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Informed Trading and Portfolio Returns

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  • Alex Boulatov
  • Terrence Hendershott
  • Dmitry Livdan

Abstract

We solve a multi-period model of strategic trading with long-lived information in multiple assets with correlated innovations in fundamental values. Market makers in each asset can only condition their pricing functions on trading in each asset. Using daily non-public data from the New York Stock Exchange, we test the model's predictions on the conditional and unconditional lead--lag relations of institutional order flow and returns within portfolios. We find support for the model prediction of positive autocorrelations in portfolio returns as well as the predictions for how informed order flow positively predicts future returns and future informed order flow. We show that these relations strengthen for portfolios formed from assets within the same industry, which likely have higher correlation of fundamental values. Furthermore, we discuss issues that arise when testing implications of strategic models with imperfect proxies for the underlying strategic behaviour. Copyright , Oxford University Press.

Suggested Citation

  • Alex Boulatov & Terrence Hendershott & Dmitry Livdan, 2013. "Informed Trading and Portfolio Returns," Review of Economic Studies, Oxford University Press, vol. 80(1), pages 35-72.
  • Handle: RePEc:oup:restud:v:80:y:2013:i:1:p:35-72
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    File URL: http://hdl.handle.net/10.1093/restud/rds020
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    Cited by:

    1. Chelley-Steeley, Patricia L. & Steeley, James M., 2014. "Portfolio size, non-trading frequency and portfolio return autocorrelation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 56-77.
    2. Paolo Pasquariello & Clara Vega, 2015. "Strategic Cross-Trading in the U.S. Stock Market," Review of Finance, European Finance Association, vol. 19(1), pages 229-282.
    3. Hendershott, Terrence & Livdan, Dmitry & Schürhoff, Norman, 2015. "Are institutions informed about news?," Journal of Financial Economics, Elsevier, vol. 117(2), pages 249-287.
    4. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    5. Charles-Albert Lehalle & Charafeddine Mouzouni, 2019. "A Mean Field Game of Portfolio Trading and Its Consequences On Perceived Correlations," Papers 1902.09606, arXiv.org.
    6. Thierry Foucault & Roman Kozhan & Wing Wah Tham, 2017. "Toxic Arbitrage," Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1053-1094.
    7. Dugast, Jérôme & Foucault, Thierry, 2018. "Data abundance and asset price informativeness," Journal of Financial Economics, Elsevier, vol. 130(2), pages 367-391.
    8. Ping Wei & Xiaodan Mao & Xiaohong Chen, 2020. "Institutional investors' attention to environmental information, trading strategies, and market impacts: Evidence from China," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 566-591, February.
    9. Cespa, Giovanni & Colla, Paolo, 2016. "Market Fragmentation, Dissimulation, and the Disclosure of Insider Trades," CEPR Discussion Papers 11690, C.E.P.R. Discussion Papers.
    10. Zhang, Chris H. & Frijns, Bart, 2019. "Noise trading and informational efficiency," EconStor Preprints 198037, ZBW - Leibniz Information Centre for Economics.
    11. Dong, Xi & Feng, Shu & Ling, Leng & Song, Pingping, 2017. "Dynamic autocorrelation of intraday stock returns," Finance Research Letters, Elsevier, vol. 20(C), pages 274-280.
    12. Peter Koudijs, 2015. "Those Who Know Most: Insider Trading in Eighteenth-Century Amsterdam," Journal of Political Economy, University of Chicago Press, vol. 123(6), pages 1356-1409.
    13. Charles-Albert Lehalle & Charafeddine Mouzouni, 2019. "A Mean Field Game Of Portfolio Trading And Its Consequences On Perceived Correlations," Working Papers hal-02003143, HAL.
    14. Li, Qian & Wang, Jiamin & Bao, Liang, 2018. "Do institutions trade ahead of false news? Evidence from an emerging market," Journal of Financial Stability, Elsevier, vol. 36(C), pages 98-113.
    15. Emiliano Pagnotta, 2016. "Chasing Private Information," 2016 Meeting Papers 1673, Society for Economic Dynamics.
    16. Pierre Collin-Dufresne & Vyacheslav Fos, 2012. "Do prices reveal the presence of informed trading?," NBER Working Papers 18452, National Bureau of Economic Research, Inc.
    17. Acheson, Graeme G. & Coyle, Christopher & Turner, John D., 2018. "Prices and informed trading: Evidence from an early stock market," QUCEH Working Paper Series 2018-05, Queen's University Belfast, Queen's University Centre for Economic History.
    18. Marvin Wee & Joey W. Yang, 2016. "The Evolution of Informed Liquidity Provision: Evidence from an Order†driven Market," European Financial Management, European Financial Management Association, vol. 22(5), pages 882-915, November.
    19. Giovanni Cespa & Thierry Focault, 2011. "Learning from Prices, Liquidity Spillovers, and Market Segmentation," CSEF Working Papers 284, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    20. Anderson, Robert M. & Eom, Kyong Shik & Hahn, Sang Buhm & Park, Jong-Ho, 2013. "Autocorrelation and partial price adjustment," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 78-93.
    21. Tomy Lee, 2019. "Latency in Fragmented Markets," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 33, pages 128-153, July.
    22. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    23. Chesney, Marc & Crameri, Remo & Mancini, Loriano, 2015. "Detecting abnormal trading activities in option markets," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 263-275.

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