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Analyst following and R&D investment

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Abstract

Exploiting an exogenous decrease in analyst access to private information, we show that firms with analyst following (test firms) experience a significant reduction in R&D investment, relative to firms without analyst following (control firms), after Reg FD. We also document that the negative impact on firms’ R&D investment after Reg FD is borne out in firms’ innovation outputs measured by patents. Additional analysis reveals that the decrease in R&D investment concentrates among test firms followed by influential analysts who likely had access to private information before Reg FD and among test firms facing disincentives to invest in R&D due to takeover pressure. Our inferences remain robust to various tests addressing potential bias arising from possible systematic differences between test and control firms. Overall this study demonstrates that private disclosures of R&D information through analysts mitigate managers’ R&D investment disincentives and facilitate innovation but that this channel is impeded by Reg FD.

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Data Availability

Data are derived from publicly available sources.

Notes

  1. This is consistent with prior studies documenting that changes in analysts’ target price and stock recommendations affect stock prices and wealth allocation (e.g., Stickel 1995; Womack 1996; Brave and Lehavy 2003). This unique role played by analysts is also supported by prior studies, which document that firms with high R&D investments seek analyst coverage to reduce the information asymmetry about those investments (Gintschel and Markov 2004; Kirk 2011). In addition, Barth et al. (2001) document that firms with high R&D investment attract more analysts, as private information acquisition becomes more profitable when a firm’s R&D investment is high. King et al. (1990) argue that at least part of analysts’ private R&D information is obtained from managers.

  2. Our design allows firms to experience increases or no changes in analyst following. An increase in analyst following would decrease firms’ cost of capital, which suggests an increase rather than a decrease in R&D investment after Reg FD. In addition, firms with no changes or experiencing an increase in analyst following would still experience the negative effects of Reg FD on analysts’ ability to discreetly disseminate a firm’s R&D investment information to the market.

  3. As private information sharing with private lenders is not prohibited by Reg FD, Reg FD should not directly affect firms’ cost of debt. Nonetheless, we control for both the costs of equity and debt in our models.

  4. As discussed in Sect. 3, research has shown that entropy balancing is more effective than propensity-score matching (Hainmueller 2012). Thus our remaining tests are based on the entropy-balanced sample.

  5. For example, Bellstam et al. (2021) provide an excerpt from a May 1993 analyst report which discusses the impact of Walmart’s innovative investment on the company’s competitive advantage. However, in the report, there is no detailed discussion about any specifics of R&D.

  6. In an untabulated analysis, we conduct additional tests to rule out this alternative explanation. First, as most internet firms are small, we limit our sample observations to large firms based on the market value of equity (Gomes et al. 2007; Chen et al. 2010). Specifically, we classify a firm as large if its market value of equity is above the 80th percentile. Second, following prior studies (e.g., Chen et al. 2010), we exclude all NASDAQ firms from our sample, as most internet firms are traded on the NASDAQ. Our findings are robust to these alternative sample selections, suggesting that our main findings are not likely to be driven by the bursting of the internet bubble.

  7. In an untabulated analysis, we also remove small firms from our sample, as the literature shows that Reg FD increases the cost of capital for these firms (Gomes et al. 2007; Duarte et al. 2008), which would decrease R&D investment. We limit our sample to large firms based on firms’ market values of equity, as discussed previously. Our main findings are robust to this alternative sample selection, suggesting that our results are not driven by an increase in the cost of capital after Reg FD.

  8. If a firm does not have a long-term rating available, we follow Beatty et al. (2008) to estimate an expected rating. The higher the rating, the riskier the firm.

  9. Our results remain robust if we cluster the standard errors at the year level or if we conduct cross-sectional analyses for each sample year (without including the variable \({AF}_{i}\times {Post}_{t}\)).

  10. Following Shipman et al. (2018), we use all the control variables in Eq. (1) for the logistic model predicting whether a firm is covered by analysts.

  11. Untabulated results show that all the control variables are perfectly balanced between test and control firms, as the means of each control variable for test and control firms do not differ significantly.

  12. To obtain the percentage, we divide the coefficient 0.0065 by the mean of the variable R&D, which is 0.054.

  13. The figure reveals that test firms experience a modest decline, relative to control firms, in 2000. However, as shown in Table 3, the decline (–0.0025) is not statistically significant. Thus the difference between firms with analyst following and control firms appears stable prior to Reg FD.

  14. The mean number of patents and citations for our sample with analyst following as control groups (ADR firms as control groups) is 6.33 (16.86) and 1.99 (0.238), respectively. ADR firms’ patent information may be incomplete if foreign firms do not file patents in the United States. However, we believe this is of less concern for two reasons. First, our difference-in-differences design controls for the impact of the possible measurement error as long as the measurement error is consistent over the sample period. Second, we find that the average numbers of patents and citations are both higher for ADR firms than for U.S. firms, indicating that foreign firms file a significant number of patents in the United States.

