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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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This is intentional, as while we show in the regressions there that these characteristics can moderate this complicated information processing effect on returns (which is borne out also in portfolios), we wanted to show clearly here that the complicated information processing effect was present across these characteristic sub-samples.

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  contained in pseudo-conglomerate price movements on subsequent conglomerate returns is monotonically decreasing across the weeks, from the most recent week (2.558, t=7.29) to four weeks prior (1.019, t=4.53). This is consistent with the idea that, as investors are availed of more time to process the information, more of the information is reflected in the prices of conglomerate firms.18 Finally, we also examine the long-run return pattern of this complicated information processing effect. This is mainly to examine whether the strong and positive return effect we show is some form of overreaction in conglomerate firm values, in which case we would expect to see a full reversal in the longer term. In contrast, if we are instead documenting the delayed updating of conglomerate firms to information truly important to their fundamental values, we should see no reversal following their delayed updating.

To test between these two alternative stories, we simply examine the cumulative returns to the portfolio strategy described in Table II over the longer term. We show these long-run cumulative returns in Figure 2, with both equal- and value-weighted results. The large month 1 returns in Figure 2 correspond to the portfolio returns from Table II. We then observe modest additional upward drift through month 6. More importantly, we see no sign of any return reversal. Extending the horizon to 12 (or 24) months produces largely the same results, as the return pattern flattens at months 7 to 8, and remains flat thereafter. The important conclusion from this figure is that we see no reversal of the return effects we document over the long-run, suggesting that we truly are capturing a mechanism of delayed updating of conglomerate firm prices to information important to their fundamental values.

8. Conclusion

We explore a new mechanism by which asset prices are sensitive to the complexity of information processing. We use a novel approach, that of identifying two sets of firms that require easy vs. complicated analyses to reflect the same piece of

                                                            

In addition, as we might expect, adding up the coefficients of the four past weeks gives roughly the same magnitude as the coefficient on past month pseudo-conglomerate return from Table IV.

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  information. Specifically, we look at industry-wide information events, and exploit the fact that, while it is straightforward to incorporate industry-specific information into a firm operating solely in that industry (i.e., standalone firms), it generally requires more complicated analyses to incorporate the same piece of information into the price of a firm with operating segments in multiple industries (i.e., conglomerate firms). We find strong evidence that easy-to-analyze firms incorporate industry information first, and hence their returns strongly predict the future updating of firm values that require more complicated analyses. 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, sell-side analysts exhibit these same information processing constraints, as their forecast revisions of easy-to-analyze standalone firms significantly predict the future forecast revisions of more complicated conglomerate firms.

Interestingly, these complicated firms also appear to be more difficult for investors to categorize, and being so, they do not experience the shift away from fundamental value due to industry sentiment shocks, nor do they experience the subsequent reversal back to fundamental value.

A portfolio that takes advantage of this return predictability yields significant returns — ranging from 11.4%-14% a year. These returns are virtually unrelated to previously known return determinants, robust to different specifications, across various subsets of firms, and exhibit no return reversal in the long-run. Understanding how the mechanism of complicated information processing, and how frictions to processing information more generally, can affect information updating, will give us a richer picture of how information is revealed into prices across the universe of firms, and so a deeper understanding of what drives asset prices.

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  DellaVigna, S., Pollet, J., 2006, Investor inattention, firm reaction, and friday earnings announcements, Journal of Finance 64(2), 709-749.

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This table shows summary statistics as of December of each year. Percent coverage of stock universe (EW) is the number of stocks either included in the conglomerate or standalone sample for a given year divided by the total number of CRSP stocks. Percent coverage of stock universe (VW) is the total market capitalization of stocks included in the conglomerate or standalone sample for the given year, divided by the total market value of the CRSP stock universe. Size is the firm’s market value of equity. Book to market is the Compustat book value of equity divided by the market value of equity. Market capitalization percentile and book to market percentile are measured based on the NYSE sample.

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0 0.27 1 0.36 0.29 Percent of sales per industry segment Table II: Complicated Processing Portfolios, abnormal returns 1977—2009 This table shows calendar time portfolio abnormal returns. At the beginning of every calendar month, all conglomerate stocks are ranked in ascending order on the basis of the returns of their corresponding pseudo-conglomerates in the previous month. A pseudo-conglomerate is simply a portfolio of the conglomerate firm’s industry segments constructed using solely the stand alone firms from any given industry. The ranked stocks are assigned to one of 10 decile portfolios. All stocks are value (equally) weighted within a given portfolio, and the portfolios are rebalanced every calendar month to maintain value (equal) weights. This table includes all available stocks with stock price greater than $5 at portfolio formation. Alpha is the intercept on a regression of monthly excess return from the rolling strategy. The explanatory variables are the monthly returns from Fama and French (1993) mimicking portfolios, Carhart (1997) momentum factor and Pastor and Stambaugh (2003) liquidity factor. L/S is the alpha of a zero-cost portfolio of conglomerate firms that holds the firms with the top 10% pseudo-conglomerate returns and sells short the firms with the bottom 10% pseudo-conglomerate returns in the previous month. Returns and alphas are in monthly percent, t-statistics are shown below the coefficient estimates, and 5% statistical significance is indicated in bold.

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This table reports Fama-MacBeth forecasting regressions of stock returns. The dependent variable in columns 1 and 2 is the monthly return of the conglomerate (RET), in columns 3 and 4 is the excess conglomerate return over its value-weighted industry return (RET-INDRET), while in columns 5 and 6 the dependent variable is the excess return of the conglomerate over its paired pseudo-conglomerate (RET-PCRET). A pseudo-conglomerate is simply a portfolio of the conglomerate firm’s industry segments constructed using solely the stand alone firms from any given industry. The explanatory variables are the lagged pseudo-conglomerate return (PCRET), the firm’s own lagged return (RET), and lagged return of the corresponding industry portfolio to the conglomerate’s principal industry (INDRET). All regressions also include SIZE, B/M, MOM, and TURNOVER, all of which are measured at the end of June of each year. Cross sectional regressions are run every calendar month and the time-series standard errors are adjusted for heteroskedasticity and autocorrelation (up to 12 lags). Fama-MacBeth t-statistics are reported below the coefficient estimates and 5% statistical significance is indicated in bold.

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