Tests of the Moderating Effect of Analyst Expertise on the Association

Hypotheses 2FPe and 2FDe predict that the strategic impacts of ex ante information

asymmetry and discretionary disclosure on the analyst coverage decision are moderated by analyst expertise, which is proxies by analysts’ general experience. Experienced analysts are less likely to be affected by high task complexity because of their greater ability to obtain, understand and analyse information developed over time (e.g. Clement 1999). Recall from the results for tests of H1F, that the relationship between firms’

strategic choices and analysts’ coverage could be explained by either: 1) a demand effect arising from the level of ex ante information asymmetry, or 2) a supply effect associated with the level of voluntary disclosure. Within each of these effects task complexity plays competing roles. Prospectors are expected to be associated with higher task complexity arising from ex ante information asymmetry, but lower task complexity resulting from superior disclosure. If experienced analysts are found to be abnormally likely to follow Prospectors, this would suggest that the demand effect of task complexity dominates the disclosure effect. If experienced analyst are found to be abnormally likely to follow Defenders, this would suggest that higher task complexity arising from these firms weaker disclosure dominates the effect of lower complexity associated with ex ante information asymmetry.

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Table 3.10 Regression Results for H2FPe and H2FDe for Firms’ Strategic Choices

OLS Regression Logistic Regression

(1) (2)

VARIABLES MEAN_EXPERIENCE VARIABLES INDIV_COVERAGE

PROS_F -0.2879** PROS_F 0.3993*** (-) (0.035) (0.000) DEF_F 0.3219* DEF_F -0.2686*** (+) (0.070) (0.000) PROS_F* EXPERIENCE -0.0114*** (-) (0.000) DEF_F* EXPERIENCE 0.0117*** (+) (0.000) COVERAGE 0.0331*** EXPERIENCE 0.0237*** (0.000) (0.000) MEAN_ BROKERSIZE -0.0055*** BROKERSIZE 0.1169*** (0.000) (0.000) CFVOL -0.5342*** CFVOL 0.0184*** (+) (0.000) (+) (0.000) lnASSET 0.2236*** lnASSET 0.3014*** (+) (0.000) (+) (0.000) LOSS 0.0904 LOSS -0.0520*** (-) (0.357) (-) (0.000) ROA -0.2200 ROA 0.3170*** (+) (0.302) (+) (0.000) BTM 0.0086 BTM -0.0051*** (-) (0.525) (-) (0.000) LEVERAGE 0.9833*** LEVERAGE -0.2491*** (-) (0.000) (-) (0.000) FREE_CASH 0.1475 FREE_CASH 0.4152*** (+) (0.375) (+) (0.000) EXT_FINANC -0.2090 EXT_FINANC 0.4210*** (+) (0.423) (+) (0.000) VOLUME -0.0000 VOLUME 0.0000*** (+) (0.573) (+) (0.000) Constant 2.0729*** Constant -5.0391*** (0.000) (0.000)

Year fixed effects Yes Year fixed effects Yes Industry fixed

effects Yes

Industry fixed

effects Yes

Observations 27,232 Observations 16,818,622 R-squared 0.156 Pseudo R-squared 0.0797

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ROC stat 0.7382

Two-tailed clustered robust p-values in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 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’, 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, 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.

Table 3.10 presents the results of tests of the moderating effect of analyst expertise on the association between strategy and analyst coverage, for the aggregate coverage sample (Column 1) and the individual coverage sample (Column 2).

Tests using the aggregate coverage sample regress MEAN_EXPERIENCE (the average number of years of general experience of the analysts following the firm) against strategy types, after controlling for the raw level of coverage, and are estimated using OLS. The R2 _{for this model is 0.156. As expected, analysts’ general experience is positively}

associated with aggregate coverage. MEAN_BROKERSIZE is negatively associated with analysts’ general experience suggesting that small brokerage houses have more experienced analysts on average than large brokerage houses.

For the tests of hypotheses H2FPe and H2FDe and their alternate forms, it can be seen that

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that for DEF_F is positive and significant (β = 0.3219, p = 0.070), suggesting lower expert coverage for Prospectors and greater expert coverage for Defenders, relative to ‘other firms’.

Results for tests using the individual coverage sample are reported in Column 2 of Table 3.10. The areas under the ROC statistics curve for the regression is 73.82% indicating reasonable fit. In these models the dependent variable is a dichotomous variable indicating that an individual analyst chose to cover the firm in that year (INDIV_COVERAGE). The test variables in this case are the interactions between strategic dummies and analysts’ general experience (PROS_F*EXPRIENCE and DEF_F*EXPERIENCE). The coefficient on DEF_F*EXPERIENCE is positive and significant (β = 0.0117, p < 0.001) while the coefficient on PROS_F*EXPRIENCE is negative and significant (β = -0.0114, p < 0.001). Like the aggregate sample regressions, these results indicate that the experienced analysts are more (less) likely to cover Defenders (Prospectors) than are junior analysts.21

The results for both sets of tests provide evidence that task complexity plays a significant role in determining analyst coverage, and that the path through which it does so is dominated by the impacts of discretionary disclosure on complexity. For Defenders, the significant positive coefficient is consistent with expert analysts’ superior ability to deal with complexity arising from the Defenders’ weaker disclosure. Further this suggests that, for Defenders, the greater complexity arising from poor disclosure plays a significant role in explaining coverage decision, and dominates any impacts on complexity of the lower ex ante information asymmetry. For Prospectors’ the lower degree of expert coverage is consistent with these firms’ high level of discretionary disclosure reducing complexity,

21 _{To assist the interpretation of the interaction terms in this model, I performed additional analysis by re-}

estimating the regressions on subsamples defined by the quintiles values of the probability of coverage (estimated from the full sample regression). The interactions remain significant with the predicted signs in all sub-samples.

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and that this effect dominates any effects of high complexity associated with ex ante information asymmetry.

3.6.2.

Results for Tests of Industry Strategic Orientation and Analyst