  15. He and Tian (2013) argue that earnings pressure imposed by analysts induces managers to cut R&D, which results in a negative association between analyst following and patent outcomes. To rule out the possibility that the decrease in innovation after Reg FD is driven by increased earnings pressure from analysts, we construct a new sample requiring firms to have no increase in analyst following in the year after Reg FD. We impose this requirement because increased analyst following likely increases earnings pressure for managers. Untabulated results resemble our findings in Table 7 for tests using both control groups, suggesting that our results are not likely to be explained by the earnings pressure argument.

  16. During our sample period, the top one percent of brokerages employ on average 14% of all analysts based on the I/B/E/S datafile alone or based on the datafile merged with Compustat.

  17. The variable Top is not included in Eq. (4a), as it is perfectly collinear with \(Top\times AF\).

  18. The mean of R&D disclosure for our sample is lower than that of Merkley (2014), which is 30.87. The difference is caused by that fact that Merkley’s sample firms are smaller in total assets (mean = $1.4 billion) than our sample firms (mean = $4.7 billion). This difference is consistent with the expectation that large firms are less likely to publicly disclose their R&D investment, as they are more likely followed by analysts.

  19. Specifically, while R&D investment is expensed to reduce current earnings, capital expenditures are capitalized as assets on the balance sheet and do not decrease earnings immediately. In addition, although stock prices underreact to intangible investments, such as R&D, the literature shows that the underreaction is less problematic for tangible investments, such as capital expenditures (McConnell and Muscarella 1985; Chung et al. 2003).

  20. Scaling changes in R&D by the market value of equity renders the variable ∆RD very small. To ease the presentation of this variable, we multiply ∆RD by 100.

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Acknowledgements

We are grateful for the helpful comments and suggestions from Stephen H. Penman (editor), an anonymous reviewer, Ruiyuan Chen, Yongqiang Chu, Kirsten Cook, Dongmei Li, Chi Wan, Isabel Wang, Joseph Zhang, and seminar participants at the University of North Texas, Texas Tech University, Villanova University, and the 2021 Midwest Section Meeting.

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  1. Wake Forest University, Winston-Salem, NC, USA

    Thomas Canace

  2. Nova Southeastern University, Fort Lauderdale, FL, USA

    Jiao Li

  3. Texas Tech University, Lubbock, TX, USA

    Tao Ma

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Appendices

Appendix 1

Table 9

Table 9 Variable Definitions
Full size table

Appendix 2

Reg FD and Analysts’ Role in Disseminating R&D Information.

In this section, we test the validity of the assumption that analysts’ role in facilitating R&D information in stock price is impaired after Reg FD. To do so, following Lev and Sougiannis (1996), we test the following model:

$${AbnRET}_{i,t+1}= a+{\beta }_{1}{\Delta RD}_{i,t}+{\beta }_{2}{AF}_{i}+{\beta }_{3}{AF}_{i}\times {\Delta RD}_{i,t}+{{\beta }_{4}AF}_{i}\times {Post}_{t}+ {\beta }_{5}{\Delta RD}_{i,t}\times {Post}_{t}+{\beta }_{6}{AF}_{i}\times {\Delta RD}_{i,t}\times {Post}_{t}{ + \beta }_{7}{Beta}_{i,t}+{\beta }_{8}{\mathrm{ln}\left(M\right)}_{i,t}+ {\beta }_{9}{\mathrm{ln}\left(\frac{B}{M}\right)}_{i,t}+{\beta }_{10}{\mathrm{ln}\left(\frac{A}{B}\right)}_{i,t}+{\beta }_{11}\mathrm{ln}{(1+\left(\frac{E}{M}\right))}_{i,t}+{\beta }_{12}{(E/M dummy)}_{i,t}+ { INDUSTR{Y}_{j} +YEAR}_{t}+ {\varepsilon }_{i,t}$$
(1A)

The dependent variable, \(AbnRET\), is the12-month buy-and-hold returns adjusted by value-weighted size and book-to-market portfolio returns starting from four months after the fiscal year t ends. The test variable, \(\Delta RD\), is the change in R&D investment, defined as the difference between current and prior year R&D, scaled by market-value-of-equity. We use the changes in R&D to capture the portion of R&D investment that is unexpected by the market.Footnote 20 Using the change of R&D, instead of the level, is also consistent with the measure of unexpected return (AbnRET), which is the abnormal return over the same period. We scale the change in R&D by market value of equity, instead of assets, to be consistent with the measure of AbnRET, which reflects the changes in the market value per unit of equity. Likewise, following Lev and Sougiannis (1996), all the other scaled variables are scaled by market value of equity. Beta is a firm’s CAPM-based beta measured from 60 monthly stock returns up to one month preceding the return calculation. The variables M, B, and A are market value of equity, book value of equity plus deferred tax, and book value of total assets, respectively. \(\frac{E}{M}\) is the ratio of positive earnings before extraordinary items to the market value of equity, and this variable is 0 when earnings are negative. \(E/M dummy\) is 1 if earnings are negative and 0 otherwise.

