«Lauren Cohen Harvard Business School and NBER Dong Lou London School of Economics This draft: June 13, 2011 First draft: February 5, 2010 ...»
Harvard Business School and NBER
London School of Economics
This draft: June 13, 2011
First draft: February 5, 2010
We would like to thank Ulf Axelson, Malcolm Baker, Nick Barberis, John Campbell, Josh Coval, Kent
Daniel, Darrell Duffie, Cam Harvey, Kewei Hou, Alan Huang, Jennifer Huang, Byoung-Hyoun Hwang, Owen Lamont, Chris Malloy, David McLean, Christopher Polk, Jeremy Stein, Sheridan Titman, Dimitri Vayanos, and seminar participants at Duisenberg School of Finance, Harvard Business School, London School of Economics, University of North Carolina at Chapel Hill, University of Texas at Austin, PanAgora Asset Management, SAC Capital, State Street Global Advisors, the 2011 Center for Research in Security Prices (CRSP) Forum, Inaugural Miami Behavioral Finance Conference, Istanbul Stock Exchange (ISE) 25th Anniversary Conference, the 2011 American Finance Association Meetings in Denver, the 2010 European Finance Association Meetings in Frankfurt, and the 2011 Nomura Global Equity Conference in London for helpful comments and suggestions. We thank David Kim for excellent research assistance. We are also grateful to Nomura International PLC for providing transaction cost data. We are grateful for funding from the National Science Foundation, the Paul Woolley Center, and INQUIRE UK. In addition, this research paper was awarded the Best Paper Prize in the ISE 25th Anniversary Best Paper Competition (2010), whom we also thank for the associated funding.
ABSTRACTWe 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.
JEL Classification: G10, G11, G14.
Key words: Complicated processing, return predictability, standalone, conglomerate, market frictions.
1. Introduction In some form, most asset pricing models have agents collect, interpret, and trade on information, continuing until prices are updated to fully reflect available information.
Understanding which frictions prevent these information revelation mechanisms from working properly not only furthers our empirical grasp of information flows in financial markets, but also provides a more solid base for theoretical frameworks of information diffusion. In this paper, we quantify how frictions in the processing of information impact the way information is incorporated into firm values. To do this, we use a novel approach of taking two sets of firms that are both subject to the same information shocks. The only difference is that one set of firms requires more complicated information processing to impound the same piece of information into prices than the other. Using this straightforward vs. complicated information processing classification, we show that frictions and constraints that impede information processing can result in substantive predictability in the cross section of asset prices.
To be more specific, we examine information events that affect an entire industry.
We then exploit the fact that, while it is relatively straightforward to incorporate industry-specific information into a firm operating solely in that industry (i.e., a standalone firm), it generally requires a set of more complicated analyses to impound the same piece of information into the price of a firm with multiple operating segments (i.e., a conglomerate firm). For instance, imagine new research suggests that chocolate increases life expectancy. To incorporate this information into the price of a focused chocolate producer, Chocolate Co., would be a straightforward and unambiguous task, as the firm only receives revenues from making chocolate. In contrast, to incorporate this positive chocolate industry shock into the price of a conglomerate firm that makes chocolate, tacos, and light bulbs (call it CTB Inc.) would be more difficult, as the percentage of aggregate revenues contributed by each industry segment varies over time, and thus requires an increased amount of research and processing capacity.
This paper simply posits that given investors’ limited processing and capital capacity, complexity in information processing can lead to a significant delay in the impounding of information into asset prices. More specifically, we predict that the
positive information about the chocolate industry be reflected in the prices of these easy-to-analyze firms (e.g., Chocolate Co.) first, which will therefore predict the future updating of the same piece of information into the prices of their more complicated peers (e.g., CTB, Inc.).
To test for the return effect induced by complications in information processing, we implement the following simple portfolio strategy. For each conglomerate firm we construct a “pseudo-conglomerate” (PC) that consists of a portfolio of the conglomerate firm’s segments made up using only standalone firms from the respective industries. So, for the example of the conglomerate firm above (CTB, Inc. - chocolate, tacos, light bulbs), assume that chocolate makes up 40% of its sales, tacos make up another 30%, and light bulbs make up the remaining 30%. CTB’s corresponding “pseudoconglomerate” would then be: 0.4*(a portfolio of all chocolate standalones) + 0.3*(a portfolio of all taco standalones) + 0.3*(a portfolio of all light bulb standalones).
We can easily calculate the performance of each pseudo-conglomerate by aggregating the value-weighted average returns of the standalone firms within each of the conglomerate firm’s industries. As these pseudo-conglomerates are composed of (relatively) easy-to-analyze firms subject to the same industry shocks, their prices should be updated, and thus reflect the information, first. Consequently, the returns of these pseudo-conglomerate portfolios should predict the future updating to the same information shocks - i.e., future returns - of their paired conglomerate firms.
