This section present the results for tests of the association between industry strategic orientation and analyst coverage. Only the aggregate coverage sample is used here, because the construction of the individual coverage sample makes the coverage decision endogenous to industry identity (analysts must cover firms in the industry in order to appear in the sample). I report all regression results in Table 3.11.
Table 3.11 Regression Results for Industry Strategic Orientation
Regressions on the Aggregate Analyst Coverage Sample
Negative Binominal
Regression OLS Regression
(1) (2) (1) VARIABLES AGG_ COVERAGE AGG_ COVERAGE VARIABLES MEAN_ EXPERIENCE PROS_I 0.1294*** PROS_I -0.0872 (0.000) (0.559) DEF_I -0.1137*** DEF_I 0.5595*** (0.000) (+) (0.000) IND_STRATEGY 0.0154*** (0.000) COVERAGE 0.0329*** (0.000) MEAN_ BROKERSIZE -0.0057*** (0.000) log_CFVOL 0.1129*** 0.1149*** log_CFVOL -0.5419*** (+) (0.000) (0.000) (0.000) LnASSET 0.3340*** 0.3338*** LnASSET 0.2152*** (+) (0.000) (0.000) (0.000) LOSS -0.0142 -0.0133 LOSS 0.0851 (-) (0.418) (0.446) (0.384) w_ROA -0.0547 -0.0591 w_ROA -0.1851 (+) (0.152) (0.119) (0.384) adj_BTM -0.0248** -0.0256** adj_BTM 0.0096 (-) (0.031) (0.029) (0.501)
98 LEVERAGE -0.4555*** -0.4674*** LEVERAGE 0.9771*** (-) (0.000) (0.000) (0.000) FREE_CASH 0.2764*** 0.2749*** FREE_CASH 0.1826 (+) (0.000) (0.000) (0.275) EXT_FINANC 0.2067*** 0.1987*** EXT_FINANC -0.2109 (+) (0.000) (0.000) (0.418) VOLUME 0.0001*** 0.0001*** VOLUME -0.0000 (+) (0.000) (0.000) (0.791) Constant 0.3631*** 0.6431*** Constant 2.0333*** (0.002) (0.000) (0.000)
Year fixed effects Yes Yes Year fixed effects Yes Industry fixed
effects Yes Yes
Industry fixed
effects Yes
Observations 27,232 27,232 Observations 27,232 Pseudo R-squared 0.113 0.113 R-squared 0.156
F test 5356 5379
Difference in Coefficients of Pros and Def: Chi-squared =45.8 P-value<0.001
Two-tailed clustered robust p-values in parentheses, *** p<0.01, ** p<0.05, * p<0.1. AGG_COVERAGE= the number of analysts following the firm within the 90-day window leading up to the earnings announcement, MEAN_EXPERIENCE = the average years of forecasting experience recorded in I/B/E/S of all analysts who follows a firm during the financial year in the ‘aggregate coverage sample’, IND_STRATEGY = a industry-level discrete strategy score, estimated using a method derived from Bentley et al. (2013). The score values ranging from 6 to 30 where high (middle) [low] values indicate Prospector strategy-oriented (other strategy-oriented) [Defender strategy-oriented] industries, respectively, PROS_I = an indicator variable, equal to 1 if the IND_STRATEGY score is between 24 and 30, and 0 otherwise, DEF_I = an indicator variable, equal to 1 if the IND_STRATEGY score is between 6 and 12, and 0 otherwise, COVERAGE = the AGG_COVERAGE variable in Model 1 for testing H1 which is the number of analysts following the firm counted during the 90 days before the earnings announcement, MEAN_BROKERSIZE = the average number of analysts employed by a brokerage house in a given year in the ‘aggregate coverage sample’, CFVOL = the covered firm’s cash flow volatility, calculated as the natural logarithm of the standard deviation of the firm’s cash flows from operations over the past five years divided by total assets, lnASSET = the natural logarithm of total assets of the covered firm at the end of the year, ROA=the covered firm’s return on assets, calculated as income before extraordinary (ib) items divided by total assets (at) at the end of the year, BTM = the covered firm’s book-to-market ratio, calculated as total common equity outstanding (ceq) divided by market capitalisation (prc*shrout) in COMPUSTAT, LEVERAGE = the covered firm’s ratio of total debt (lt) scaled by total assets (at), FREE_CASH = the covered firm’s cash from operations (oancf) minus average capital expenditures (capx) for the last five years, scaled by current assets (act), EXT_FINANC = an indicator variable, equal to 1 if the firm’s variable FREE_CASH for the covered firm is less than -0.5, and 0 otherwise, and VOLUME = the covered firm’s annual trading volume (cshtrm) of firm’s stock in million. In regressions for individual analyst coverage: EXPERIENCE = the numbers of years since the analysts’ forecasts first appeared in I/B/E/S, and BROKERSIZE = the number of analysts employed by a brokerage house in a given financial year.
Column 1 and 2 of Table 3.11 presents the results for tests of Hypothesis 1I estimated on
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to the equivalent regressions on the firm-level sample. Like the firm-level results presented above and in Bentley-Goode et al. (2017), the coefficient for the ordinal measure STRATEGY (Column 1) is positive and significant. In Column 2, the coefficient for the test variable PROS_I is positive and significant (β = 0.1294, p < 0.001) indicating that firms in Prospector-oriented industries receive greater analyst coverage than the industries with ‘other strategic orientations’, consistent with the prediction for H1Ip.
Conversely, the coefficient on DEF_I is negative and significant (β = -0.1137, p < 0.001) suggesting that members of Defender-oriented industries receive lower analyst followings than industries with ‘other strategic orientations’, consistent with the prediction for H1Id. The test of the difference in coefficients on PROS_I and DEF_I
(tabulated) is significant (p < 0.001), which suggest Prospector-oriented industries receive greater analyst coverage than Defender-oriented industries.
Collectively, these findings are consistent with the association between firm-level strategy and coverage, which indicates that industry strategic orientation, as expected, has an complementary impact on top of firms’ strategy types, and together affect the analyst coverage decision.
Like the results for tests of firm-level strategy, the findings for Prospector-oriented industries may reflect: 1) the demand associated with greater ex ante information asymmetry and / or 2) the reduced task complexity effect from more frequent discretionary disclosures. Similarly, the lower coverage for Defender-oriented industries is consistent with: 1) reduced investor demand due to low inherent information asymmetry, and/or 2) increased task complexity arising from infrequent voluntary disclosures.
To further investigate the likely sources of difference in aggregate coverage, in Column 3 of Table 3.11, I estimate regressions of MEAN_EXPERINCE against industry strategic
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orientation, in a similar manner to the firm strategy level tests tabulated in Table 10. The results with respect to Defenders are broadly similar to the equivalent firm strategy tests. The coefficient for DEF_I is positive and significant (β = 0.5595, p < 0.001), implying that Defender-oriented industries receive abnormally large coverage from experienced analysts. Once more, this is consistent with expert analysts being better able to deal with the high task complexity arising from Defenders lower incidence of voluntary disclosures and this dominates any negative effect associated with lower complexity arising from lower ex ante information asymmetry.
However, unlike the results for tests using firm-level strategy, there is no significant negative association between MEAN_EXPERIENCE and Prospector-oriented industries; the coefficient for PROS_I is insignificant (β = -0.0872, p = 0.559), suggesting that when assessed at the industry strategic orientation level, complexity effects associated with Prospector-like attributes disclosure frequency are weaker.22