• Akerlof GA, Kranton RE (2000) Economics and identity. Q J Econ 115(3):715–753

    Article 

    Google Scholar
     

  • Armstrong SJ, Cools E, Sadler-Smith E (2012) Role of cognitive styles in business and management: Reviewing 40 years of research. Int J Manag Rev 14(3):238–262

    Article 

    Google Scholar
     

  • Arráiz I, Bruhn M, Stucchi R (2017) Psychometrics as a tool to improve credit information. World Bank Econ Rev 30(Supplement 1):S67–S76


    Google Scholar
     

  • Belleflamme P, Omrani N, Peitz M (2015) The economics of crowdfunding platforms. Inf Econ Policy 33:11–28

    Article 

    Google Scholar
     

  • Blanco A, Pino-Mejías R, Lara J, Rayo S (2013) Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst Appl 40(1):356–364

    Article 

    Google Scholar
     

  • Bolton LE, Bloom PN, Cohen JB (2011) Using loan plus lender literacy information to combat one-sided marketing of debt consolidation loans. J Market Res, 48(SPL):S51–S59

  • Bradley-Geist JC, Landis RS (2012) Homogeneity of personality in occupations and organizations: a comparison of alternative statistical tests. J Bus Psychol 27(2):149–159

    Article 

    Google Scholar
     

  • Brockett PL, Golden LL (2007) Biological and psychobehavioral correlates of credit scores and automobile insurance losses: toward an explication of why credit scoring works. J Risk Insurance 74(1):23–63

    Article 

    Google Scholar
     

  • Carless SA (1999) Career assessment: Holland’s vocational interests, personality characteristics, and abilities. J Career Assess 7(2):125–144

    Article 

    Google Scholar
     

  • Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83(403):596–610

    Article 

    Google Scholar
     

  • Cohen WW, Ravikumar P, Fienberg SE (2003) A comparison of string distance metrics for name-matching tasks. In: Proceedings of the international workshop on information integration on the web (IIWeb), vol 2003 (pp 73–78)

  • Cumming DJ, Martinez-Salgueiro A, Reardon RS, Sewaid A (2021) COVID-19 bust, policy response, and rebound: equity crowdfunding and P2P versus banks. J Technol Transf, 1–22

  • Dahlbäck O (1991) Saving and risk taking. J Econ Psychol 12(3):479–500

    Article 

    Google Scholar
     

  • DaSilva A, Giannikos CI (2006) Higher risk aversion in older agents: Its asset pricing implications. Available at SSRN 955958

  • Emekter R, Tu Y, Jirasakuldech B, Lu M (2015) Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Appl Econ 47(1):54–70

    Article 

    Google Scholar
     

  • Fernández B, Garcia-Merino T, Mayoral R, Santos V, Vallelado E (2011) Herding, information uncertainty and investors’ cognitive profile. Qual Res Financ Mark 3(1):7–33

    Article 

    Google Scholar
     

  • Fisher SH, Herrick R (2002) Whistle while you work: job satisfaction and retirement from the US house. Legis Stud Q 27(3):445–457

    Article 

    Google Scholar
     

  • Fitch C, Chaplin R, Trend C, Collard S (2007) Debt and mental health: the role of psychiatrists. Adv Psychiatr Treat 13(3):194–202

    Article 

    Google Scholar
     

  • Florez-Lopez R, Ramon-Jeronimo JM (2015) Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment: a correlated-adjusted decision forest proposal. Expert Syst Appl 42(13):5737–5753

  • Furnham A, Dissou G, Sloan P, Chamorro-Premuzic T (2007) Personality and intelligence in business people: a study of two personality and two intelligence measures. J Bus Psychol 22(1):99–109

    Article 

    Google Scholar
     

  • Galloway G (2002) Psychographic segmentation of park visitor markets: evidence for the utility of sensation seeking. Tour Manage 23(6):581–596

    Article 

    Google Scholar
     

  • Garcia-Sedeñto M, Navarro JI, Menacho I (2009) Relationship between personality traits and vocational choice. Psychol Rep 105(2):633–642

    Article 

    Google Scholar
     

  • Gathergood J (2012) Self-control, financial literacy and consumer over-indebtedness. J Econ Psychol 33(3):590–602

    Article 

    Google Scholar
     

  • Ge R, Feng J, Gu B, Zhang P (2017) Predicting and deterring default with social media information in peer-to-peer lending. J Manag Inf Syst 34(2):401–424

    Article 

    Google Scholar
     

  • Guiso L, Sapienza P, Zingales L (2013) The determinants of attitudes toward strategic default on mortgages. J Financ 68(4):1473–1515

    Article 

    Google Scholar
     

  • Guo Y, Zhou W, Luo C, Liu C, Xiong H (2016) Instance-based credit risk assessment for investment decisions in P2P lending. Eur J Oper Res 249(2):417–426

