I employ both the aggregate coverage sample and individual coverage sample to test my hypotheses relating to the association between firm strategy and analyst coverage (H1Fp,
H1Fd). Collectively, these hypotheses predict that Prospectors will attract greater coverage
than all other strategy types, and that Defenders will attract lower coverage than ‘other firms’. Table 3.9 presents the results of all tests of Hypotheses 1F.
Table 3.9 Regression Results for H1F for Firms’ Strategic Choices
Negative Binominal Regressions Logistic Regressions
(1) (2) (3) VARIABLES AGG_ COVERAGE AGG_ COVERAGE INDIV_ COVERAGE INDIV_ COVERAGE STRATEGY 0.0327*** 0.0411*** (0.000) (0.000) PROS_F 0.1670*** 0.2848*** (0.000) (0.000) DEF_F -0.1969*** -0.1396*** (0.000) (0.000) EXPERIENCE 0.0237*** 0.0237*** (0.000) (0.000) BROKERSIZE 0.1169*** 0.1168*** (0.000) (0.000) CFVOL 0.0972*** 0.1086*** 0.0092*** 0.0186*** (+) (0.000) (0.000) (0.001) (0.000) lnASSET 0.3290*** 0.3290*** 0.3009*** 0.3015*** (+) (0.000) (0.000) (0.000) (0.000) LOSS -0.0391** -0.0175 -0.0730*** -0.0528***
90 (-) (0.025) (0.318) (0.000) (0.000) ROA 0.0061 -0.0357 0.3651*** 0.3143*** (+) (0.881) (0.372) (0.000) (0.000) BTM -0.0195* -0.0232** -0.0047*** -0.0051*** (-) (0.053) (0.040) (0.000) (0.000) LEVERAGE -0.3884*** -0.4485*** -0.1654*** -0.2505*** (-) (0.000) (0.000) (0.000) (0.000) FREE_CASH 0.2929*** 0.3004*** 0.4081*** 0.4140*** (+) (0.000) (0.000) (0.000) (0.000) EXT_FINANC 0.1835*** 0.1958*** 0.4159*** 0.4210*** (+) (0.000) (0.000) (0.000) (0.000) VOLUME 0.0001*** 0.0001*** 0.0000*** 0.0000*** (+) (0.000) (0.000) (0.000) (0.000) Constant -0.0381 0.6224*** -5.8512*** -5.0382*** (0.746) (0.000) (0.000) (0.000) Year fixed effects Yes Yes Yes Yes Industry fixed
effects Yes Yes Yes Yes
Observations 27,232 27,232 16,818,622 16,818,622 Pseudo R-squared 0.116 0.114 0.0807 0.0796
F test 5693 5493 235832 232534
ROC stats 0.7402 0.7380
Difference in Coefficients of Pros and Def:
Chi-squared =77.95 P-value<0.001 Chi-squared =40.08 P-value<0.001 Two-tailed 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, INDIV_COVERAGE = an indicator variable, equal to 1 if an analyst covers a firm during the year, and 0 otherwise, STRATEGY = a firm’s discrete strategy score estimated using the model of Bentley et al. (2013). Values range from 6 to 30 where high (middle) [low] values indicate Prospector (other firms) [Defender] firms, respectively, PROS_F = an indicator variable, equal to 1 if the STRATEGY score is between 24 and 30, and 0 otherwise, DEF_F = an indicator variable, equal to 1 if the STRATEGY score is between 6 and 12, and 0 otherwise, 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, 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.
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The first two columns of Table 3.9 provide the results for tests of Hypothesis 1F estimated
on the aggregate coverage sample. The Pseudo R2 for Model 1 estimated on the aggregate coverage sample is 0.114 (Column 2). For comparison with Bentley-Goode et al. (2017) I present results using the discrete strategy scores (Column 1) as well as those using the indicators of strategic type (Column 2) .
All control variables in Column 1 and 2 exhibit a consistent sign with the prediction and most are significantly associated with AGG_COVERAGE, except for LOSS and ROA. This is consistent with Bentley-Goode et al. (2017)’s model using the discrete strategy scores in the regression. The control for investment banking incentive (EXT_FINANC) and trading incentives (VOLUME) are positively associated with analyst coverage indicating analyst incentives might have an impact on analysts’ choice to cover a firm. For tests of H1Fp, the coefficient on PROS_F is positive and significant (β=0.167, p<0.001)
indicating that Prospectors receive greater analyst coverage than ‘other firms’. This result is further supported by the positive and significant coefficient on STRATEGY in Column 1 (β = 0.0327, p < 0.001). These results are consistent with Barth et al. (2001)’s argument that the value-adding benefits from covering firms with high ex ante information asymmetry dominates the increased task complexity effect. However, it is difficult to differentiate whether the positive association is derived from 1) the value-adding effect from ex ante information asymmetry or 2) the reduced task complexity effect from high voluntary disclosures.
For tests of H1Fd, the coefficient on DEF_F in Column 2, Table 3.9 is negative and
significant (β = -0.1969, p < 0.001) suggesting that Defenders receives lower analyst following than firms that pursue ‘other strategies’. This result indicates that the relationship between Defenders and analyst coverage is driven by the effects of either or
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both: 1) the reduced value-adding benefits of low inherent information asymmetry, and/or 2) the increased task complexity effect of low voluntary disclosures.
The test of the difference in coefficients for PROS_F and DEF_F (tabulated) suggest that the positive coefficients on PROS_F are significantly different from the negative coefficients on DEF_F (p < 0.001), indicating that Prospectors receives more analyst coverage than Defenders.
Columns 3 and 4 of Table 3.9 present the results from estimation using the individual coverage sample, of the probability that an individual analyst will cover the firm (INDIV_COVERAGE). The Pseudo-R2s for the model using the ordinal strategy score and that using indicator variables are both 0.08. The areas under the ROC statistics curve for the regressions are 74.02% and 73.80% respectively, indicating reasonable fit. All control variables in Model 2 are significantly associated with the COVERAGE variables and consistent with the predicted direction.
Consistent with the results from the aggregate coverage sample, both the coefficient on STRATEGY in Column 3 (β = 0.0411, p<0.001) and the coefficient on PROS_F in Column 4 (β = 0.2848, p<0.001) are positive and significant, while the coefficient on DEF_F is significantly negative (β= -0.1396, p<0.001). These results indicate that Prospectors receive greater analyst coverage while Defenders receive lower analyst coverage than firms following ‘other strategies’. The test of difference in coefficients on PROS_F and DEF_F once more shows the positive association for PROS_F are significantly different from the negative association for DEF_F (p < 0.001).
Collectively, the results indicating that Prospectors receive greater coverage is consistent with either or both a demand effect driven by high ex ante information asymmetry or a supply effect arising from superior discretionary disclosures. The findings that Defenders attract lower coverage than ‘other firms’ could be explained by either lower demand due
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to lower ex ante information asymmetry or a greater supply cost arising from Defenders’ weaker discretionary disclosures.
To further disentangle the strategic impacts of ex ante information asymmetry from the strategic effects of voluntary disclosure, I use analyst expertise to differentiate the situations in which one of the impacts dominates the ultimate impacts of firm’s strategic choices.
3.6.1.2. Tests of the Moderating Effect of Analyst Expertise on the Association