Control Variables

Consistent with prior literature (e.g. Aboody and Lev 2000, Barth et al. 2001 and Bentley- Goode et al. 2017), Models 1(a) and (b) control for firm characteristics that are likely to impact information asymmetry, and which may potentially be correlated with strategy. These control variables are described below.

CFVOL

Consistent with Bentley-Goode et al. (2017), cash flow volatility is measured by the natural logarithm of the standard deviation of the firm’s cash flow from operation over the past five years divided by total assets (CFVOL). A positive association between the number of analyst following a firm (COVERAGE) and cash flow volatility is expected as cash flow volatility is associated with high information asymmetry and price volatility which attracts analysts to follow the firm (Eccles 1988 and Wang 1993). CFVOL is also conceivably correlated with corporate strategy. Prospectors are expected to be associated with higher cash flow volatility because of the riskiness and outcome uncertainties associated with R&D investments, while Defenders focus on a stable product market which ensure them to have more persistent cash flows (Miles and Snow 2003).

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lnASSET

I also control for firm size using the natural logarithm of total assets (lnASSET) (Bentley- Goode et al. 2017). Thus, I will use the market capitalisation as a control in additional tests. The coefficient of lnASSET is expected to be positive as investors are more likely to demand for information of large firms and analysts gain more benefits such as superior professional rankings and reputation by covering these firms (Mikhail et al. 2004, Leone and Wu 2007, Emery and Li 2009).15

3.3.2. Firm Performance

To control for the impact of other firm characteristics on firm’s voluntary disclosure, I include two controls for firm performance (Verrecchia 1983). Return on assets (ROA), calculated as income before extraordinary items divided by total asset is employed as a control for profitability (Bentley-Goode et al. 2017). Following Bentley-Goode et al. (2017), I also control for the impact of bad news on discretionary disclosures (e.g. Skinner 1994) by including a dummy for loss-making firm-years. The variable LOSS equals one if the firm’s income before extraordinary items was negative in the prior year, and zero otherwise. A positive (negative) relationship between the number of analysts following the firm and ROA (LOSS) is expected as analysts are more likely to follow firms with better financial performances (Chung and Jo 1996).

Firm Growth

The firm’s growth opportunities affect the numbers of analyst following a firm (e.g. Barth et al. 2001). Prior analyst literature, for example Aboody and Lev (2000) and Barth et al. (2001) use the change in sales as a proxy for firm growth. However, Miles and Snow’s (2003) typology implies that the change in sales is strongly correlated with corporate

15 _{Prior analyst literature examining the analyst coverage decision predominately uses the natural logarithm}

of firm’s market capitalisation as a control for firm size (e.g. Lang and Lundholm 1996 and Barth et al. 2001). The results reported later in this study are not substantively affected if I use market capitalisation, rather than total assets, as the basis of my size control.

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strategy. For example, Prospector’s key strategic focus is product market expansion which increase volatilities of change in sales, whereas Defenders are more like to stick with a fixed product range to earn a persistent level of sales (Miles and Snow 2003). Because of that, change in sales is one of the component used in calculation the strategy score measure as well. To avoid potential multicollinearity, I use the book-to-market ratio (BTM) as a proxy for firm growth, consistent with Bentley-Goode et al. (2017). BTM equals to the firm’s total common equity outstanding divided by market capitalisation from COMPUSTAT. The coefficient of BTM is expected to be negative as analysts are more likely to follow firms with opportunities of future growth (Barth et al. 2001).

