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«Lauren Cohen Harvard Business School and NBER Dong Lou London School of Economics This draft: June 13, 2011 First draft: February 5, 2010 ...»

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5.1 More complicated firms In this section, we examine the mechanism of complicated information processing affecting the price updating of conglomerate firms in more depth. We begin by examining conglomerate firms that are especially complicated to value. If our return effect is truly driven by investors’ limited capacity and resources, combined with the valuation difficulty of conglomerate firms, we would expect that the more complicated the firm, the more severe the lag in incorporating information into prices, and thus the stronger the return predictability. To test this prediction, we create a measure of how complicated a conglomerate firm is using a Herfindahl index based on the firm’s segment sales. For example, the Herfindahl index for the conglomerate firm in the previous section that operates in the chocolate, taco, and light bulb industries, CTB, is defined as (.4)2+(.3)2+(.3)2=0.34. The idea behind this measure is that, the more dispersed a firm’s operations across its industry segments, the more complicated the analyses needed


  to incorporate a given piece of information into its price.9 We then create a categorical variable that equals one if a conglomerate firm is above the sample median in a given year in terms of this Herfindahl measure, and zero otherwise. The prediction is thus that the coefficient on the interaction term of PCRETt-1 with this categorical variable be negative, i.e., these firms requiring less complicated information processing should have less severe return predictability.

The results of the test are reported in Column 1 of Table V. The regression specification is similar to those in Table IV, i.e., a Fama-MacBeth predictive regression with the dependent variable being the conglomerate firm return (RETt) in the following month. In addition to the interaction term between the categorical variable and PCRETt-1, the categorical variable itself along with all control variables from the full specification (Table IV, Column 2) are also included, which are unreported for brevity.

We observe from Column 1 that the coefficient estimate on the interaction term between an indicator of less complicated firms and past pseudo-conglomerate’s return (PCRETt-1) is negative and statistically significant, -3.458 (t=-3.33). For comparison, the unconditional coefficient on PCRETt-1 from Table IV is 6.896. Thus, consistent with the complexity of conglomerate firms driving the return predictability pattern, firms that are relatively less complicated, and so require simpler processing to incorporate information about any single segment into prices, exhibit less pronounced predictable returns.

5.2 Difficult-to-arbitrage firms

Even if a subset of investors are severely constrained in their information processing capacity, and therefore can cause a delay in information revelation in a set of complex-to-analyze firms, the less constrained investors (e.g., professional money managers) should take advantage of the return predictability and arbitrage away part of


We have also used the number of industry segments within a conglomerate firm as an alternative measure, and get similar results. We prefer the Herfindahl index as it captures the actual concentration of firm operations, as opposed to a simple count of industry segments. For example, consider a second conglomerate firm, CTB2, that also operates in the chocolate, taco, and light bulb industries, but receives 90% of its total revenue from the chocolate industry. Although it has three operating segments, it is actually closer to a standalone firm.


  the predictable abnormal returns. An immediate prediction of this argument is that, for stocks with more binding limits to arbitrage, we should see a stronger return effect, as more sophisticated investors are less able (or willing) to fully update these firms’ prices.

We employ two variables that are commonly used in the literature to capture limits to arbitrage in the stock market: idiosyncratic volatility and firm size. While we are not claiming these are perfect proxies, we do believe, especially in the case of idiosyncratic volatility, that these proxies are likely correlated with classic limits to arbitrage, such as the ability to retain positions (capital) in the face of prices moving (temporarily) further away from fundamental values.

To test this prediction, we construct two categorical variables that equal one if the firm is above the sample median in terms of idiosyncratic volatility and market capitalization, respectively, and zero otherwise. As shown in Column 2 of Table V, the coefficient estimate on the interaction term between the idiosyncratic volatility dummy and PCRETt-1 is large and statistically significant, 3.159 (t=2.43), which implies that the magnitude of the documented return effect is over 50% larger for stocks with high idiosyncratic volatility relative to those with low idiosyncratic volatility. This is consistent with our prediction that firms that are more likely to have large temporary price swings, and are thus less attractive to arbitrage capital, should exhibit a stronger return effect. In the same vein, Column 3 shows that, while the complicatedinformation-processing return effect among large conglomerate firms is strong and significant, the effect in small firms is even larger. Both of these findings lend support to our prediction that complications in information processing have an even larger impact on difficult-to-arbitrage stocks.

5.3 Investors’ inattention

In the final three columns of Table V, we test whether our results are entirely driven by an investors’ inattention explanation, i.e., that investors are unaware of a piece of information and/or a particular stock. While this seems unlikely, given that the industry-wide information we are identifying has already entered first into the values of smaller standalones firms, we still employ some common proxies for (in)attention to test


  this more formally. Specifically, if investors’ limited attention plays a significant role here, we would expect stronger return predictability for conglomerate firms that attract

less investor attention. We use three common proxies for inattention in the literature:

lower institutional investor ownership, lower turnover, and lower analyst coverage.

Note that institutional ownership here is the residual institutional ownership after being orthogonalized with respect to firm size.

The results are reported in Columns 4 to 6. All three interaction terms are insignificant and small in magnitude, with the coefficient on turnover even being in the wrong direction. This lends further support to our hypothesis that the return effect is driven by complications in the processing of information for conglomerate firms, and not simply by investors ignoring this underlying information and/or the underlying stocks.

