IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v54y2023ics1544612323000995.html
   My bibliography  Save this article

Aggregate insider trading in the S&P 500 and the predictability of international equity premia

Author

Listed:
  • Guettler, Andre
  • Hable, Patrick
  • Launhardt, Patrick
  • Miebs, Felix

Abstract

We show that aggregate insider trading (AIT) in the S&P 500 is a reliable predictor of the U.S. equity premium, while AIT outside the S&P 500 seems to be uninformative. In an international setting, we find that AIT based on S&P 500 insiders predicts international equity premia. Contrary to our U.S. based measure of AIT, we do not find any predictive content of domestic AIT for international equity premia. The informational content of AIT of S&P 500 insiders for U.S. and international equity premia stems from the insiders’ ability to forecast cash flow news in- and outside the U.S.

Suggested Citation

  • Guettler, Andre & Hable, Patrick & Launhardt, Patrick & Miebs, Felix, 2023. "Aggregate insider trading in the S&P 500 and the predictability of international equity premia," Finance Research Letters, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323000995
    DOI: 10.1016/j.frl.2023.103725
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323000995
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.103725?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. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    3. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    4. John Y. Campbell & Robert J. Shiller, 1988. "Stock Prices, Earnings and Expected Dividends," Cowles Foundation Discussion Papers 858, Cowles Foundation for Research in Economics, Yale University.
    5. Xavier Gabaix, 2011. "The Granular Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 79(3), pages 733-772, May.
    6. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    7. Lakonishok, Josef & Lee, Inmoo, 2001. "Are Insider Trades Informative?," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 79-111.
    8. Lauren Cohen & Christopher Malloy & Lukasz Pomorski, 2012. "Decoding Inside Information," Journal of Finance, American Finance Association, vol. 67(3), pages 1009-1043, June.
    9. H. Nejat Seyhun, 1992. "Why Does Aggregate Insider Trading Predict Future Stock Returns?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(4), pages 1303-1331.
    10. repec:bla:jfinan:v:43:y:1988:i:3:p:661-76 is not listed on IDEAS
    11. Hjalmarsson, Erik, 2010. "Predicting Global Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(1), pages 49-80, February.
    12. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    13. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2013. "International Stock Return Predictability: What Is the Role of the United States?," Journal of Finance, American Finance Association, vol. 68(4), pages 1633-1662, August.
    14. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    15. Martin J. Conyon & John E. Core & Wayne R. Guay, 2011. "Are U.S. CEOs Paid More Than U.K. CEOs? Inferences from Risk-adjusted Pay," The Review of Financial Studies, Society for Financial Studies, vol. 24(2), pages 402-438.
    16. 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.
    17. Li, Yan & Ng, David T. & Swaminathan, Bhaskaran, 2013. "Predicting market returns using aggregate implied cost of capital," Journal of Financial Economics, Elsevier, vol. 110(2), pages 419-436.
    18. Darren T. Roulstone, 2003. "The Relation Between Insider‐Trading Restrictions and Executive Compensation," Journal of Accounting Research, Wiley Blackwell, vol. 41(3), pages 525-551, June.
    19. Nicolas Crouzet & Neil R. Mehrotra, 2020. "Small and Large Firms over the Business Cycle," American Economic Review, American Economic Association, vol. 110(11), pages 3549-3601, November.
    20. Seyhun, H Nejat, 1988. "The Information Content of Aggregate Insider Trading," The Journal of Business, University of Chicago Press, vol. 61(1), pages 1-24, January.
    21. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    22. Ahern, Kenneth R., 2017. "Information networks: Evidence from illegal insider trading tips," Journal of Financial Economics, Elsevier, vol. 125(1), pages 26-47.
    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. Atanasov, Victoria, 2018. "World output gap and global stock returns," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 181-197.
    2. David Haab & Thomas Nitschka, 2017. "Predicting returns on asset markets of a small, open economy and the influence of global risks," Working Papers 2017-14, Swiss National Bank.
    3. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    4. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    5. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    6. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    7. Arseny Gorbenko & Marcin Kacperczyk, 2023. "Short Interest and Aggregate Stock Returns: International Evidence," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(4), pages 691-733.
    8. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    9. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    10. repec:zbw:bofrdp:2016_029 is not listed on IDEAS
    11. repec:zbw:bofrdp:2017_001 is not listed on IDEAS
    12. Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
    13. repec:zbw:bofrdp:2018_007 is not listed on IDEAS
    14. Victoria Atanasov & Stig V. Møller & Richard Priestley, 2020. "Consumption Fluctuations and Expected Returns," Journal of Finance, American Finance Association, vol. 75(3), pages 1677-1713, June.
    15. Huang, Dashan & Li, Jiangyuan & Wang, Liyao, 2021. "Are disagreements agreeable? Evidence from information aggregation," Journal of Financial Economics, Elsevier, vol. 141(1), pages 83-101.
    16. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.
    17. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
    18. 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.
    19. Chen, Yong & Da, Zhi & Huang, Dayong, 2022. "Short selling efficiency," Journal of Financial Economics, Elsevier, vol. 145(2), pages 387-408.
    20. Atanasov, Victoria, 2021. "Unemployment and aggregate stock returns," Journal of Banking & Finance, Elsevier, vol. 129(C).
    21. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
    22. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    23. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.

    More about this item

    Keywords

    Equity risk premium; Aggregate insider trading; Predictive regression; Informed traders;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    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:finlet:v:54:y:2023:i:c:s1544612323000995. 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/frl .

    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.