than two independent variables, six core variables are selected in the process. On the basis of systematically combing a large number of sample data, the regression model between dependent variables and independent variables is established by using mathematical statistics method. The relationship between six core variables and dependent variables is empirically tested by the linear regression and two-stage least square methods to rule out the potential influence of core area, taxpayer, contract on the number of bidders, and confirm the influence of the core variables on the dependent variables. It is found that the core area, size, taxpayer and contract have a negative impact on △p, while the number of bidders, and turns have a positive impact on △p.
590 $a School code: 0010.
650 4 $a Finance. $3 183252
650 4 $a Business administration. $3 708619
653 $a Judicial Auction
653 $a Loan Disposition
653 $a Non Performing Assets
653 $a Price Setting
653 $a Value Assessment
653 $a 司法拍卖
653 $a 不良资产
653 $a 定价
653 $a 价值评估
690 $a 0310
690 $a 0508
710 2 $a Arizona State University. $b Business Administration. $3 531026
773 0 $t Dissertations Abstracts International $g 82-11A.
790 $a 0010
791 $a Ph.D.
792 $a 2021
793 $a Chinese
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