5.4 Change of firm status

In this section, we perform a cleaner test of the mechanism of complicated information processing affecting firm values, by examining a particular setting where we can follow the same firm as both a standalone and a conglomerate. Specifically, we restrict our analysis to solely those standalone firms that transition to conglomerate firms, through, for example, mergers and acquisitions, and initializing new business lines. 10 Although this rather restrictive setting results in many fewer firms, the advantage of this test is that we can now examine the time lags in information updating of the exact same firm when it requires easy as opposed to complicated information processing.

The prediction is that, when the same firm operates in multiple segments, its corresponding pseudo-conglomerate should be a significant and positive predictor of its future price movements (after controlling for all other known return determinants and industry-wide return continuation). When it is a standalone firm, however, the analogous pseudo-conglomerate portfolio, which is now simply a portfolio of all other


We have examined the opposite case as well, i.e., conglomerate firms transition to standalones through divestiture, but empirically in the majority of these cases the conglomerate firm actually keeps a portion of the unit (and its facilities), and yet stops reporting the segment’s financial information separately.

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  standalones in the same industry, should have relatively weaker (or insignificant) predictability over its future returns.

We implement this test by first identifying all cases in which a standalone firm transitions into a conglomerate firm.11 We then include observations within three years prior to the status change in the standalone-status sample, and those within three years subsequent to the status change in the conglomerate-status sample. We conduct FamaMacBeth return predictive regressions, similar to those in Table IV, on both samples.

The results are reported in Table VI. Comparing Columns 2 and 4 (with a stricter specification where the dependent variable is RETt-PCRETt), we observe that PCRETt-1 has no predictability over excess returns when a firm is a standalone (0.581, t=1.08), but, in contrast, has significant return predictability when the same firm is a more complicated conglomerate firm (3.206, t=2.71). The difference between these two coefficients of 2.626 is significant at the 5% level (t=2.12). 12 Also, note that the coefficients on PCRETt-1 in Columns 3 and 4 (when the given firm is a conglomerate) are quite similar to those based on the universe of conglomerate firms, reported in Columns 1 and 5 of Table IV, respectively. This suggests that there is nothing unusual about these conglomerate firms that have recently changed status, relative to all other conglomerates, in terms of complications in information processing.

5.5 Analyst information updating in complicated firms

All the results we have presented to this point are consistent with two interpretations. The first interpretation, which we focus on in this paper, is a complicated information processing mechanism, in which investors have limited capacity to assess how a given piece of industry-specific information can affect a complicated firm’s value that comprises of a number of industry segments, each with a distinct yet


We exclude all such cases in years 1998 and 1999, which are likely due to a significant change in reporting requirements corresponding to the introduction of SFAS No. 131, which superseded No. 14.

Columns 1 and 3 show that, while there is some autocorrelation in standalone firm returns (from Column 1), the same result holds. The difference of 3.570 between Columns 1 and 3 is statistically significant (t=2.22). This suggests that the same firm’s future price movements are significantly more related to the past pseudo-conglomerate returns when it is a complicated conglomerate firm, as opposed to a simple standalone firm.


  unknown weight. The second explanation is a complicated trading channel, where even if investors knew the exact weights of individual segments, and how a given piece of information about a single segment would affect the complicated firm’s value, it might still be difficult for them to undertake the complex set of trades needed to get this information into prices. For instance, consider again the three segment conglomerate firm CTB, Inc. If information arrives about one of the industries (e.g., chocolate), in the absence of information about the other two segments, and given that one does not want to bear the information risk of these other segments, one would have to long the conglomerate firm, and then put on a series of trades to hedge out the risk of the other two segments.

While it could certainly be that both explanations, complicated information processing and complicated trading, are present in driving these price patterns, in this section, we present a test that helps distinguish between the two. Specifically, we examine the behavior of sell-side analysts who usually cover both simple- and complicated-to-analyze firms. On the one hand, analysts are constrained to only issue forecasts for an entire firm rather than its individual segments, and thus face the same complexity as an average investor in incorporating information about a single segment into conglomerate firm values. On the other hand, since analysts do not have to undertake any hedging trades in their forecasts, they are completely free of the complicated trading friction. Thus, if it is mainly the complicated information processing mechanism that is driving our results, we would expect to see a similar predictive pattern in analyst forecasts between simple standalone and complicated conglomerate firms, assuming that analysts also have limited information processing capacity. On the flip side, if it is the complicated trading channel that is driving our results, we should see no such effect in analyst behavior.

We test these predictions using sell-side analysts’ annual earnings forecasts, as these forecasts are updated most frequently and thus afford us the most statistical power. We conduct a regression analysis that is almost identical to those performed in Table IV expect that, instead of using stock returns, we focus on monthly revisions in consensus forecasts for the subsequent annual earnings announcements. Thus, we test whether analysts’ forecast revisions for simple standalones firms, which we now

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  aggregate into a measure labeled the pseudo-conglomerate forecast (PCFt-1), predict future forecast revisions of their corresponding complicated conglomerate firms (Ft).

The tests are shown in Table VII. The results imply that analysts are affected by similar information processing complications as investors and thus update their forecasts for simple standalone firms before these more complicated conglomerate firms.

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