Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data
Financial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indire...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2021/5178970 |
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| _version_ | 1850212243339214848 |
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| author | Qizhi He Pingfan Xia Bo Li Jia-Bao Liu |
| author_facet | Qizhi He Pingfan Xia Bo Li Jia-Bao Liu |
| author_sort | Qizhi He |
| collection | DOAJ |
| description | Financial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indirectly in the number of participants for being full, and the larger the preference, the shorter the time and the fewer the participants. Second, investors can discriminate the default risk not reflected by the interest rate, and the bigger the default risk, the longer the time and the more participants being full. Third, investors can discriminate the pure return rate deducted from the maturity term and credit risk, and the higher the return, the shorter the time and the fewer the participants being full. Fourth, default risks are reflected well by online loan platform interest rates, and inventors do not choose the item blindly according to the interest rate but consider comprehensively the profit and the risk. In the future, interest rate liberalization should be deepened, the choosing function of interest rates should be played better, and the information disclosure, investor education, and investor effective usage of other information should be strengthened. |
| format | Article |
| id | doaj-art-8e9166d44e9a430fadd8a2d5d7abe93a |
| institution | OA Journals |
| issn | 2314-8896 2314-8888 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-8e9166d44e9a430fadd8a2d5d7abe93a2025-08-20T02:09:23ZengWileyJournal of Function Spaces2314-88962314-88882021-01-01202110.1155/2021/51789705178970Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big DataQizhi He0Pingfan Xia1Bo Li2Jia-Bao Liu3School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Finance, Anhui University of Finance & Economics, Bengbu, 233030 Anhui, ChinaSchool of Finance, Anhui University of Finance & Economics, Bengbu, 233030 Anhui, ChinaSchool of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230601 Anhui, ChinaFinancial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indirectly in the number of participants for being full, and the larger the preference, the shorter the time and the fewer the participants. Second, investors can discriminate the default risk not reflected by the interest rate, and the bigger the default risk, the longer the time and the more participants being full. Third, investors can discriminate the pure return rate deducted from the maturity term and credit risk, and the higher the return, the shorter the time and the fewer the participants being full. Fourth, default risks are reflected well by online loan platform interest rates, and inventors do not choose the item blindly according to the interest rate but consider comprehensively the profit and the risk. In the future, interest rate liberalization should be deepened, the choosing function of interest rates should be played better, and the information disclosure, investor education, and investor effective usage of other information should be strengthened.http://dx.doi.org/10.1155/2021/5178970 |
| spellingShingle | Qizhi He Pingfan Xia Bo Li Jia-Bao Liu Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data Journal of Function Spaces |
| title | Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data |
| title_full | Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data |
| title_fullStr | Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data |
| title_full_unstemmed | Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data |
| title_short | Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data |
| title_sort | evaluating investors recognition abilities for risk and profit in online loan markets using nonlinear models and financial big data |
| url | http://dx.doi.org/10.1155/2021/5178970 |
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