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Complicated Firms


Dong Lou


London School of Economics & Political Science (LSE)

Lauren Cohen


Harvard Business School; National Bureau of Economic Research (NBER)

October 11, 2010

AFA 2011 Denver Meetings Paper

Abstract:     
We exploit a novel setting in which the same piece of information affects two sets of firms: one set of firms requires straightforward processing to update prices, while the other set requires more complicated analyses to incorporate the same piece of information into prices. We document substantial return predictability from the set of easy-to-analyze firms to their more complicated peers. Specifically, a simple portfolio strategy that takes advantage of this straightforward vs. complicated information processing classification yields returns of 117 basis points per month. Consistent with processing complexity driving the return relation, we further document that the more complicated the firm, the more pronounced the return predictability. In addition, we find that sell-side analysts are subject to these same information processing constraints, as their forecast revisions of easy-to-analyze firms predict their future revisions of more complicated firms.

Number of Pages in PDF File: 43

Keywords: Complicated trades, stand alone, conglomerate, frictions

JEL Classification: G10, G11, G14

working papers series


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Date posted: March 15, 2010 ; Last revised: November 30, 2010

Suggested Citation

Lou, Dong and Cohen, Lauren, Complicated Firms (October 11, 2010). AFA 2011 Denver Meetings Paper. Available at SSRN: http://ssrn.com/abstract=1570869 or http://dx.doi.org/10.2139/ssrn.1570869

Contact Information

Dong Lou
London School of Economics & Political Science (LSE) ( email )
Department of Finance
Houghton Street
London, WC2A 2AE
United Kingdom
+44 (0)207 1075360 (Phone)
HOME PAGE: http://personal.lse.ac.uk/loud/
Lauren Cohen (Contact Author)
Harvard Business School ( email )
Soldiers Field Road
Morgan 270C
Boston, MA 02163
United States
National Bureau of Economic Research (NBER) ( email )
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Feedback to SSRN (Beta)


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