Revealed Wisdom of the Crowd: Bids Predict Loan Quality

References

• Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, et al. (2016) TensorFlow: A system for large-scale machine learning. 12th USENIX Sympos. Oper. Systems Design Implementation (USENIX Association, Savannah, GA), 265–283.Google Scholar
• Agrawal A, Catalini C, Goldfarb A (2014) Some simple economics of crowdfunding. Innovation Policy Econom. 14(1):63–97.Crossref, Google Scholar
• Agrawal A, Catalini C, Goldfarb A (2015) Crowdfunding: Geography, social networks, and the timing of investment decisions. J. Econom. Management Strategy 24(2):253–274.Crossref, Google Scholar
• Atanasov P, Rescober P, Stone E, Swift SA, Servan-Schreiber E, Tetlock P, Mellers B (2017) Distilling the wisdom of crowds: Prediction markets vs. prediction polls. Management Sci. 63(3):691–706.Link, Google Scholar
• Bansal S, Gutierrez GJ (2020) Estimating uncertainties using judgmental forecasts with expert heterogeneity. Oper. Res. 68(2):363–380.Abstract, Google Scholar
• Bapna S, Ganco M (2021) Gender gaps in equity crowdfunding: Evidence from a randomized field experiment. Management Sci. 67(5):2679–2710.Link, Google Scholar
• Bapna R, Goes P, Gupta A (2003) Analysis and design of business-to-consumer online auctions. Management Sci. 49(1):85–101.Link, Google Scholar
• Bayus BL (2013) Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community. Management Sci. 59(1):226–244.Link, Google Scholar
• Ben-Assuli O, Padman R (2020) Trajectories of repeated readmissions of chronic disease patients: Risk stratification, profiling, and prediction. MIS Quart. 44(1):201–227.Crossref, Google Scholar
• Benjamin V, Raghu TS (2023) Augmenting social bot detection with crowd-generated labels. Inform. Systems Res. 34(2):487–507.Link, Google Scholar
• Berg T, Burg V, Gombović A, Puri M (2020) On the rise of Fintechs: Credit scoring using digital footprints. Rev. Financial Stud. 33(7):2845–2897.Crossref, Google Scholar
• Berry S, Levinsohn J, Pakes A (1995) Automobile prices in market equilibrium. Econometrica 63(4):841–890.Crossref, Google Scholar
• Bikhchandani S, Hirshleifer D, Tamuz O, Welch I (2024) Information cascades and social learning. J. Econom. Literature 62(3):1040–1093.Google Scholar
• Boudreau KJ, Lacetera N, Lakhani KR (2011) Incentives and problem uncertainty in innovation contests: An empirical analysis. Management Sci. 57(5):843–863.Link, Google Scholar
• Bowers A (2015) Relative comparison and category membership: The case of equity analysts. Organ. Sci. 26(2):571–583.Link, Google Scholar
• Budescu DV, Chen E (2015) Identifying expertise to extract the wisdom of crowds. Management Sci. 61(2):267–280.Link, Google Scholar
• Burtch G, Ghose A, Wattal S (2013) An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Inform. Systems Res. 24(3):499–519.Link, Google Scholar
• Burtch G, Ghose A, Wattal S (2014) Cultural differences and geography as determinants of online prosocial lending. MIS Quart. 38(3):773–794.Crossref, Google Scholar
• Burtch G, Ghose A, Wattal S (2016) Secret admirers: An empirical examination of information hiding and contribution dynamics in online crowdfunding. Inform. Systems Res. 27(3):478–496.Link, Google Scholar
• Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 785–794.Google Scholar
• Chen J, Stallaert J (2014) An economic analysis of online advertising using behavioral targeting. MIS Quart. 38(2):429–450.Crossref, Google Scholar
• Chen H, De P, Hu YJ, Hwang BH (2014) Wisdom of crowds: The value of stock opinions transmitted through social media. Rev. Financial Stud. 27(5):1367–1403.Crossref, Google Scholar
• Choi H, Mela CF, Balseiro SR, Leary A (2020) Online display advertising markets: A literature review and future directions. Inform. Systems Res. 31(2):556–575.Link, Google Scholar
• Cox DR (1958) The regression analysis of binary sequences. J. Roy. Statist. Soc. B 20(2):215–232.Crossref, Google Scholar
• Da Z, Huang X (2020) Harnessing the wisdom of crowds. Management Sci. 66(5):1847–1867.Link, Google Scholar
• Dai H, Zhang DJ (2019) Prosocial goal pursuit in crowdfunding: Evidence from Kickstarter. J. Marketing Res. 56(3):498–517.Crossref, Google Scholar
• DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44(3):837–845.Crossref, Google Scholar
• Donovan J (2021) Financial reporting and entrepreneurial finance: Evidence from equity crowdfunding. Management Sci. 67(11):7214–7237.Link, Google Scholar
