Conglomerate Industry Choice and Product Differentiation

We use text-based computational analysis of business descriptions from 10-Ks to examine in which industries conglomerates are most likely to operate and to understand conglomerate valuations. We find that conglomerates are more likely to operate in industry pairs that are closer together in the product space and in industry pairs that have profitable opportunities "between" them. Conglomerate firms have lower stock market valuations than matched single-segment firms when their products are easier to replicate with single-segment firms. Conglomerate firms have stock market premiums when they have higher product differentiation and produce in more profitable industries. These findings are consistent with successful conglomerate firms having higher product differentiation and lower cost entry into profitable markets when operating in strategically chosen industry pairs.

Text-Based Network Industries and Endogenous Product Differentiation

We study how firms differ from their competitors using new time-varying measures of product similarity based on text-based analysis of firm 10-K product descriptions. This year-by-year set of product similarity measures allows us to generate a new set of industries in which firms can have their own distinct set of competitors. Our new sets of competitors explain specific discussion of high competition, rivals identified by managers as peer firms, and changes to industry competitors following exogenous industry shocks. We also find evidence that firm R&D and advertising are associated with subsequent differentiation from competitors, consistent with theories of endogenous product differentiation

Real and Financial Industry Booms and Busts

We examine how product market competition affects firm cash flows and stock returns in industry booms and busts. In competitive industries, we find that high industry-level stock-market valuation, investment and new financing are followed by sharply lower operating cash flows and abnormal stock returns. We also find that analyst estimates are positively biased and returns comove more when industry valuations are high in competitive industries. In concentrated industries these relations are weak and generally insignificant. Our results suggest that when industry stock-market valuations are high, firms and investors in competitive industries do not fully internalize the negative externality of industry competition on cash flows and stock returns.

Shocks to Product Networks and Post-Earnings Announcement Drift

This paper examines whether shocks to less visible product market peers are an important determinant of industry level post-earnings announcement drift (IPEAD) (Ayers and Freeman 1997; Hui et al. 2016). On the real-side, we find that a focal firm’s earnings are persistently related to the earnings surprises of its peers. On the financial-side, IPEAD arises only when these peers are less visible and when shocks are driven by persistent supply-side shocks to expenses, and not by demand-side shocks to sales. Text-based measures of disclosure opacity show that IPEAD is also stronger when firms provide less informative 10-K disclosures regarding their expenses. Collectively, our results suggest that inattention to less visible peers and a poor informational environment surrounding supply-side shocks are likely channels that generate IPEAD. IPEAD returns are economically large in subsamples motivated by this explanation

Conglomerate Industry Spanning

ABSTRACT We use text-based analysis of business descriptions from 10-Ks filed with the SEC to examine in which industries conglomerates are most likely operate and to understand conglomerate valuations. We find that conglomerates are most likely to operate in industry pairs that are closer together in the product space and in industry pairs that have profitable opportunities "between" them. Examining cross-sectional conglomerate valuations, we find that conglomerates that are more difficult to reconstruct using text-analysis of firm pure plays tend to trade at modest premia. The conglomerates that are most easy to replicate trade at small discounts relative to matched pure-play firms. These findings are consistent with conglomerate firms generating product synergies when producing in related profitable industries

