Evolución de la difusión de medios impresos

Following Teoh et al. (1998a, 1998b) and Louis (2004), I employ discretionary current accruals as a measure of earnings management and use the cross-sectional adoption of the modified-Jones model (Dechow, Sloan, and Sweeney 1995) to obtain the discretionary current accruals for a given year. Specifically, for each industry (two-digit SIC code) and each year, I estimated equation (3) using all firms that have necessary accounting data on Compustat and with assets greater than $1M.

CAit = β0+ β1 (1 / ASSETit-1) + β2ΔSALEit + εi (3) where, CA is current accruals divided by total assets at the end of year t-1; ASSET is total assets

(Compustat item AT); ΔSALE is the change in sales (Compustat item SALE) from the prior year divided by total assets at the end of year t-1; ε is the regression residual.22

22

To mitigate the influence of outliers, I winsorize CA, 1/ ASSET and ΔSALE at the 1% and 99% levels. I also require there be at least 20 observations in each industry and year to accurately estimate the regression coefficients.

Following previous studies (e.g., Teoh et al. 1998a; Fischer and Louis 2008; Hovakimian and Hutton 2010), current accruals is defined as change in non-cash current assets (Compustat item ACT minus Compustat

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item CHE) minus change in current liabilities (Compustat item LCT) plus change in the current portion of long-term debt (Compustat item DD1).

Equation (4) estimates expected current accruals using the coefficient estimates obtained from equation (3) with an adjustment for the change in accounts receivables:

ECAit = 𝛽̂0 + 𝛽̂1 (1 / ASSETit-1) + 𝛽̂2(ΔSALEit - ΔARit) (4)

where, 𝛽̂0, 𝛽̂1,and 𝛽̂2 are estimated coefficients from equation (3); ECA is the expected current accruals; ΔAR is the change in accounts receivable (Compustat item RECT) from the prior year divided by total assets at the end of year t-1. Discretionary current accruals (DCA) is the

difference between current accruals and expected current accruals.

Finally, I adjust DCA obtained from the modified-Jones model for performance since Kothari, Leone, and Wasley (2005) show that performance-adjusted accruals are well-specified and yield powerful tests. Specifically, following previous studies (e.g., Louis 2004; Louis and Robinson 2005; Gong, Louis, and Sun 2008), for each year and each industry, I build five portfolios by sorting the data into quintiles based on return-on-assets (i.e., income before extraordinary items (Compustat item IB) divided by lagged total assets) from year -2 relative to the issuance year.23 The performance-adjusted DCA for a sample firm is calculated as the DCA

of the firm minus the median DCA for its respective industry-performance-matched portfolio. As suggested by Gong et al. (2008), the portfolio benchmarking approach allows researchers to control not only for performance but also for random effects stemming from other events which may impact accruals or other managerial incentives to engage in earnings management.

23 _{Teoh et al. (1998a) document that issuing firms report positive discretionary current accruals in year -1. Therefore,}

performance-matching based on the ROA reported in year -1 could lead to biased results. However, I reran all the analyses by creating matching portfolios based on the ROA reported in year -1, as well as year 0 and obtained similar results.

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2.4 SAMPLE FORMATION AND DESCRIPTIVE STATISTICS

The initial sample of SEOs is obtained from the Security Data Corporation’s (SDC) New Issues Database for the period from 1983 to 2006. Following previous studies (e.g., Lee and Masulis 2009; Cohen and Zarowin 2010), I exclude the following: (1) SEOs lacking Compustat annual financial statement data for the four years prior to the SEO filing date24

Summary statistics are reported in Table 2.1. Panel A reports the size characteristics as of the end of year -1 (where year 0 is the offering year). Panel B and C present the year and

industry distribution, respectively. The results suggest that the sample is not dominated by a particular year, though 1983 stands out as a very active year for SEOs as it contains 10.88 percent of the sample. Furthermore, examination of the industry distribution reveals that the two largest industry groups in the sample are computer equipment/services firms and electronic equipment firms, which constitute 15.02 percent and 11.19 percent of the sample, respectively. Panel D reports the number of issuers classified as constrained and unconstrained according to

, (2) close-end funds, unit investment trusts, REITs, and limited partnerships, (3) spin-offs, (4) reverse LBOs, (5) rights issues, (6) pure secondary offerings, (7) simultaneous or combined offers of several classes of securities such as unit offers and warrants, (8) non-domestic and simultaneous domestic- international offers, (9) offering of securities with CRSP share codes other than 10 or 11, (10) SEOs with offer prices less than $5. Furthermore, financial firms (SIC codes 6000-6999) and utilities (SIC codes 4900-4999) are excluded from the sample due to the greater regulation for these firms, limiting their ability to engage in earnings management. The final sample includes 1,645 SEOs for which the accruals data are available in the year of the offering.

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This restriction is likely to induce survivorship bias, resulting in the inclusion of larger and more successful firms. However, I expect that this will reduce the variation in the earnings management and financial constraint measures and thus, result in a more conservative test of my hypotheses.

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each of the four measures of financial constraints. It also shows the extent to which different classifications are correlated. For example, out of the 548 issuers considered constrained according to the WW index, 420 are also considered constrained according to the payout ratio classification, while only 93 issuers are considered unconstrained. The remaining issuers are the WW-constrained firms that are neither constrained nor unconstrained under the payout ratio classification.

Table 2.2 presents the median firm characteristics (as of the end of year -1) by financial constraints categories under each classification. The cross-sample differences between

constrained and unconstrained issuers are in line with expectations and with prior studies in the literature. The results consistently suggest that financially constrained firms tend to have lower market value, higher market-to-book ratio and experience higher volatility of cash flows, revenue, and sales growth. In addition, regardless of the classification method, the median financially constrained firm does not pay any dividends or repurchase any shares in the pre-SEO year and is followed by only one analyst (two analysts under the payout ratio classification). On the other hand, the median unconstrained firm has positive payout activity in the year prior to the offering and is followed by higher number of analysts.

Supporting the notion that constrained firms engage in precautionary savings (e.g., Han and Qui 2007; Duchin 2010), cash-to-assets ratio is higher for constrained versus unconstrained issuers. Moreover, consistent with Whited and Wu (2006), debt-to-assets ratio decreases with financial constraints as constrained firms often lack resources that can be used as collateral for their debt. The results also reveal that Altman’s (1968) Z-score, which is a measure of the

52 for the constrained group.25

Further, the size and SA index measures suggest that unconstrained firms report lower industry-adjusted ROA as compared to constrained firms, while the payout ratio measure does not reveal any significant difference in ROA between the two groups. However, according to the WW index classification, the unconstrained group reports higher industry-adjusted ROA than the constrained group. Finally, the results indicate that the offer size (i.e., offer amount scaled by pre-SEO market value) is significantly larger for the constrained group versus unconstrained group under all classifications.

This is in line with DeAngelo et al. (2010) who document that mature and dividend paying issuers have lower Z-scores than young and non-dividend paying issuers. Although this finding may seem counterintuitive, it is reassuring in that the proposed link between financial constraints and upward earnings management around equity offerings is

unlikely to be an artifact of the constraint firms’ desire to avoid a potential bankruptcy.