• Duarte J, Siegel S, Young L (2012) Trust and credit: The role of appearance in peer-to-peer lending. Rev. Financial Stud. 25(8):2455–2484.Crossref, Google Scholar
• Ewens M, Townsend RR (2020) Are early stage investors biased against women? J. Financial Econom. 135(3):653–677.Crossref, Google Scholar
• Frydman C, Krajbich I (2022) Using response times to infer others’ private information: An application to information cascades. Management Sci. 68(4):2970–2986.Link, Google Scholar
• Fu R, Huang Y, Singh PV (2021) Crowds, lending, machine, and bias. Inform. Systems Res. 32(1):72–92.Link, Google Scholar
• Gao Q, Lin M, Sias RW (2023) Words matter: The role of texts in online credit markets. J. Financial Quant. Anal. 58(1):1–28.Crossref, Google Scholar
• Greenstein S, Zhu F (2018) Do experts or crowd-based models produce more bias? Evidence from Encyclopedia Britannica and Wikipedia. MIS Quart. 42(3):945–959.Crossref, Google Scholar
• Grenadier SR, Wang N (2005) Investment timing, agency, and information. J. Financial Econom. 75(3):493–533.Crossref, Google Scholar
• Hasan I, He Q, Lu H (2022) Social capital, trusting, and trustworthiness: Evidence from peer-to-peer lending. J. Financial Quant. Anal. 57(4):1409–1453.Crossref, Google Scholar
• Haykin S (1994) Neural Networks: A Comprehensive Foundation (Prentice Hall PTR, New York).Google Scholar
• Herzenstein M, Sonenshein S, Dholakia UM (2011) Tell me a good story and I may lend you money: The role of narratives in peer-to-peer lending decisions. J. Marketing Res. 48:S138–S149.Crossref, Google Scholar
• Hildebrand T, Puri M, Rocholl J (2017) Adverse incentives in crowdfunding. Management Sci. 63(3):587–608.Link, Google Scholar
• Hofman JM, Watts DJ, Athey S, Garip F, Griffiths TL, Kleinberg J, Margetts H, et al. (2021) Integrating explanation and prediction in computational social science. Nature 595(7866):181–188.Crossref, Google Scholar
• Holden CW, Lin M, Lu K, Schneemeier J, Wei Z, Yang J (2022) The effect of secondary market closure on primary market funding time: Evidence from peer-to-peer lending. Working paper, Indiana University, Bloomington.Google Scholar
• Hong Y, Hu Y, Burtch G (2018) Embeddedness, pro-sociality, and social influence: Evidence from online crowdfunding. MIS Quart. 42(4):1211–1224.Crossref, Google Scholar
• Iyer R, Khwaja AI, Luttmer EF, Shue K (2016) Screening peers softly: Inferring the quality of small borrowers. Management Sci. 62(6):1554–1577.Link, Google Scholar
• James G, Witten D, Hastie T, Tibshirani R (2013) An Introduction to Statistical Learning (Springer, New York).Crossref, Google Scholar
• Jeppesen LB, Lakhani KR (2010) Marginality and problem-solving effectiveness in broadcast search. Organ. Sci. 21(5):1016–1033.Link, Google Scholar
• Jiang Y, Ho Y, Yan X, Tan Y (2020) When online lending meets real estate: Examining investment decisions in lending-based real estate crowdfunding. Inform. Systems Res. 31(3):715–730.Link, Google Scholar
• Jiang Y, Ho YC, Yan X, Tan Y (2022) What’s in a “username”? The effect of perceived anonymity on herding in crowdfunding. Inform. Systems Res. 33(1):1–17.Link, Google Scholar
• Kanze D, Huang L, Conley MA, Higgins ET (2018) We ask men to win and women not to lose: Closing the gender gap in startup funding. Acad. Management J. 61(2):586–614.Crossref, Google Scholar
• Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inform. Processing Systems 30:3146–3154.Google Scholar
• Kim K, Viswanathan S (2019) The experts in the crowd: The role of experienced investors in a crowdfunding market. MIS Quart. 43(2):347–372.Crossref, Google Scholar
• Konovalov A, Krajbich I (2019) Revealed strength of preference: Inference from response times. Judgment Decision Making 14(4):381–394.Google Scholar
• Krishnamurthi L, Raj SP (1988) A model of brand choice and purchase quantity price sensitivities. Marketing Sci. 7(1):1–20.Link, Google Scholar
• Lee HCB, Ba S, Li X, Stallaert J (2018) Salience bias in crowdsourcing contests. Inform. Systems Res. 29(2):401–418.Link, Google Scholar
• Li C, Wang H, Jiang S, Gu B (2024) The effect of AI-enabled credit scoring on financial inclusion: Evidence from 1M underserved population. MIS Quart. 48(4):1803–1834.Crossref, Google Scholar
• Lin M, Viswanathan S (2016) Home bias in online investments: An empirical study of an online crowdfunding market. Management Sci. 62(5):1393–1414.Link, Google Scholar
• Lin M, Prabhala NR, Viswanathan S (2013) Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Sci. 59(1):17–35.Link, Google Scholar
