Information diffusion of upstream and downstream industry-wide earnings surprises and its implications

Since the late 1960s, the predictability of stock returns after earnings announcements has attracted substantial attention. Bernard and Thomas (1989; 1990) and Bartov (1992), among others, find evidence that the post-earnings-announcement drift represents the market's failure to fully reflect the attributes of the stochastic process underlying earnings. Bhushan (1994) concludes that transaction costs influence the trading and arbitrage activities of professionals in a way that preclude them from taking positions that would eliminate the drift. Mendenhall (2004) highlights that arbitrage risk impedes arbitragers who attempt to profit from the drift.

The financial press identifies numerous such cases. For example, Hudson, Kris, 2006, “Wal-Mart Ripple Effect Strikes Again: Cutbacks Weigh on Supplier Earnings,” Wall Street Journal, April 27, page C1. Chen, Stephanie, 2008, “Cargo Slump Bodes Ill for Supply Chain,” Wall Street Journal, March 20, A13. Covel, Simona, 2008, “Banks’ Pain Spreads to Their Suppliers,” Wall Street Journal, October 7, B1. Stoll, John and Jeffrey McCracken, 2009, “Bankruptcy Fears Grip Auto-Parts Suppliers,” Wall Street Journal, January 26, A1.

Inter-industry trade network is another rich area to examine the transfer of information. Aobdia et al. (2014) study how centrality impacts transfers of information and find that the stock returns and accounting performance of central industries better predict the performance of industries linked to them. Although this study only focuses on the main customer/supplier industry, which is a subset of the trade network, the result based on the single but important link can provide not only a basic understanding on information diffusion but also a lower bound estimation of information diffusion effect.

I use “esurprises.sas” provided by Wharton Research Data Services (WRDS) to construct SUEs defined by Livnat and Mendenhall (2006). I adopt their data selection criteria which are described as follows: (1) The earnings announcement date is reported in Compustat for both quarter t and quarter t + 1. The earnings report dates in Compustat and in I/B/E/S (if available) differ by not more than one calendar day; (2) The price per share is available from Compustat as of the end of quarter t, and is greater than $1; (3) The market (book) value of equity at the end of quarter t − 1 is available from Compustat and is larger than $5 million (positive). Additionally, I use actual report dates (RDQ) in Compustat and forecast period end dates (FPEDATS) in I/B/E/S to remove any irregularity in the I/B/E/S dataset. I assume that quarterly forecasts for which RDQ does not fall within 6 months after FPEDATS are irregular. I find that over the entire sample period, more than 87% of actual quarterly earnings in I/B/E/S are reported within 2 months after FPEDATS. To make proper comparisons between I/B/E/S and Compustat data, I use the unadjusted (for splits and stock dividends) I/B/E/S forecasts and actual earnings. I thus avoid the potential rounding problems pointed out by Payne and Thomas (2003). Further, I/B/E/S determines whether most forecasts are based on primary or diluted EPS. When matching I/B/E/S forecasts and Compustat actual earnings figures, I use the earnings definition (primary or diluted EPS) as indicated by I/B/E/S.

Mendenhall (2004) sets the standard deviation to $0.01 if it is zero. The correct lower bound for non-zero standard deviations in this context should be $0.01* \( \sqrt {N - 1} /N \). This is derived when all analysts provide the same earnings estimates except one analyst who estimates the earnings one penny difference. In the 1st fiscal quarter in 1997, for example, Bed Bath & Beyond Inc. was given an identical earnings estimate by 11 analysts and thus the denominator of SUE5 is set to 0.003. Stocks with only one earnings estimate in a quarter are excluded from calculations in both SUE4 and SUE5 for the quarter.

For the second part of their analysis investigating various trading strategies based on cross-predictability effects, Menzly and Ozbas (2010) correctly delay using any data from a given survey until the end of the year in which the survey is publicly released. Aobdia, Caskey, and Ozel (2014), only using the BEA’s 1997 Input–Output account, is also subject to the look-ahead bias.

In post-1997 BEA I–O industry accounts, the NAICS coding structure changes periodically. However, the difference in I–O industry codes does not necessarily imply that the industry composition has changed. This creates a challenge for obtaining consistent I–O industry accounts over time. Using the Census Bridge between the 1997 and 2002 NAICS industries available at the web site, http://​www.​census.​gov/​econ/​census02/​data/​bridge/​, I am able to align the following different NAICS codes in the information sector across time. I–O industry account 5131 (Radio and television broadcasting), 5132 (Cable networks and program distribution), 5111 (Newspaper, book, and directory publishers), 5133 (Telecommunications), and 5142 (Data processing services) in 1997 NAICS code corresponds to their counterpart 5151 (Radio and television broadcasting), 5152 (Cable networks and program distribution), 5161 (Internet publishing and broadcasting), 5170 (Telecommunications), and 5182 (Data processing, hosting, and related services) in 2002 NAICS code, respectively. I–O industry code 5181 (Internet service providers and web search portals) and 5190 (Other information services) in 2002 NAICS code corresponds to 5141 (Information services) in 1997 NAICS code. The bridge between the 1997 and 2007 NAICS industries is more direct according to the file at https://​www.​bea.​gov/​scb/​pdf/​2013/​06%20​June/​0613_​preview_​comprehensive_​iea_​revision.​pdf. I–O industry account 5131 (Radio and television broadcasting), 5132 (Cable networks and program distribution), 5133 (Telecommunications), 5141 (Information services), and 5142 (Data processing services) in 1997 NAICS code corresponds to their counterpart 5151 (Radio and television broadcasting), 5152 (Cable networks and program distribution), 5170 (Telecommunications), 5190 (Other information services), and 5180 (Data processing, hosting, and related services) in 2007 NAICS code, respectively.

