IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v83y2023icp373-382.html
   My bibliography  Save this article

Multidimensional attention to Fintech, trading behavior and stock returns

Author

Listed:
  • Chen, Rongda
  • Huang, Jiahao
  • Jin, Chenglu
  • Yang, Yili
  • Chen, Bing

Abstract

The rapid development of Fintech may significantly change investor behavior and investment decision-making processes. We introduce a multidimensional attention framework, which includes investor attention, academic attention and capital markets attention to Fintech, and accordingly constructs an index of Multidimensional Attention to Fintech (MAF) using a text-analysis approach. MAF is then used to measure the dynamic impacts of Fintech on investor behavior and stock market returns in China. Existing literature suggests that trading behaviors such as the number of newly opened accounts and trading volume have significantly positive impacts on stock market returns. However, these findings are not supported after adding our MAF index. Our results suggest that an increasing number of newly opened accounts and higher trading volume may not be attributable mainly to active market and trading behavior but are consequences of the development of Fintech itself.

Suggested Citation

  • Chen, Rongda & Huang, Jiahao & Jin, Chenglu & Yang, Yili & Chen, Bing, 2023. "Multidimensional attention to Fintech, trading behavior and stock returns," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 373-382.
    • Handle: RePEc:eee:reveco:v:83:y:2023:i:c:p:373-382
    DOI: 10.1016/j.iref.2022.09.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056022002398
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2022.09.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    2. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    3. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    4. Robert Bartlett & Adair Morse & Richard Stanton & Nancy Wallace, 2019. "Consumer-Lending Discrimination in the FinTech Era," NBER Working Papers 25943, National Bureau of Economic Research, Inc.
    5. Han, Xing & Li, Youwei, 2017. "Can investor sentiment be a momentum time-series predictor? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 212-239.
    6. Edelen, Roger M. & Ince, Ozgur S. & Kadlec, Gregory B., 2016. "Institutional investors and stock return anomalies," Journal of Financial Economics, Elsevier, vol. 119(3), pages 472-488.
    7. Buchak, Greg & Matvos, Gregor & Piskorski, Tomasz & Seru, Amit, 2018. "Fintech, regulatory arbitrage, and the rise of shadow banks," Journal of Financial Economics, Elsevier, vol. 130(3), pages 453-483.
    8. Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
    9. Guo, Jiaqi & Li, Youwei & Zheng, Min, 2019. "Bottom-up sentiment and return predictability of the market portfolio," Finance Research Letters, Elsevier, vol. 29(C), pages 57-60.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hou, Yang & Meng, Jiayin, 2018. "The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment," MPRA Paper 94838, University Library of Munich, Germany.
    2. Gric, Zuzana & Ehrenbergerova, Dominika & Hodula, Martin, 2022. "The power of sentiment: Irrational beliefs of households and consumer loan dynamics," Journal of Financial Stability, Elsevier, vol. 59(C).
    3. Liang, Chao & Xu, Yongan & Wang, Jianqiong & Yang, Mo, 2022. "Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Wang, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    5. Chen, Rongda & Wang, Shengnan & Ye, Mengya & Jin, Chenglu & Ren, He & Chen, Shu, 2022. "Cross-Market Investor Sentiment of Energy Futures and Return Comovements," Finance Research Letters, Elsevier, vol. 49(C).
    6. Reis, Pedro Manuel Nogueira & Pinho, Carlos, 2020. "A new European investor sentiment index (EURsent) and its return and volatility predictability," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    7. Shen, Shulin & Xia, Le & Shuai, Yulin & Gao, Da, 2022. "Measuring news media sentiment using big data for Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    8. Li, Yuan & Ran, Jimmy, 2020. "Investor Sentiment and Stock Price Premium Validation with Siamese Twins from China," Journal of Multinational Financial Management, Elsevier, vol. 57.
    9. Yuan Li, 2022. "Mood Beta, Sentiment and Stock Returns in China," SAGE Open, , vol. 12(1), pages 21582440221, February.
    10. Bissoondoyal-Bheenick, Emawtee & Do, Hung & Hu, Xiaolu & Zhong, Angel, 2022. "Sentiment and stock market connectedness: Evidence from the U.S. – China trade war," International Review of Financial Analysis, Elsevier, vol. 80(C).
    11. Yuan Li & Yu Zhang, 2021. "Investor Sentiment, Idiosyncratic Risk, and Stock Price Premium: Evidence From Chinese Cross-Listed Companies," SAGE Open, , vol. 11(2), pages 21582440211, June.
    12. Kim Kaivanto & Peng Zhang, 2019. "Investor Sentiment as a Predictor of Market Returns," Working Papers 268005798, Lancaster University Management School, Economics Department.
    13. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    14. Chue, Timothy K. & Gul, Ferdinand A. & Mian, G. Mujtaba, 2019. "Aggregate investor sentiment and stock return synchronicity," Journal of Banking & Finance, Elsevier, vol. 108(C).
    15. ALAJEKWU, Udoka Bernard & OBIALOR, Michael Chukwumee & OKORO, Cyprian Okey, 2017. "Ffect Of Investor Sentiment On Future Returns In The Nigerian Stock Market," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 17(2), pages 103-126.
    16. Bai, Chenjiang & Duan, Yuejiao & Liu, Congya & Qiu, Leiju, 2022. "International taxation sentiment and COVID-19 crisis," Research in International Business and Finance, Elsevier, vol. 63(C).
    17. Chen, Yangyang & Goyal, Abhinav & Veeraraghavan, Madhu & Zolotoy, Leon, 2020. "Terrorist attacks, investor sentiment, and the pricing of initial public offerings," Journal of Corporate Finance, Elsevier, vol. 65(C).
    18. Wang, Lu & Ma, Feng & Niu, Tianjiao & Liang, Chao, 2021. "The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market," Energy Economics, Elsevier, vol. 99(C).
    19. Seok, Sangik & Cho, Hoon & Ryu, Doojin, 2022. "Scheduled macroeconomic news announcements and intraday market sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    20. Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2018. "Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 74-87, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reveco:v:83:y:2023:i:c:p:373-382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.