Information Asymmetry Among Investors and Strategic Bidding in Peer-to-Peer Lending

2021 ◽  
Author(s):  
Kai Lu ◽  
Zaiyan Wei ◽  
Tat Y. Chan
Keyword(s):  
Information Asymmetry ◽  
Early Stage ◽  
Peer To Peer ◽  
Data Set ◽  
Risky Investment ◽  
Strategic Behaviors ◽  
P2p Lending ◽  
Default Rate ◽  
Low Probability ◽  
Information Spillover

Peer-to-peer (P2P) lending became a global phenomenon in recent years. Despite their prominence in the “FinTech” era, P2P platforms remain a risky investment because of the high default rate of unsecured personal loans funded on such platforms. In contrast, the rate of return can be much higher than that of other investments if P2P loans are repaid. Therefore, investors of P2P loans need information about borrowers’ ability to repay. An important channel is to learn from other investors who may have information advantages. We argue that, because collective effort from investors is required in P2P lending, it could be optimal for informed investors to bid early in projects with the purpose of signaling the quality. With a unique data set from Prosper.com, we find that informed investors are indeed more likely to bid in the early stage of a project with a low probability of being funded, whereas uninformed investors will follow. The “squatting” behavior (early bidding) of informed investors facilitates information spillover to uninformed investors, benefitting the investors and borrowers who otherwise may not raise sufficient funding. Our findings also have implications for P2P lending platforms on how to manage the information asymmetry and strategic behaviors of investors.

scholarly journals The Influence of Borrowers' Language Features on P2P Lending

2021 ◽  
Author(s):  
Zhengwei Ma ◽  
Dan Zhang
Keyword(s):  
Logistic Regression ◽  
Information Asymmetry ◽  
Logistic Regression Model ◽  
Risk Control ◽  
Education Level ◽  
Probability Of Default ◽  
Language Function ◽  
Linguistic Features ◽  
P2p Lending ◽  
Default Rate

Abstract Under the background of the reshuffle of the P2P market in China, this paper investigates the influence of four borrower's language features on their funding and default rate based on language function theories. In our study, we use a logistic regression model and the empirical results show that: the more redundant the borrower's language expression is, the more open and objective the content is, and the more attention is paid to the punctuation details, the easier it is to obtain the loan successfully. When the borrower's description is more redundant and more attention is paid to the punctuation details, the probability of default would become lower. Taking the education level into consideration, we find that the negative relating effect between the description redundancy and the default rate would be lower with the increase of the borrower’s education level. Therefore, we can conclude that the four linguistic features of borrowers which are defined in this paper can alleviate the information asymmetry problem of P2P lending to some extent and the borrower's linguistic features can be included into the risk control system.

scholarly journals Determinants of Borrowers’ Default in P2P Lending under Consideration of the Loan Risk Class

Games ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 82 ◽  
Author(s):  
Michal Polena ◽  
Tobias Regner
Keyword(s):  
Credit Card ◽  
Annual Income ◽  
Data Set ◽  
Risk Class ◽  
Risk Levels ◽  
The Past ◽  
Income Ratio ◽  
Loan Risk ◽  
P2p Lending ◽  
Default Rate

We study the determinants of borrowers’ default in P2P lending with a new data set consisting of 70,673 loan observations from the Lending Club. Previous research identified a number of default determining variables but did not distinguish between different loan risk levels. We define four loan risk classes and test the significance of the default determining variables within each loan risk class. Our findings suggest that the significance of most variables depends on the loan risk class. Only a few variables are consistently significant across all risk classes. The debt-to-income ratio, inquiries in the past six months and a loan intended for a small business are positively correlated with the default rate. Annual income and credit card as loan purpose are negatively correlated.

Strategic Information Transmission in Peer-to-Peer Lending Markets

2018 ◽  
Vol 82 (2) ◽  
pp. 42-63 ◽  
Author(s):  
Fabio Caldieraro ◽  
Jonathan Z. Zhang ◽  
Marcus Cunha ◽  
Jeffrey D. Shulman
Keyword(s):  
Information Asymmetry ◽  
Peer To Peer ◽  
Strategic Information ◽  
Goods And Services ◽  
Financial Products ◽  
Massive Growth ◽  
Ex Post ◽  
P2p Lending ◽  
Loan Quality

Peer-to-peer (P2P) marketplaces, such as Uber, Airbnb, and Lending Club, have experienced massive growth in recent years. They now constitute a significant portion of the world's economy and provide opportunities for people to transact directly with one another. However, such growth also challenges participants to cope with information asymmetry about the quality of the offerings in the marketplace. By conducting an analysis of a P2P lending market, the authors propose and test a theory in which countersignaling provides a mechanism to attenuate information asymmetry about financial products (loans) offered on the platform. Data from a P2P lending website reveal significant, nonmonotonic relationships among the transmission of nonverifiable information, loan funding, and ex post loan quality, consistent with the proposed theory. The results provide insights for platform owners who seek to manage the level of information asymmetry in their P2P environments to create more balanced marketplaces, as well as for P2P participants interested in improving their ability to process information about the goods and services they seek to transact online.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