Text-Based Industry Momentum

ABSTRACT We test the hypothesis that product market shocks to less-visible textbased industry peers can explain momentum and long-term reversals. We examine industry peer firms identified through common product text and focus on less-visible industry peers that do not share common SIC codes. Shocks to less visible peers generate economically large momentum profits, and are stronger than own-firm momentum variables. More visible traditional SICbased peers generate only small, short-lived momentum profits. Subsequent long-term reversals only occur when the return-differential between more and less visible peers becomes large. Our findings suggest that momentum is driven by inattention to less visible horizontal peers. * University of Southern California and University of Southern California and National Bureau of Economic Research, respectively. Hoberg can be reached at [email protected] and Phillips can be reached at [email protected]. We thank Michael Cooper, Dongmei Li, Peter MacKay, Oguz Ozbas, Jiaping Qiu, Merih Sevilir, Albert Sheen, Denis Sosyura and seminar participants at the CFEA conference, Duisenberg School of Finance and Tinbergen Institute, Erasmus University, Hebrew University, Interdisciplinary Center of Herzilia, McMaster University, Rotterdam School of Management, Stanford University, Tel Aviv University, Tilberg University, University of Chicago, University of Illinois, the University of Mannheim, the University of Miami and the University of Utah for helpful comments. All errors are the authors alone. Copyright c 2014 by Gerard Hoberg and Gordon Phillips. All rights reserved. Since Jegadeesh and Titman (1993) Using less visible industry peer firms, we find that industry momentum profits are significant and 2-3x larger than momentum based on own-firm returns. These momentum profits slowly reverse over three year horizons, especially when less visible peer returns outpaced those of more visible peers. Most relevant to our paper are earlier contributions by Moskowitz and Grinblatt (1999) (MG), Hong and Stein (1999) and Barberis, Shleifer, and Vishny (1998). MG propose that industry momentum is a key driver of firm-level momentum. Hong and Stein (1999) and Barberis, Shleifer, and Vishny (1998) suggest that inattention or slow-moving information might also be a key driver of momentum. Our results are consistent with inattention-driven slow moving information following industry shocks triggering momentum profits in localized product markets. Our results support the following interpretation of momentum profit cycles. Initially, the market underreacts to large shocks to economically linked firms. This underreaction is more severe when economic links between the shocked firms are less visible. These momentum returns partially reverse over three years, but only when the average return of less visible industry peers outpaces the average return of more visible industry peers. This for example can occur when investors trade based on 1 Rouwenhorst (1998) and Rouwenhorst (1999) further show that momentum exists around the world, and Gebhardt, Hvidkjaer, and Swaminathan (2005) show that it also spills over into bond markets. 1 simple price-change strategies alone, as modeled by Hong and Stein (1999). Our findings run counter, in part, to recent evidence in the literature. For example, MG's conclusion that industry momentum matters was called into question by Grundy and Martin (2001) (GM), who show that industry momentum is not robust to the bid-ask bounce, and to lagging the portfolio formation period by one month. To underscore this point, Jegadeesh and Titman (2011) highlight GM's findings in their recent review and conclude that industry momentum cannot explain the momentum anomaly. The authors conclude that momentum profits likely "arise because of a delayed reaction to firm-specific information". The conclusion in the literature that industry momentum matters little is based on using highly visible traditional SIC-based industry links. We show that this long-standing conclusion is reversed when less visible text-based industry links are used to re-test the industry momentum hypothesis. In particular, own-firm momentum is less important once we control for less visible industry momentum. Our revised conclusion is robust to the GM critique. Our conclusion is that momentum due to less visible text-based industry links is more important than both own-firm momentum, and momentum due to more visible SIC-based industry links. Our findings thus support the growing consensus among some scholars (see for example Barberis, Shleifer, and Vishny (1998), Hong and Stein (1999), Jegadeesh and Titman (2011)) that market inefficiencies such as inattention likely play a strong role in generating momentum. Our findings also suggest that systematic risk models likely cannot explain momentum. 2 Recent work by Cohen and Frazzini (2008) and Menzley and Ozbas (2010) sug-2 Systematic risk models, which require transparency for equilibrium pricing, predict that links with more visibility should generate stronger risk premia. Investors need to be aware of risk loadings in order to price them in equilibrium. Risk models also require that systematic shocks are pervasive and difficult to diversify. In conflict with these predictions, we instead find that less visible links matter more than highly visible links, and we also find that momentum is most priced when shocks are localized, unrelated to risk factors, and thus easier to diversify. Griffin, Ji, and Martin (2003) also suggest that systematic risk likely cannot explain momentum through a different test (the absence of a business cycle effect). 