The coefficient \({\beta }_{1}\) captures the relationship between unexpected changes in R&D investment and future excess stock return. Consistent with prior studies, we expect \({\beta }_{1}\) to be positive, suggesting the stock market undervalues R&D investment. The coefficient \({\beta }_{3}\) on \(AF\times \Delta RD\) captures the effect of analyst following on the relationship between unexpected R&D investment and abnormal stock return. We expect \({\beta }_{3}\) to be negative, suggesting that stock prices’ delay in R&D valuation is mitigated by analysts. The variable of interest is \(AF\times \Delta RD\times Post\), and its coefficient \({\beta }_{6}\) captures the effect of Reg FD on analysts’ role in facilitating the price incorporation of R&D investment. We expect \({\beta }_{6}\) to be positive and significant, indicating that analysts’ mitigation effect is reduced after Reg FD.

Panel A of Table 9 reports the statistical distributions for variables used in the test. The average (median) abnormal return is –1.5% (–6.8%), the average change in R&D is − 0.161, and the median firm experiences no change in R&D. Around 28.6% of firm-years have negative net income (E/M dummy). Panel B of Table 9 presents the results for estimating Eq. (1A). Consistent with our expectation and prior studies (e.g., Lev and Sougiannis 1996), the coefficient on ∆RD is positive and significant (coefficient = 0.0171, p-value = 0.015), indicating that the stock market undervalues R&D investment. The coefficient on the two-way interaction term (\(AF\times \Delta RD\)) is negative and significant (coefficient =  − 0.0275, p-value = 0.022), suggesting that the undervaluation is more pronounced for firms with no analyst following. The coefficient on \(AF\times \Delta RD\times Post\) is positive and significant (coefficient = 0.0221, p-value = 0.083), which suggests that the underreaction becomes worse for treated firms after Reg FD than for control firms. The evidence is consistent with the expectation that Reg FD constrains analysts’ informational role for R&D investment Table 10.

Table 10 Testing the Validity of Reg RD – Market Return
Full size table

Appendix 3: Analysts’ Forward Earnings Forecast Errors around Reg FD

In this section, we examine whether analysts’ forward earnings forecast accuracy deteriorates after Reg FD, particularly for firms with high R&D investment. Control firms are excluded from this analysis, due to nonexistence of earnings forecasts. If analysts experience a deterioration in their R&D information acquisition, we should expect their earnings forecast errors to increase after Reg FD when the firm they follow has a substantial amount of R&D investment, relative to a firm with low R&D investment.

To do so, we estimate the model following Behn et al. (2008):

$${FERROR}_{i,t,t+2}=\alpha +{\beta }_{1}{HRD}_{i,t}+{\beta }_{2}{Post}_{t}\times {HRD}_{i,t}+{\beta }_{3}Siz{e}_{i,t}+{\beta }_{4}{Earning}_{i,t}+{\beta }_{5}Horiz{on}_{i,t}+ {\beta }_{6}R{etVol}_{i,t}+{\beta }_{7}{AF\_Con}_{i}+INDUSTR{Y}_{j}+{YEAR}_{t}+{\upvarepsilon }_{i,t}.$$

The dependent variable, \({FERROR}_{i,t,t+2}\), is the forecast error of year t + 2 earnings per share (EPS) issued in year t, which is the absolute difference between realized t + 2 EPS and the last consensus (mean) in year t scaled by the beginning year t stock price. We examine year t + 2 EPS forecast error, instead of year t + 1, to account for the possibility that the economic realization of R&D investment on firms’ performance takes longer than one year (e.g., He and Tian 2013). The variable, \(HRD\), is an indicator variable of high R&D, which equals 1 if a firm’s R&D investment is above the median in year t and 0 otherwise. The coefficient \({\beta }_{2}\) on \(Post\times HRD\) captures the effect of Reg FD on high R&D firms’ analysts forecast errors. We predict \({\beta }_{2}\) to be positive, indicating that forecast errors increase after Reg FD for high R&D firms, relative to low R&D firms. Control variables include firms’ book value of asset (Size), net income scaled by market value of equity (Earnings), forecast horizon (Horizon), which is the log value of the number of days between forecast dates and earnings release dates, and daily return volatility (RetVol). Detailed definitions of all variables are provided in Appendix 1.

The results are reported in Table 10. Panel A presents the variable distributions, and the average (median) forecast error is 0.122 (0.018). Panel B of Table 10 reports the regression estimation results. Consistent with our expectation, the coefficient on \(Post\times HRD\) is positive and significant (coefficient = 0.0484, p-value = 0.026), suggesting that forecast errors increase after Reg FD for test firms with high R&D investment. Hence, when a firm’s R&D investment is high, analysts’ information about the valuation of current R&D investment becomes less accurate after Reg FD (Table 11).

Table 11 Testing the Validity of Reg RD – Forecast Error
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Canace, T., Li, J. & Ma, T. Analyst following and R&D investment. Rev Account Stud (2023). https://doi.org/10.1007/s11142-023-09766-9

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