    Article 

    Google Scholar
     

  • Haack J, Wiese H, Abraham A, Chiarcos C (2012) Factors of reduction of money illusion. Science 25:453–470


    Google Scholar
     

  • Hogan R, Blake R (1999) John Holland’s vocational typology and personality theory. J Vocat Behav 55(1):41–56

    Article 

    Google Scholar
     

  • Holland JL (1973) Making vocational choices: a theory of careers. Englewood Cliffs, New Jersey


    Google Scholar
     

  • Insler M, Compton J, Schmitt P (2016) The investment decisions of young adults under relaxed borrowing constraints. J Behav Exp Econ 64:106–121

    Article 

    Google Scholar
     

  • Jagtiani J, Lemieux C (2019) The roles of alternative data and machine learning in fintech lending: evidence from the LendingClub consumer platform. Financ Manage 48(4):1009–1029

    Article 

    Google Scholar
     

  • Jiang C, Wang Z, Wang R, Ding Y (2018) Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Ann Oper Res 266(1–2):511–529

    Article 

    Google Scholar
     

  • Jiménez G, Saurina J (2004) Collateral, type of lender and relationship banking as determinants of credit risk. J Bank Finance 28(9):2191–2212

    Article 

    Google Scholar
     

  • Jin Y, Zhu Y (2015) A data-driven approach to predict default risk of loan for online peer-to-peer (P2P) lending. In: 2015 Fifth international conference on communication systems and network technologies (pp 609–613). IEEE

  • Jung, C. G. (1923). Psychological types (HG Baynes, Trans.). London: Kegan Paul.

  • King DD, Ott-Holland CJ, Ryan AM, Huang JL, Wadlington PL, Elizondo F (2017) Personality homogeneity in organizations and occupations: considering similarity sources. J Bus Psychol 32(6):641–653

    Article 

    Google Scholar
     

  • Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci 275:1–12

    Article 

    Google Scholar
     

  • Kou G, Xu Y, Peng Y, Shen F, Chen Y, Chang K, Kou S (2021) Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Syst 140: 113429

  • Kuhnen CM, Knutson B (2011) The influence of affect on beliefs, preferences, and financial decisions. J Financ Quant Anal 46(3):605–626

    Article 

    Google Scholar
     

  • Lamdin DJ (2008) Does consumer sentiment foretell revolving credit use? J Fam Econ Issues 29(2):279–288

    Article 

    Google Scholar
     

  • Lee E, Lee B (2012) Herding behavior in online P2P lending: an empirical investigation. Electron Commer Res Appl 11(5):495–503

    Article 

    Google Scholar
     

  • Li T, Kou G, Peng Y, Philip SY (2021a) An integrated cluster detection, optimization, and interpretation approach for financial data. IEEE Trans Cybern

  • Li WD, Fay D, Frese M, Harms PD, Gao XY (2014) Reciprocal relationship between proactive personality and work characteristics: a latent change score approach. J Appl Psychol 99(5):948

    Article 

    Google Scholar
     

  • Li Z, Tian Y, Li K, Zhou F, Yang W (2017) Reject inference in credit scoring using semi-supervised support vector machines. Expert Syst Appl 74:105–114

    Article 

    Google Scholar
     

  • Li, Z., Zhang, J., Yao, X., Kou, G. (2021b). How to identify early defaults in online lending: a cost-sensitive multi-layer learning framework. Knowl-Based Syst 221:106963

  • Liberti JM, Petersen MA (2019) Information: hard and soft. Rev Corp Financ Stud 8(1):1–41

    Article 

    Google Scholar
     

  • Ma L, Zhao X, Zhou Z, Liu Y (2018) A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decis Support Syst 111:60–71

    Article 

    Google Scholar
     

  • Manaf K, Pitara SW, Subaeki B, Gunawan R (2019) Comparison of carp rabin algorithm and Jaro-Winkler distance to determine the equality of Sunda languages. In: 2019 IEEE 13th international conference on telecommunication systems, services, and applications (TSSA), pp 77–81

  • McKenna J, Hyllegard K, Linder R (2003) Linking psychological type to financial decision-making. J Financ Counseling Plan 14(1)

  • Muradoglu G, Harvey N (2012) Behavioural finance: the role of psychological factors in financial decisions. Rev Behav Finance 4(2):68–80

    Article 

    Google Scholar
     

  • Nicholson N, Soane E, Fenton-O’Creevy M, Willman P (2005) Personality and domain-specific risk taking. J Risk Res 8(2):157–176

    Article 

    Google Scholar
     

  • Nigmonov A, Shams S, Alam K (2022) Macroeconomic determinants of loan defaults: evidence from the US peer-to-peer lending market. Res Int Bus Finance, 59:101516

