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Automatic vs Manual Investing: Role of Past Performance

Author

Listed:
  • Said Kaawach

    (University of Huddersfield)

  • Oskar Kowalewski

    (IESEG School of Management)

  • Oleksandr Talavera

    (University of Birmingham)

Abstract

Using unique data from a leading peer-to-peer (P2P) lending platform, we investigate the link between past investment performance and choice of auto-investing tool. Our results suggest that investors with poorly performing loan portfolios are more likely to switch automatically. This negative relationship can be explained by algorithmic aversion or investor inattention. In other words, the results suggest that good-performing investors who pay close attention to their loan portfolios or are not interested in using automated services are more likely to rely on themselves in manual mode. These results are robust to alternative specifications.

Suggested Citation

  • Said Kaawach & Oskar Kowalewski & Oleksandr Talavera, 2023. "Automatic vs Manual Investing: Role of Past Performance," Discussion Papers 23-04, Department of Economics, University of Birmingham.
    • Handle: RePEc:bir:birmec:23-04
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    References listed on IDEAS

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    1. Neal M. Stoughton & Youchang Wu & Josef Zechner, 2011. "Intermediated Investment Management," Journal of Finance, American Finance Association, vol. 66(3), pages 947-980, June.
    2. Gervais, Simon & Odean, Terrance, 2001. "Learning to be Overconfident," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 1-27.
    3. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    4. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    5. Duan, Yang & Hsieh, Tien-Shih & Wang, Ray R. & Wang, Zhihong, 2020. "Entrepreneurs' facial trustworthiness, gender, and crowdfunding success," Journal of Corporate Finance, Elsevier, vol. 64(C).
    6. Terrance Odean., 1996. "Volume, Volatility, Price and Profit When All Trader Are Above Average," Research Program in Finance Working Papers RPF-266, University of California at Berkeley.
    7. Martin G. Kocher & Julius Pahlke & Stefan T. Trautmann, 2013. "Tempus Fugit : Time Pressure in Risky Decisions," Management Science, INFORMS, vol. 59(10), pages 2380-2391, October.
    8. David Easley & David Michayluk & Maureen O’Hara and Tālis & J Putniņš, 2021. "The Active World of Passive Investing [Mutual fund’s R2 as predictor of performance]," Review of Finance, European Finance Association, vol. 25(5), pages 1433-1471.
    9. D’Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 278-299.
    10. Robert F. Stambaugh, 2014. "Investment Noise and Trends," NBER Working Papers 20072, National Bureau of Economic Research, Inc.
    11. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    12. Lusardi, Annamaria & Tufano, Peter, 2015. "Debt literacy, financial experiences, and overindebtedness," Journal of Pension Economics and Finance, Cambridge University Press, vol. 14(4), pages 332-368, October.
    13. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    14. Li Liao & Zhengwei Wang & Jia Xiang & Hongjun Yan & Jun Yang & LaurenCohen, 2021. "User Interface and Firsthand Experience in Retail Investing," The Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4486-4523.
    15. Stephen Foerster & Juhani T. Linnainmaa & Brian T. Melzer & Alessandro Previtero, 2017. "Retail Financial Advice: Does One Size Fit All?," Journal of Finance, American Finance Association, vol. 72(4), pages 1441-1482, August.
    16. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2020. "Gender gap in peer-to-peer lending: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 112(C).
    17. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    18. Caglayan, Mustafa & Talavera, Oleksandr & Zhang, Wei, 2021. "Herding behaviour in P2P lending markets," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 27-41.
    19. Merkle, Christoph, 2017. "Financial overconfidence over time: Foresight, hindsight, and insight of investors," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 68-87.
    20. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    21. Jiang, Jiajun & Liu, Yu-Jane & Lu, Ruichang, 2020. "Social heterogeneity and local bias in peer-to-peer lending – evidence from China," Journal of Comparative Economics, Elsevier, vol. 48(2), pages 302-324.
    22. Nicolae Gârleanu & Lasse Heje Pedersen, 2022. "Active and Passive Investing: Understanding Samuelson’s Dictum [A noisy rational expectations equilibrium for multi-asset securities markets]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(2), pages 389-446.
    23. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    24. Gogoll, Jan & Uhl, Matthias, 2018. "Rage against the machine: Automation in the moral domain," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 97-103.
    25. Zhang, Jing & Zhang, Wei & Li, Yuelei & Caglayan, Mustafa, 2021. "Decision time and investors' portfolio strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    26. Ruyi Ge & Zhiqiang (Eric) Zheng & Xuan Tian & Li Liao, 2021. "Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 32(3), pages 774-785, September.
    27. D'Acunto, Francesco & Ghosh, Pulak & Jain, Rajiv & Rossi, Alberto G., 2022. "How costly are cultural biases?," LawFin Working Paper Series 34, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    28. repec:bla:jfinan:v:53:y:1998:i:6:p:1887-1934 is not listed on IDEAS
    29. Robert F. Stambaugh, 2014. "Presidential Address: Investment Noise and Trends," Journal of Finance, American Finance Association, vol. 69(4), pages 1415-1453, August.
    30. Jefferson Duarte & Stephan Siegel & Lance Young, 2012. "Trust and Credit: The Role of Appearance in Peer-to-peer Lending," The Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2455-2484.
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    More about this item

    Keywords

    FinTech; Peer-to-Peer Lending; Investor Switching; Automatic Bidding;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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