Complicated Firms

Lauren  Cohen; Dong  Lou

Article | Journal of Financial Economics | May 2012

Complicated Firms

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 118 basis points per month. Consistent with processing complexity driving the return relation, we further show 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.

Keywords: Investment Portfolio; Information; Price; Forecasting and Prediction; Complexity; Mathematical Methods;

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Citation:

Cohen, Lauren, and Dong Lou. "Complicated Firms." Journal of Financial Economics 104, no. 2 (May 2012). (Winner of Istanbul Stock Exchange 25th Anniversary Best Paper Competition. First Prize presented by Istanbul Stock Exchange. Winner of Center for Research in Security Prices Forum. Best Paper Prize presented by University of Chicago Booth School of Business. Winner of Paul Woolley Centre for the Study of Capital Market Dysfunctionality. Academic Grant presented by Paul Woolley Centre for the Study of Capital Market Dysfunctionality. Winner of Crowell Memorial Prize For the best paper on quantitative investing presented by PanAgora Asset Management, Inc.)

Article | Management Science | December 2019

Patent Trolls: Evidence from Targeted Firms

Lauren Cohen, Umit Gurun and Scott Duke Kominers

We provide the first large-sample evidence on the behavior and impact of nonpracticing entities (NPEs) in the intellectual-property space. We find that, on average, NPEs appear to behave as opportunistic “patent trolls.” NPEs sue cash-rich firms and target cash in business segments unrelated to alleged infringement at essentially the same frequency as they target cash in segments related to alleged infringement. By contrast, cash is neither a key driver of intellectual-property lawsuits by practicing entities (e.g., IBM and Intel) nor of any other type of litigation against firms. We find further suggestive evidence of NPE opportunism: targeting of firms that have reduced ability to defend themselves, repeated assertions of lower-quality patents, increased assertion activity nearing patent expiration, and forum shopping. We find, moreover, that NPE litigation has a real negative impact on innovation at targeted firms: firms substantially reduce their innovative activity after settling with NPEs (or losing to them in court). Meanwhile, we neither find any markers of significant NPE pass-through to end innovators nor of a positive impact of NPEs on innovation in the industries in which they are most prevalent.

Keywords: Patent trolls; patents; innovation; Patents; Lawsuits and Litigation; Ethics; Innovation and Invention;

Citation:

Cohen, Lauren, Umit Gurun, and Scott Duke Kominers. "Patent Trolls: Evidence from Targeted Firms." Management Science 65, no. 12 (December 2019): 5461–5486. (Cited in the United States Federal Trade Commission Report on Patent Assertion Entities, 2016.) View Details

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We provide evidence that mutual fund managers misclassify their holdings and that these misclassifications have a real and significant impact on investor capital flows. In particular, we provide the first systematic study of bond funds’ reported asset profiles to Morningstar against their actual portfolios. Many funds report more investment grade assets than are actually held in their portfolios, making these funds appear significantly less risky. This results in pervasive misclassifications across the universe of U.S. fixed income mutual funds by Morningstar, who relies on these reported holdings. The problem is widespread—resulting in about 30% of funds being misclassified with safer profiles, when compared against their actual, publicly reported holdings. “Misclassified funds”—i.e., those that hold risky bonds but claim to hold safer bonds—outperform the actual low-risk funds in their peer groups. “Misclassified funds,” therefore, receive higher Morningstar Ratings (significantly more Morningstar Stars) and higher investor flows due to this perceived outperformance. However, when we correctly classify them based on their actual risk, these funds are mediocre performers. Misreporting is stronger following several quarters of large negative returns.

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