  • Nitani M, Riding A, Orser B (2020) Self-employment, gender, financial knowledge, and high-cost borrowing. J Small Bus Manage 58(4):669–706

    Article 

    Google Scholar
     

  • Norvilitis JM, Merwin MM, Osberg TM, Roehling PV, Young P, Kamas MM (2006) Personality factors, money attitudes, financial knowledge, and credit-card debt in college students 1. J Appl Soc Psychol 36(6):1395–1413

    Article 

    Google Scholar
     

  • Nyhus EK, Webley P (2001) The role of personality in household saving and borrowing behaviour. Eur J Pers 15(S1):S85–S103

  • Oberlechner T, Hocking S (2004) Information sources, news, and rumors in financial markets: Insights into the foreign exchange market. J Econ Psychol 25(3):407–424

    Article 

    Google Scholar
     

  • Peress J (2004) Wealth, information acquisition, and portfolio choice. Rev Financ Stud 17(3):879–914

    Article 

    Google Scholar
     

  • Saluja HK, Sharma RRK, Yadav VK, Drave V (2018) Big-data for interactive advertisement: few propositions. In: Proceedings of the international conference on industrial engineering and operations management.

  • Serrano-Cinca C, Gutiérrez-Nieto B (2016) The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decis Support Syst 89:113–122

    Article 

    Google Scholar
     

  • Serrano-Cinca C, Gutiérrez-Nieto B, López-Palacios L (2015) Determinants of default in P2P lending. PLoS ONE, 10(10): e0139427

  • Song Y, Wang Y, Ye X, Wang D, Yin Y, Wang Y (2020) Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending. Inf Sci 525:182–204

    Article 

    Google Scholar
     

  • Tang H (2019) Peer-to-peer lenders versus banks: substitutes or complements? Rev Financ Stud 32(5):1900–1938

    Article 

    Google Scholar
     

  • Tokunaga H (1993) The use and abuse of consumer credit: application of psychological theory and research. J Econ Psychol 14(2):285–316

    Article 

    Google Scholar
     

  • Vissing-Jorgensen A (2003) Perspectives on behavioral finance: does” irrationality” disappear with wealth? Evidence from expectations and actions. NBER Macroecon Annu 18:139–194

    Article 

    Google Scholar
     

  • Volpone SD, Tonidandel S, Avery DR, Castel S (2015) Exploring the use of credit scores in selection processes: Beware of adverse impact. J Bus Psychol 30(2):357–372

    Article 

    Google Scholar
     

  • Walczak S, Borkan GL (2016) Personality type effects on perceptions of online credit card payment services. J Theor Appl Electron Commer Res 11(1):67–83

    Article 

    Google Scholar
     

  • Wang H, Chen K, Zhu W, Song Z (2015a) A process model on P2P lending. Financ Innov 1(1):1–8

    Article 

    Google Scholar
     

  • Wang H, Kou G, Peng Y (2021) Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. J Oper Res Soc 72(4):923–934

    Article 

    Google Scholar
     

  • Wang L, Lu W, Malhotra NK (2011) Demographics, attitude, personality and credit card features correlate with credit card debt: a view from China. J Econ Psychol 32(1):179–193

    Article 

    Google Scholar
     

  • Wang P, Zheng H, Chen D, Ding L (2015b) Exploring the critical factors influencing online lending intentions. Financ Innov 1(1):1–11

    Article 

    Google Scholar
     

  • Wang, Y., Drabek, Z., Wang, Z. (2022). The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company. J Behav Exp Econ 96:101806

  • Wardrop R, Zhang B, Rau R, Gray M (2015) Moving mainstream. Eur Altern Finance Benchmark Rep 1:43


    Google Scholar
     

  • Watson JJ (2003) The relationship of materialism to spending tendencies, saving, and debt. J Econ Psychol 24(6):723–739

    Article 

    Google Scholar
     

  • Xia Y, He L, Li Y, Liu N, Ding Y (2020) Predicting loan default in peer-to-peer lending using narrative data. J Forecast 39(2):260–280

    Article 

    Google Scholar
     

  • Xia Y, Liu C, Li Y, Liu N (2017) A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225–241

    Article 

    Google Scholar
     

  • Xia Y, Yang X, Zhang Y (2018) A rejection inference technique based on contrastive pessimistic likelihood estimation for P2P lending. Electron Commer Res Appl 30:111–124

    Article 

    Google Scholar
     

  • Yan J, Yu W, Zhao JL (2015) How signaling and search costs affect information asymmetry in P2P lending: the economics of big data. Financ Innov 1(1):19

    Article 

    Google Scholar
     

  • Zanin L (2020) Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market. J Behav Exp Finance, 25, 100272

  • Zuckerman M, Kuhlman DM (2000) Personality and risk-taking: common bisocial factors. J Pers 68(6):999–1029

    Article 

    Google Scholar
     

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