Note that Industry A is Industry B’s main supplier but Industry B may not be Industry A’s main customer. In the 1987 BEA Benchmark I–O account, for example, BEA 38 Primary Nonferrous Metals Manufacturing is BEA 53 Electric Industrial Equipment/Apparatus’ main supplier but BEA 38 Primary Nonferrous Metals Manufacturing’s main customer is BEA 11 New Construction. In the 1992 BEA Benchmark I–O account, the supplier relationship between BEA 38 and BEA 53 still holds but BEA 38 Primary Nonferrous Metals Manufacturing’s main customer changes to BEA 59 Motor vehicles/equipment.

The coefficient on one‐month lagged SUE from either customer or supplier industry is always stronger using analyst forecasts (for example, SUEs 4 and 5) than using lagged earnings (for example, SUEs 1 and 2). Contrastingly, the coefficient on one‐month lagged own industry SUE is insignificant or weaker using analyst forecasts. It seems that financial analysts are more or less able to incorporate information content of the lagged earnings surprises from an industry itself, but cannot completely incorporate information content of the lagged earnings surprises from its customer/supplier industry. The availability of more accurate information in a firm’s own industry, in turn, leads to a lesser degree of financial analysts’ forecast dispersion. It is consistent with Kwon (2002) showing that increases in more accurate information available for high-tech firms lead to a higher level of financial analysts’ earnings forecast accuracy. A further investigation on when and how analysts consider customer versus supplier industry information in their forecasts would provide greater insights into the diffusion of industry information. To effectively conduct this investigation, I have to collect analyst forecast revision data and leave this pursuit to future research. I thank an anonymous referee for making this point.

Livnat and Mendenhall (2006) conclude that the earnings surprise measured by the random walk model and analyst forecast represents somewhat different forms of mispricing. Bradshaw and Sloan (2002) point out that many of the expenses omitted from the analyst forecasts are captured by Compustat’s special item variable. Analyses based on SUE1 and SUE2 result similarly while analyses based on SUE4 and SUE5 result similarly. Clement (1999) and Jacob, Lys, and Neale (1999) argue that the price-deflated forecast error is more vulnerable to intertemporal and cross-sectional differences in price-to-earnings ratios, thus the interpretation on results by SUE3 needs additional caution. All results, not reported here, are available upon the request.

Petersen and Strongin (1996) measure the cyclicality of an industry as the sensitivity of the percentage change in real value added in the industry to the real growth rate in gross national product. This study simply defines that a pro-cyclical (countercyclical) firm relative to its supply chain’s prospects is the firm whose stock returns positively (negatively) react to its main customer/supplier industry returns. A good example of counter-cyclical firms is identified in “In Tough Times, Auto-Parts Firms Receive a ‘Countercyclical Boost’” by David Gaffen, Wall Street Journal, February 20, 2009, page C6. Poor sales of new cars mean more business for auto-parts stores such as AutoZone and O’Reily Automotive.

In any given month t, the strategies hold a series of portfolios that are selected in the current month as well as in the previous K-1 months, where K is the holding period. For instance, for a 6-month holding strategy, a December long portfolio comprises stocks with the highest constructed expected returns ranked at the end of the previous November, the previous October, and so on up to the previous June. Each monthly cohort is assigned an equal weight in this portfolio.

Over the sample period of January 1986 to December 2015, the average return is 0.20% (1.37%) per month for the bottom (top) portfolio of the 10 portfolios formed on momentum directly retrieved from Kenneth French’s web site as of February 19, 2017. Although the portfolio construction is different here, it is not unusual that the past loser has positive returns over the considered sample period.

When we consider main supplier industry signals, the 4-factor alphas are only significant for the long/short portfolio up to the 2-month holding period. It seems to contradict the argument made earlier that it takes longer for the supplier information to diffuse than the customer information. While this is a conflicting finding, the result is still consistent with the expectation that the strategies based on the gradual diffusion of industry-wide earnings surprises across the supplier industry link are weaker.

I do not control for the intra-industry lead-lag effect of Hou (2007) for several reasons. First, I focus on the slow diffusion of earnings-related information across the supply chain, not on the diffusion of news on certain firms within an industry. Additionally, given there are 71 BEA industries prior to 1997, three size-sorted portfolios within an industry will yield 213 portfolios. Some portfolios might contain few firms and thus their returns will be noisy and may not be good as control variables. Furthermore, the extreme portfolios in this study are composed of medium-sized firms and are diversified across industries. The intra-industry lead-lag effect will thus be less of a concern.

Using the same methodology that the investment strategies are formed, I only include firms in the regressions of Models 1–4 if they have significant response coefficients and their associated main customer/supplier industry had an earnings signal in the prior month. Thus, the average number of observations is about half of those in the benchmark model.

After taking into account endogeneity among leverage, debt maturity, and cash holding, Brick and Liao (2017) present evidence that firms facing financial constraints borrow long term debt to build up the firm’s cash reserves in order to have the flexibility to fund future investments.