• Lin M, Sias RW, Wei Z (2023) Experts vs. non-experts in online markets. MIS Quart. 47(1):97–126.Crossref, Google Scholar
• Liu D, Brass DJ, Lu Y, Chen D (2015) Friendships in online peer-to-peer lending: Pipes, prisms, and relational herding. MIS Quart. 39(3):729–742.Crossref, Google Scholar
• Lukyanenko R, Parsons J, Wiersma YF, Maddah M (2019) Expecting the unexpected: Effects of data collection design choices on the quality of crowdsourced user-generated content. MIS Quart. 43(2):623–648.Crossref, Google Scholar
• Majchrzak A, Malhotra A (2016) Effect of knowledge-sharing trajectories on innovative outcomes in temporary online crowds. Inform. Systems Res. 27(4):685–703.Link, Google Scholar
• Mannes AE (2009) Are we wise about the wisdom of crowds? The use of group judgments in belief revision. Management Sci. 55(8):1267–1279.Link, Google Scholar
• Mollick E, Nanda R (2016) Wisdom or madness? Comparing crowds with expert evaluation in funding the arts. Management Sci. 62(6):1533–1553.Link, Google Scholar
• Moritz A, Block JH (2016) Crowdfunding: A literature review and research directions. Brüntje D, Gajda O, eds. Crowdfunding in Europe: State of the Art in Theory and Practice (Springer International Publishing, Cham, Switzerland), 25–53.Google Scholar
• Müller-Trede J, Choshen-Hillel S, Barneron M, Yaniv I (2018) The wisdom of crowds in matters of taste. Management Sci. 64(4):1779–1803.Link, Google Scholar
• Netzer O, Lemaire A, Herzenstein M (2019) When words sweat: Identifying signals for loan default in the text of loan applications. J. Marketing Res. 56(6):960–980.Crossref, Google Scholar
• Padmanabhan B, Fang X, Sahoo N, Burton-Jones A (2022) Machine learning in information systems research. MIS Quart. 46(1):iii–xix.Crossref, Google Scholar
• Pang MS (2017) Politics and information technology investments in the US Federal government in 2003–2016. Inform. Systems Res. 28(1):33–45.Link, Google Scholar
• Papke LE, Wooldridge JM (1996) Econometric methods for fractional response variables with an application to 401 (k) plan participation rates. J. Appl. Econometrics 11(6):619–632.Crossref, Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12(85):2825–2830.Google Scholar
• Samuelson PA (1938) A note on the pure theory of consumer’s behaviour. Economica (N.S.) 5(17):61–71.Crossref, Google Scholar
• Shmueli G, Koppius OR (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.Crossref, Google Scholar
• Simester D, Timoshenko A, Zoumpoulis SI (2020) Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Sci. 66(6):2495–2522.Link, Google Scholar
• Simpson B, Schreier M, Bitterl S, White K (2021) Making the world a better place: How crowdfunding increases consumer demand for social-good products. J. Marketing Res. 58(2):363–376.Crossref, Google Scholar
• Song Y, Li Z, Sahoo N (2022) Matching returning donors to projects on philanthropic crowdfunding platforms. Management Sci. 68(1):355–375.Link, Google Scholar
• Spann M, Skiera B (2003) Internet-based virtual stock markets for business forecasting. Management Sci. 49(10):1310–1326.Link, Google Scholar
• Surowiecki J (2005) The Wisdom of Crowds (Anchor, New York).Google Scholar
• Trusov M, Ma L, Jamal Z (2016) Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Sci. 35(3):405–426.Link, Google Scholar
• Vallee B, Zeng Y (2019) Marketplace lending: A new banking paradigm? Rev. Financial Stud. 32(5):1939–1982.Crossref, Google Scholar
• Wang H, Overby E (2020) How does algorithmic trading influence investor participation in peer-to-peer online lending markets? Proc. 53rd Hawaii Internat. Conf. System Sci. (University of Hawai’i at Mānoa, Maui), 1–10.Google Scholar
• Wang J, Ipeirotis PG, Provost F (2017) Cost-effective quality assurance in crowd labeling. Inform. Systems Res. 28(1):137–158.Link, Google Scholar
• Wang G, Chen G, Zhao H, Zhang F, Yang S, Lu T (2021) Leveraging multisource heterogeneous data for financial risk prediction: A novel hybrid-strategy-based self-adaptive method. MIS Quart. 45(4):1949–1998.Crossref, Google Scholar
• Wei Z, Lin M (2017) Market mechanisms in online peer-to-peer lending. Management Sci. 63(12):4236–4257.Link, Google Scholar
• Younkin P, Kuppuswamy V (2018) The colorblind crowd? Founder race and performance in crowdfunding. Management Sci. 64(7):3269–3287.Link, Google Scholar
• Zhang J, Liu P (2012) Rational herding in microloan markets. Management Sci. 58(5):892–912.Link, Google Scholar
• Zheng A, Casari A (2018) Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (O’Reilly Media, Inc., Sebastopol, CA).Google Scholar