Demand for External Financing

Prior literature suggests firms’ reliance on external financing affects firm’s discretionary disclosure and information asymmetry (e.g. Diamond and Verrecchia 1991). I use LEVERAGE, measured by the ratio of total debt to total assets, as my first proxy for firm’s demand for external financing (Bentley-Goode et al. 2017). Firms’ demand for external financing may be associated with strategic choices. Bentley-Goode et al. (2017) argues that Prospectors are more likely to require external financing to fund their R&D investments and this affects the choices of analysts to follow a firm in two ways. First, Diamond and Verrecchia (1991) suggests that firms makes more discretionary disclosure to reduce their cost of capital and that reduces the ex-ante information asymmetry of that firm. If the reduction in information asymmetry dominates, I expect a negative relationship between the coverage decision and leverage as the benefits from making profitable recommendations are lower (Eccles 1988). On the other hand, firm’s financing requirements are also associated with higher probability of investment banking opportunities for analysts’ employers (e.g. Lin and McNichols and Kothari and Kolasinski 2008). If the incentives for investment banking opportunities dominates, I expect a positive relationship between analyst following and leverage.

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To further control for the potential impact of external financing on the analyst coverage decision, I use free cash flow (FREE_CASH) and an indicator variable for financial distress (EXT_FINANC) to capture the needs for external financing following Bentley- Goode et al. (2016). FREE_CASH is measure by the cash from operation minus the capital expenditure scaled by current assets to capture the liquidity of the firm. As firms that have a negative free cash flow are more likely to be under financial distress. Thus, EXT_FINAC is an indicator variable that set equal to one if the scaled free cash flow of the firm is below -0.5. Similar to LEVERAGE, I expect a positive relationship between the number of analyst followings and FREE_CASH if negative free cash flow indicates more disclosures to reduce firm’s cost of capital. This leads to lower analyst coverage as the benefits of making profitable recommendations are lower (Eccles 1988). I expect a negative relationship between the analyst following and FREE_CASH if negative free cash flow indicates potential investment banking opportunities (e.g. SEOs) which increases analyst followings. Different from FREE_CASH, EXT_FINAC is a direct measure of firm’s financial distress which should reflect the impact of analyst incentives for investment banking opportunities on the analyst coverage decision.

Analyst Attributes

Consistent with Barth et al. (2001), I control for the potential incentive for analysts to generate trading commissions for their brokerage houses by increasing the trading volume of the covered stock. Thus, VOLUME measures the annual trading volume of the firm’s stock, in millions of shares. A positive relationship is expected between the analyst coverage decision and trading volume as analysts are more likely to cover firms with high trading volume to maximise the trading commissions received by their brokerage houses.

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Additional Controls Used in Models of the Individual Analyst Coverage Decision

To control for the impact of individual analyst attributes on their choices to follow a firm, I include two controls, general experience (EXPERIENCE) and the size of analyst’s employers (BROKERSIZE) in the model that uses the individual analyst coverage sample. Based on prior literature, general experience of analyst is measured by the number years that an analyst exists in the I/B/E/S (Clement 1999, Clement and Tse 2003, 2005, Kim et al. 2011, Drake and Myers 2011 and Casey 2012). A positive relationship between the probability of an analyst to follow a firm and general experience is expected as experienced analysts possess better ability and takes less effort to cover a firm (Clement 1999 and Drake and Myers 2011). EXPERIENCE is also conceivably correlated with corporate strategy as the high task complexity associated with Prospectors’ high ex ante information asymmetry might be mitigated by analyst experiences on the jobs.

As discussed in hypothesis development section, the characteristics of analyst’s employers (BROKERSIZE) can influence analyst following through the reduction of task complexity (e.g. Clement 1999 and Drake and Myers 2011) and the incentives associated with investment banking opportunities (e.g. Lin and McNichols 1998 and Kothari and Kolasinski 2008). BROKERSIZE is also potentially correlated with corporate strategy as large brokerage houses might provide resources to reduce analyst’s task complexity. For example, the reputation and administrative assistance offered by large brokerage houses can reduce the difficult for assists to access private information in Defender firms (Clement 1999 and Drake and Myers 2011). The size of a brokerage house (BROKERSIZE) is measured by the number of analysts employed by a brokerage house who provide forecasts to I/B/E/S in a given financial year. A positive association is expected between the individual analyst coverage decision and the size of a brokerage house as large brokerage house have more resources to reduce analyst’s cost to cover a

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firm and might be associated with greater incentive to obtain investment banking opportunities.