2 gests that inattention also plays a role in generating predictable returns following shocks to vertically linked firms. We focus on horizontal industry shocks and not vertical shocks, and our objective is to address the industry momentum literature. Controls for vertical shocks do not materially affect our results. Furthermore, only our less visible horizontal industry shocks, and not vertical shocks, can explain ownfirm momentum and ex post reversals. The finding that vertical and horizontal peers contain distinct information is expected as horizontal economic links overlap little with vertical links, as reported in Hoberg and Phillips (2015). We also assess the extent to which peer shocks transmit slowly. We find that SIC peer shocks (which are highly visible) transmit quickly within just two months. In contrast, shocks to TNIC peers (which are less visible as they were not publicized during our sample) predict subsequent returns up to twelve months. For the standard one-year momentum horizon, neither own-firm momentum nor SIC-peer momentum remains statistically significant when less visible TNIC peer momentum variables are included in standard Fama-Macbeth return regressions. Our results are consistent with the momentum literature in terms of the duration of momentum profits being roughly 12 months. They are also consistent with the long-term reversals literature, as we find that long-term TNIC momentum reversals at 36 month horizons. These long horizons explain why our results are not driven by the existing short-horizon finding that large firm returns lead small firm returns especially within-industry (see Hou The visibility channel predicts that momentum should be stronger following shocks to specific peers that are less visible to the investment community. Especially following the publication of MG, SIC peers were highly visible to investors. SIC peers were also widely reported in financial databases, financial reports, regulatory disclosures and online data resources. However, TNIC peer data was not widely 3 distributed during our sample and the first paper illustrating TNIC peers (Hoberg and Phillips (2010a)) was published late in our sample. 3 Because TNIC and SIC measure the same primitive of horizontal relatedness, we consider TNIC peers that are not SIC peers to examine the role of visibility. For each firm, we compute "disparity" as the fraction of TNIC peers that are not also SIC peers. We predict and find that shocks to TNIC peers that are not SIC peers, and shocks in high disparity product markets, generate the strongest momentum returns. Further supporting a role for inattention, our results for TNIC momentum are stronger when fewer mutual funds jointly own the economically linked firms in a given TNIC industry. This test, first used by Cohen and Frazzini (2008) to examine customer-supplier links, identifies a more specific mechanism for inattention. Our results suggest that momentum is stronger when fewer professional investors (mutual fund managers) are paying attention to our less-visible economically linked firms, as they are not in their portfolios. Under the inattention hypothesis, a further prediction is that highly visible systematic shocks should decay more quickly than idiosyncratic shocks, which are localized and less visible. Alternative risk-based theories would predict that returns should be more linked to systematic shocks. We find that only idiosyncratic shocks transmit slowly and generate momentum. These findings are consistent with inattention and not systematic risk-based explanations. The spatial nature of TNIC industries allows us to examine if momentum is related to the breadth of various shocks. We define broad shocks as those that impact a larger set of related firms that are more distant in the product market space, whereas localized shocks affect only a small number of proximate firms. We find that local TNIC peers calibrated to be as fine as the SIC-4 classification generate strong momentum returns, as do TNIC peers that are calibrated to be as fine as the 3 Publication dates of academic articles pertaining to predictable stock returns are relevant, as Mclean and Pontiff (2014) find evidence that anomalies attenuate after such publication, perhaps due to increased attention. 4 SIC-3 network. However, broader TNIC peers, calibrated to be as coarse as the SIC-2 industry network do not generate additional momentum profits. The result is that only 2% of all firm pairs are needed to explain industry momentum, further suggesting momentum is idiosyncratic and localized in the product market. This is further in conflict with systematic risk-based explanations. We examine various momentum horizon variables to further assess the findings of JT and MG, and to test for ex-post reversals as in Debondt and Thaler (1985). Using the standard 11-month momentum horizon, we find that neither own-firm momentum, nor SIC-peer momentum, is statistically significant when the less visible TNIC peer momentum variables are included in standard Fama-Macbeth return regressions. Moreover, the economic magnitude of TNIC peer momentum profits