Do Limit Orders Alter Inferences about Investor Performance and Behavior?

Authors

  • JUHANI T. LINNAINMAA

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    • Linnainmaa is with University of Chicago Booth School of Business. I would like to thank Tony Bernardo, Michael Brennan, John Campbell, John Cochrane, Andrea Frazzini, Ron Goettler, Mark Grinblatt, Joel Hasbrouck, Eric Hughson (AFA discussant), Steve Kaplan, Matti Keloharju, Owen Lamont, Hanno Lustig, Mark Mitchell, Toby Moskowitz, Robert Novy-Marx, Ľuboš Pástor, Monika Piazzesi, Josh Rauh, Gideon Saar, Andrei Simonov, Dick Thaler, Tuomo Vuolteenaho, Ning Zhu, an anonymous referee, the associate editor, the editor (Campbell Harvey), and seminar participants at the American Finance Association 2004 Meetings, Harvard University, Helsinki School of Economics, University of Illinois at Urbana-Champaign, New York University, Stanford University, University of Chicago, Yale University, and MIT for their comments. I gratefully acknowledge financial support from the Allstate Dissertation Fellowship. I also thank Matti Keloharju for his help with the FCSD registry, Terry Odean for the U.S. discount broker data, and Shane Shepherd for valuable research assistance. Ron Goettler graciously provided the C++ code for the model used in Section V's simulations. I am also indebted to him for answering my (many) questions about the intricacies of the algorithm. Brenda Priebe and Sandra Sizer served as copy editors. All errors are mine.


ABSTRACT

Individual investors lose money around earnings announcements, experience poor posttrade returns, exhibit the disposition effect, and make contrarian trades. Using simulations and trading records of all individual investors in Finland, I find that these trading patterns can be explained in large part by investors' use of limit orders. These patterns arise mechanically because limit orders are price-contingent and suffer from adverse selection. Reverse causality from behavioral biases to order choices does not appear to explain my findings. I propose a simple method for measuring a data set's susceptibility to this limit order effect.

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