We then sort conglomerate firms into decile portfolios based on lagged returns of their corresponding pseudo-conglomerates, and find strong evidence that complexity in information processing can cause significant return predictability in the cross-section of stocks. Specifically, a portfolio that goes long in those conglomerate firms whose corresponding pseudo-conglomerates performed best in the prior month and goes short in those conglomerate firms whose pseudo-conglomerates performed worst in the prior month, has value-weighted returns of 95 basis points (t=3.18) in the following month.
For the analogous equal-weighted portfolio, the returns are 118 basis points per month (t=5.51). Both results are virtually unaffected when controls for size, book-to-market, past returns, and liquidity are included. Further, we observe no sign of any return
reversal in the future. This robust return pattern helps confirm that we truly are capturing a mechanism of delayed updating of conglomerate firm prices to information important to their fundamental values.
Note a few important things about these complicated-processing portfolios that distinguish our findings from prior research. This is not a traditional momentum effect in the sense that the return of the same stock or portfolio (e.g., industry) predicts itself, as our strategy relies on the returns of one set of firms being able to predict the price movements of an entirely different set of firms. More specifically, our findings are not driven by the industry momentum effect identified by Moskowitz and Grinblatt (1999), as our results remain highly significant even after applying various controls for past value-weighted industry returns.1 Nor are our findings consistent with a pure investor inattention story. We show that industry-specific shocks are updated into the prices of smaller firms (e.g., focused firms) first, and only then into the prices of larger conglomerate firms. In fact, this is the only anomaly in which, to our knowledge, predictability flows from smaller to larger firms, and so is unique in this sense. Lastly, our calendar-timer portfolio strategy trades only in conglomerate firms (larger firms on average), so liquidity and other microstructure issues have nearly no impact on our portfolio results.
To explore the mechanism in more depth, if our findings are truly driven by complications in information processing impeding material information from being impounded into conglomerate firm values, we would expect that the more complicated analyses that are required, the more severe the delay in incorporating information. We find strong support for this prediction in the data. Specifically, we show that the more diversified a firm’s operations are across industries (measured by a Herfindahl index), thus requiring more complicated analyses to incorporate information about any single industry segment into conglomerate prices, the more pronounced the return predictability.
The horizon of our return effect is also different from the industry momentum effect. While the return effect we document is large in the first month after portfolio formation and does not reverse subsequently, industry momentum continues for a year and reverses significantly starting in year two.
In a cleaner way, we perform a test looking at the exact same firms that switch status. Specifically, we look at standalones that transition to conglomerate firms.
Although we have significantly fewer firms in the test, the advantage of this test is that we can examine information updating of the exact same firm when it requires easy vs.
complicated information processing. The prediction is that when the same firm is a conglomerate, its corresponding pseudo-conglomerate’s returns should be a stronger predictor of its future price movements than when it is a standalone. Consistent with this prediction, we find that the exact same firms have significantly predictable abnormal returns from their paired pseudo-conglomerates when they are conglomerate firms, but not when they are focused firms.
Our documented return patterns thus far are generally consistent with two interpretations: i) a complicated information processing channel, and ii) a complicated trading mechanism, where even if investors knew the exact weights of individual segments, and how a given piece of information would affect the complicated firm’s value, it might still be difficult for them to undertake the complex set of trades needed to impound this information into the price. For instance, in the case of CTB Inc., if good information comes out about the chocolate industry, in the absence of information about tacos and light bulbs, and given that investors do not want to bear the information risk of these other segments, they would have to long the conglomerate (CTB Inc.), and then put on a series of trades to hedge out the risk of the other two segments. To distinguish between the two hypotheses, we examine the behavior of sellside analysts on these same two sets of firms; while analysts may be subject to the same information processing constraints, they are not subject to any complicated trading frictions. We find evidence that analyst forecast revisions of focused firms significantly predict future forecast revisions of complicated firms, consistent with complicated information processing being the driving factor behind our documented patterns.
In a related vein, we examine the impact of this same complication on the transmission of non-fundamental shocks. Specifically, building upon prior evidence on categorical thinking, we test whether the complication in information processing is also a friction to categorization (i.e., complicated firms are more difficult to categorize than simple firms). Given that sentiment has been shown to often act at the level of
categories, if complicated firms are more difficult to categorize, this would predict that sentiment-related return shocks should affect simple-to-analyze firms and complicatedto-analyze firms in different ways. We find evidence consistent with this prediction. In particular, we document that difficult to categorize firms are not subject to the shift away from fundamental value due to industry-wide sentiment, nor do they experience the subsequent reversal back to fundamental value. This is consistent with frictions to categorization (i.e., being a conglomerate firm) preventing complicated firms from being categorized, and thus from being subject to the same sentiment shocks as easy to categorize firms.