is considerably larger. Our results are also strong for six month horizons. Hong and Stein (1999) and Barberis, Shleifer, and Vishny (1998) predict that momentum profits eventually reverse when excessive trend chasing drives prices beyond fundamentals (overreaction). We find such reversals for 36 month horizons, and further examine their foundations. Our earlier findings suggest that the market inefficiently prices shocks to less visible peers, but efficiently prices shocks to more visible peers. Hence we measure overreaction by examining the extent to which less visible peers have more extreme reactions relative to more efficiently priced high visibility peers. We sort firms into quintiles based on the ratio of past three-year cumulative returns of less visible peers to that of more visible SIC-based peers and find that reversals only occur when this ratio becomes high, consistent with overreaction to less visible peer returns following episodes of momentum. The paper is organized as follows. Section I discusses the related literature and hypotheses, and Section II describes our data and methods. In Section III, we present summary statistics and results regarding comovement and short-term lagged information dissemination. Section IV considers long-term momentum and Section V examines long-term reversals. Section VI concludes. 5 I Hypotheses In this section, we formalize our predictions through four central hypotheses. Our predictions match those of the theoretical models by Hong and Stein (1999) and Barberis, Shleifer, and Vishny (1998). However, we further predict that the specific mechanism driving inattention momentum is less visible industry links through which large price shocks need to propagate. Hypothesis H1: Industry momentum arises from underreaction to shocks affecting groups of peer firms with less-visible economic links. Hypothesis H2: Past returns of less visible industry peers will be stronger than the past returns of highly visible peers in simultaneous regressions predicting future returns. Momentum profits from less visible peer shocks should also be economically larger than those from highly visible peers shocks. Hypothesis H3: Momentum profits should be largest following idiosyncratic shocks, given investors pay less attention to these shocks. Profits should be smaller following more visible systematic shocks. Hypothesis H4: At longer horizons, as the market corrects initial underreaction, long-term reversals should only occur in subsamples where the cumulative return differential between less visible and more visible industry peers becomes high. Hypotheses H1 to H3 are direct implications of inattention to economic shocks to economically related firms. The intuition behind H4 is that longer-term inattention to the same group of peers that initially led to underreaction can lead to subsequent overreaction as inattention impairs the market's ability to determine when the shock has fully transmitted. This logic is consistent for example with that of the model in Hong and Stein (1999). We test hypotheses H1 to H3 using horizons up to one year. We then examine H4 using longer horizons up to three years. Our use of less visible TNIC peers and highly visible SIC peers that measure the same fundamental concept of industry relatedness, but with different levels of visibility to investors, 6 provides a way to examine these hypotheses. II Data and Methods The methodology we use to extract 10-K text follows Hoberg and Phillips (2015). The first step is to use web crawling and text parsing algorithms to construct a database of business descriptions from 10-K annual filings from the SEC Edgar website from 1996 to 2011. We search the Edgar database for filings that appear as "10-K," "10-K405," "10-KSB," or "10-KSB40." The business descriptions appear as Item 1 or Item 1A in most 10-Ks. The document is then processed using APL to extract the business description text and the company identifier, CIK. Business descriptions are legally required to be accurate, as Item 101 of Regulation S-K requires firms to describe the significant products they offer, and these descriptions must be updated and representative of the current fiscal year of the 10-K. We use the Wharton Research Data Service (WRDS) SEC Analytics product to map each SEC CIK to its COMPUSTAT gvkey on a historical basis. We require that each firm has a valid link from the 10-K CIK to the CRSP/Compustat merged database, and it must also have a valid CRSP permno in order to remain in our database. Our focus is therefore on publicly traded firms in the CRSP database. Our primary database of monthly firm returns is thus based on the CRSP monthly returns database. Because our 10-K data begins with fiscal years ending in 1996, after using the lag structure advocated in Davis, Fama, and French (2000), our starting point is the CRSP monthly returns database beginning in July 1997 and ending in December 2012. A Asset Pricing Variables We construct size and book to market ratio variables following Davis, Fama, and French (2000) and Fama and French (1992). Market size is the natural log of the 7 CRSP market cap. Following the lag convention in the literature, we use size variables from each June, and apply them to the monthly panel to use to predict returns in the following one year interval from July to June. The book-to-market ratio is based on CRSP and Compustat variables. The