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Institutional Investors and the Information Production Theory of Stock Splits

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  • Chemmanur, Thomas J.
  • Hu, Gang
  • Huang, Jiekun

Abstract

We make use of a large sample of transaction-level institutional trading data to test an extended version of Brennan and Hughes’ (1991) information production theory of stock splits. We compare brokerage commissions paid by institutional investors before and after a split, assess the private information held by them, and relate the informativeness of their trading to brokerage commissions paid. We show that institutions make abnormal profits net of brokerage commissions by trading in splitting stocks. We also show that the information asymmetry faced by firms goes down after stock splits. Overall, our empirical results support the information production theory.

Suggested Citation

  • Chemmanur, Thomas J. & Hu, Gang & Huang, Jiekun, 2015. "Institutional Investors and the Information Production Theory of Stock Splits," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(3), pages 413-445, June.
    • Handle: RePEc:cup:jfinqa:v:50:y:2015:i:03:p:413-445_00
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    Citations

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    Cited by:

    1. Ebenezer Asem & Vishaal Baulkaran & Pawan Jain & Mark Sunderman, 2022. "Are institutional investors informed? The case of dividend changes for REITS and Industrial Firms," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1685-1707, May.
    2. Li, Fengyu & Liu, Mark H. & Shi, Yongdong (Eric), 2017. "Institutional ownership around stock splits," Pacific-Basin Finance Journal, Elsevier, vol. 46(PA), pages 14-40.
    3. Maria Chiara Iannino & Sergey Zhuk, 2020. "Signaling through Timing of Stock Splits," Discussion Paper Series, School of Economics and Finance 202009, School of Economics and Finance, University of St Andrews, revised 18 Jun 2021.
    4. Hu, Conghui & Lin, Ji-Chai & Liu, Yu-Jane, 2022. "What are the benefits of attracting gambling investors? Evidence from stock splits in China," Journal of Corporate Finance, Elsevier, vol. 74(C).
    5. Hoang, Lai T. & Wee, Marvin & Yang, Joey Wenling, 2023. "Strategic trading by insiders in the presence of institutional investors," Journal of Financial Markets, Elsevier, vol. 64(C).
    6. Li, Fengfei & Lin, Ji-Chai & Lin, Tse-Chun & Shang, Longfei, 2023. "Behavioral bias, distorted stock prices, and stock splits," Journal of Banking & Finance, Elsevier, vol. 154(C).
    7. Davis, Frederick & Khadivar, Hamed & Walker, Thomas J., 2021. "Institutional trading in firms rumored to be takeover targets," Journal of Corporate Finance, Elsevier, vol. 66(C).
    8. Hu, Gang & Jo, Koren M. & Wang, Yi Alex & Xie, Jing, 2018. "Institutional trading and Abel Noser data," Journal of Corporate Finance, Elsevier, vol. 52(C), pages 143-167.
    9. Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    10. Li, Fengfei & Lin, Chen & Lin, Tse-Chun, 2021. "Salient anchor and analyst recommendation downgrade," Journal of Corporate Finance, Elsevier, vol. 69(C).
    11. Chan, Konan & Li, Fengfei & Lin, Ji-Chai & Lin, Tse-Chun, 2017. "What do stock price levels tell us about the firms?," Journal of Corporate Finance, Elsevier, vol. 46(C), pages 34-50.
    12. Henry, Darren & Nguyen, Lily & Pham, Viet Hung, 2017. "Institutional trading before dividend reduction announcements," Journal of Financial Markets, Elsevier, vol. 36(C), pages 40-55.

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