PERLINDUNGAN DATA PRIBADI PENERIMA PINJAMAN DALAM TRANSAKSI PINJAM MEMINJAM UANG BERBASIS TEKNOLOGI INFORMASI (PEER TO PEER LENDING)

2021 ◽  
Vol 50 (4) ◽  
pp. 789
Author(s):  
Hendrawan Agusta
Keyword(s):  
Peer To Peer ◽  
P2p Lending ◽  
Peer Lending ◽  
Financial Technology

Perkembangan teknologi informasi sangat pesat, adanya kolaborasi antara teknologi informasi dengan berbagai bidang kehidupan melahirkan berbagai macam inovasi yang membuat kehidupan masyarakat semakin mudah. Inovasi di bidang teknologi informasi melahirkan model bisnis baru yang pada gilirannya mampu menghasilkan efisiensi bagi masyarakat. Revolusi teknologi informasi tersebut terus berkembang dan sekarang memasuki bidang keuangan yang regulasinya ketat. Kolaborasi antara teknologi informasi dengan bidang keuangan melahirkan Teknologi Finansial atau Financial Technology (Fintech), salah satunya pinjam-meminjam uang berbasis teknologi informasi (Peer to Peer Lending/P2P Lending). Masyarakat menjadi lebih mudah mengakses kebutuhan keuangannya melalui P2P Lending. Di sisi lain, muncul tantangan dalam P2P Lending mengenai perlindungan data (data pribadi, data transaksi dan data keuangan). Dalam penelitian ini yang akan dibahas hanya data pribadi Penerima Pinjaman, dimana data pribadi tersebut perlu dilindungi agar tidak terjadi penyalahgunaan yang menimbulkan permasalahan hukum

Racial disparities in stage-specific gastric cancer: analysis of results from the Surveillance Epidemiology and End Results (SEER) program database

2017 ◽  
Vol 65 (6) ◽  
pp. 991-998 ◽  
Author(s):  
Gang Zhang ◽  
Xing Zhao ◽  
Jie Li ◽  
Yu Yuan ◽  
Ming Wen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Radiation Treatment ◽  
Early Stage ◽  
Ethnic Disparities ◽  
Incidence Rates ◽  
Pacific Islanders ◽  
Seer Program ◽  
Second Primary ◽  
Data Set ◽  
End Results

The incidence of gastric cancer is declining in western countries but continues to represent a serious health problem worldwide, especially in Asia and among Asian Americans. This study aimed to investigate ethnic disparities in stage-specific gastric cancer, including differences in incidence, treatment and survival. The cohort study was analyzed using the data set of patients with gastric cancer registered in the Surveillance, Epidemiology, and End Results (SEER) program from 2004 to 2013. Among 54,165 patients with gastric cancer, 38,308 were whites (70.7%), 7546 were blacks (13.9%), 494 were American Indian/Alaskan Natives (0.9%) and 7817 were Asians/Pacific Islanders (14.4%). Variables were patient demographics, disease characteristics, surgery/radiation treatment, overall survival (OS) and cause specific survival (CSS). Asians/Pacific Islanders demonstrated the highest incidence rates for gastric cancer compared with other groups and had the greatest decline in incidence during the study period (13.03 to 9.28 per 100,000/year), as well as the highest percentage of patients with American Joint Committee on Cancer (AJCC) early stage gastric cancer. There were significant differences between groups in treatment across stages I–IV (all p<0.001); Asians/Pacific Islanders had the highest rate of surgery plus radiation (45.1%). Significant differences were found in OS and CSS between groups (p<0.001); OS was highest among Asians/Pacific Islanders. Multivariate analysis revealed that age, race, grade, stage, location, and second primary cancer were valid prognostic factors for survival. Marked ethnic disparities exist in age-adjusted incidence of primary gastric cancer, with significant differences between races in age, gender, histological type, grade, AJCC stage, location, second cancer, treatment and survival.

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

scholarly journals An Immune Signature Robustly Predicts Clinical Deterioration for Hepatitis C Virus-Related Early-Stage Cirrhosis Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Cheng Guo ◽  
Chenglai Dong ◽  
Junjie Zhang ◽  
Rui Wang ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Early Stage ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Calibration Plot ◽  
Prognostic Signature ◽  
Data Set ◽  
Immune Infiltration ◽  
Immune Related Genes

Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the “clusterProfiler” R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

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