numerator, the book value of equity, is based on the accounting variables from fiscal years ending in each calendar year (see Davis, Fama, and French (2000)) for details). We divide each book value of equity by the CRSP market value of equity prevailing at the end of December of the given calendar year. We then compute the log book to market ratio as the natural log of the book value of equity from Compustat divided by the CRSP market value of equity. Following standard lags used in the literature, this value is then applied to the monthly panel to predict returns for the one year window beginning in July of the following year until June one year later. For each firm, we compute our momentum variable as the stock return during the eleven month period beginning in month t − 12 relative to the given monthly observation to be predicted, and ending in month t − 2. This lag structure that avoids month t − 1 is intended to avoid contamination from microstructure effects, such as the well-known one-month reversal effect. After requiring that adequate data exist to compute the aforementioned asset pricing control variables, and requiring valid return data in CRSP and also a valid link to 10-K data from Edgar, our final sample has 811,672 observations. B Industry Momentum Variables The variables we focus on are based on the return of peer firms residing in related product markets relative to a given firm (henceforth the focal firm). The central question is whether shocks to the related firms generates comovement, and more interesting, if the shocks disseminate slowly and thus entail prolonged return predictability and subsequent reversals. 8 We consider simultaneously-measured monthly returns of product market peers. For text-based industries, we compute the equal weighted average of the simultaneous monthly stock returns of TNIC industry peers (excluding the focal firm itself). Similarly, for traditional SIC-3 industry returns, we compute the average simultaneous monthly stock return of SIC-3 industry peers (excluding the focal firm itself). Our central prediction regarding simultaneous returns is that the return of the focal firm will comove more with the return of its product market peers when the peers are less visible. Because they were not publicized during our sample, text-based industry peers are less visible than SIC peers, which were widely available to investors during our sample. The more interesting question is whether the implied price impact of product market shocks is disseminated with delay. In our first series of tests, we focus on the monthly return of TNIC and SIC peers as independent variables, and we examine their relationship with ex-post own firm returns using various lags. This test assesses whether lagged monthly returns from more and less visible product market peers predicts monthly ex-post focal firm returns. In a second series of tests, we expand the holding period horizon of the ex-ante peer firm returns to 3, 6, 12, 24, and 36 months. The prediction is that if information from product market shocks is disseminated with delay, then these extended horizon variables should be particularly strong predictors of ex-post focal firm returns given that they aggregate information in product market shocks over longer periods of time. Moreover, at the longest horizons, we would expect to see evidence of reversals, especially in subsamples where less visible to highly visible return differentials develop, as predicted by Hypothesis H4. C Industry Disparity We also consider more refined subsamples based on the data structures generated by text-based industries. In particular, we consider "disparity", which we define as 9 the extent to which a given focal firm's less visible TNIC peers disagree with highly visible SIC peers. In particular, disparity is equal to one minus the ratio of total sales of peers in the intersection of TNIC-3 and SIC-3 industry peer groups, divided by the total combined sales of peers in the union of TNIC-3 and SIC-3 peer groups overall. The use of sales weights is based on the assumption that the price of a focal firm is more likely to be influenced by larger rivals than smaller rivals. A firm in an industry with a high degree of disparity is thus in an industry with a large number of big TNIC peers that are not SIC-3 peers and vice-a-versa. Our prediction is that the dissemination of information should be particularly lagged when disparity is high, as this would indicate that less visible links are not replicated by the highly visible links, leaving fewer alternative channels for information dissemination for these links. D Systematic and Idiosyncratic Risk We also consider whether shocks to peers are idiosyncratic of systematic in nature. We thus begin with a simple decomposition of any firm's monthly return into a systematic and an idiosyncratic component. We use daily stock return data to implement this decomposition for each monthly stock return of each firm in each month. Using daily excess stock returns as the dependent variable, we regress these returns onto the daily stock returns of the market factor, HML, SMB, and UMD. 4 The predicted value from this regression is the systematic return. We use the geometric return formulation to aggregate the systematic daily returns to a database of monthly systematic stock returns for each firm in each month. We define the idiosyncratic component of r