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Korean J Financ Stud > Volume 53(2); 2024 > Article
부정적 언론보도와 자본구조조정의 관계에 관한 연구*

Abstract

This paper examines how negative publicity influences firms’ capital structure decision. We find that firms exhibit faster adjustment of capital structure toward the target level after more negative media coverage, advocating the monitoring role of negative media sentiment on corporate behaviors. Further, our finding is significantly stronger for non-chaebol firms. Additional analyses show that the monitoring role is stronger when firms are more scrutinized by outsiders and have better corporate governance. Overall, this paper shows evidence that negative media sentiment could play an effective role in monitoring firms’ capital structure and correcting their inefficiencies.

요약

본 논문은 기업과 관련한 부정적인 언론보도가 기업의 자본구조조정(capital structure adjustment)에 어떤 영향을 미치는지를 분석한다. 국내 상장기업을 대상으로 자본구조에 관한 부분조정모형을 활용하여 실증분석을 한 결과는 다음과 같다. 첫째, 부정적인 언론 보도가 많을수록 기업은 실제 자본구조를 목표수준으로 빠르게 조절하는 것을 발견하였다. 둘째, 앞서 발견한 결과가 대규모기업집단(재벌)에 속하지 않은 기업에서 강하게 나타남을 보였다. 셋째, 기업의 자본구조조정과 관련한 언론의 감시역할(monitoring role)은 외부이해관계자(재무분석가 및 대형회계법인)로부터 더 많은 감시가 이루어 지거나, 기업지배구조(외국인지분율 및 사외이사의 수)가 우수한 경우에 두드러짐을 확인했다. 본 연구의 결과는 언론이 부정적인 보도를 통해 기업의 자본구조조정에 대한 감시역할을 효과적으로 수행하고 있음을 시사한다.

1. Introduction

One interesting observation in financial news articles is that many of them deliver a warning on corporate leverage. For instance, Asiana Airlines, the second largest South Korean airline company, suffered a contraction in flight demand and piled on debt after Covid-19, causing its leverage to become much higher than previously. This soaring leverage is becoming a concern for Korean Air, which is the largest South Korean airline company and aims to complete an acquisition of Asiana Airlines. This has attracted considerable media attention in regard to Asiana Airlines’ leverage numbers as well as on its impact on Korean Air’s financial health.1)
Firms have their target level of capital structure based on the trade-off between benefits and costs of debt financing (Graham and Harvey, 2001) but they often deviate from the optimal level of leverage. Firms’ adjustment speed toward the optimal level is affected by various factors such as financing frictions (Fischer et al., 1989; DeAngelo et al., 2011), macroeconomic conditions (Cook and Tang, 2010), and institutional environment (Öztekin and Flannery, 2012). While prior studies have examined various determinants of the adjustment speed, there is limited evidence on the role of negative publicity, which can significantly affect firms’ operational decisions (Morellec et al., 2012; Peress, 2014). This paper attempts to fill this gap by exploring how the negative media coverage influences adjustment speed of capital structure.
One can expect that positive tone of media coverage may facilitate faster adjustment of capital structure toward the target level. External financing costs are a primary obstacle of immediate leverage adjustment closer to the optimal level. Since positive media coverage reduces the cost of debt (Dang et al., 2022) and the cost of equity (Kothari et al., 2009), smaller external financing costs attributable to positive media coverage will make firms move their leverage toward the optimal level faster (Dang et al., 2019).
However, we can also formulate an opposite expectation that negative media coverage motivates firms to follow optimal capital structure policy by closing the gap between the actual and target capital structure. The media often scrutinizes firms’ decisions and forms public opinion about the firms (Miller, 2006; Dyck et al., 2010). It also helps detect corporate frauds and reduce the probability of corporate governance violations and profitable insider trading (Dyck et al., 2008; Dai et al., 2015).2) Negative media coverage has a much greater impact on public responses (Soroka, 2006) than positive media coverage which often clouds the true picture of corporate behaviors (Gurun and Butler, 2012; Solomon, 2012; Ahern and Sosyura, 2014). Thus, this negative media coverage may be effective in correcting firms’ behavior (i.e., capital structure adjustment speed).
We note that positive or negative media coverage may not have significant impact on leverage adjustment because, in certain circumstances, the media coverage may not be effective in correcting firms’ behaviors and fail to speed up the firms’ capital structure adjustment. First, the media could be subject to managerial manipulation of the firms that have close private relationships with the media (Gurun and Butler, 2012; Solomon, 2012), increasing sensational and favorable news articles. Second, even when the media provides close monitoring of the firms, the firms may not be responsive enough to correct the inefficiencies. Thus, how the negative media coverage affects firms’ leverage adjustment speed is an empirical question.
In this paper, we employ the data from Korean listed firms and examine the relation between the negative media tone and capital structure adjustment speed.3) The Korean firms provide an interesting research-setting to explore the role of the media.4) Korean economy heavily relies on a few large business groups called chaebols. Chaebols have private relationships with the media companies through ownership or the marriages with family members and, hence, can exercise power over the media companies to obtain positive coverage on themselves (Cho et al., 2021). This implies that a limited number of Korean companies can conjure positive media coverage while most other companies do not have such power. Thus, Korean data allow us to evaluate the negative media coverage’s monitoring role on corporate behaviors as well as its limitations under external pressure.
We analyze 27,456 Korean firm-year observations during the period from 2001 to 2018 after constructing the variables for the tone of media coverage by manually searching news articles from two major Korean economic newspapers (Maeil Business Newspaper and The Korea Economic Daily). We find that more negative media coverage is associated with faster adjustment of capital structure toward the target level, providing a support for the effectiveness of the media’s role as a watchdog. When we split the sample based on the affiliation with large business groups (chaebol or non-chaebol), we find that the association between the negative media coverage and leverage adjustment speed is stronger for non-chaebol firms, implying that a closer relationship between the media and the management impairs the effectiveness of the media as an external monitoring mechanism.
We further observe that our findings are more pronounced for firms under heavier scrutiny by external intermediaries (e.g., frequent analyst coverages and audit service by big audit firms) and firms with better corporate governance (e.g., high foreign shareholding and board independence). These results suggest that media’s monitoring role is complementary to other corporate disciplinary mechanisms, emphasizing the mutual importance of external and internal scrutiny for effective corporate monitoring. Our findings are robust to the use of alternative target leverage estimation, alternative media tone proxies, and alternative sample selection procedures. We also mitigate endogeneity concerns by controlling for confounding effects.
This study contributes to the literature in the following ways. First, consistent with prior literature on the monitoring role of media coverage, we show that the negative media tone serves as a criticism of corporate behaviors and provides an effective monitoring (Baloria and Heese, 2018). We further find that the firm’s private relationships with the media compromise the effectiveness of media coverage. This paper also provides evidence that the media plays a complementary role with other disciplinary mechanisms (e.g., stakeholders’ scrutiny and corporate governance) in corporate decisions. Specifically, the effect of negative media sentiment on firms’ leverage adjustment speed could become more effective when firms are more reactive to negative feedbacks about corporate behaviors.
This paper proceeds as follows: Section 2 summarizes prior related literature and develops our hypotheses; Section 3 describes our empirical research designs; Section 4 reports our main findings; Section 5 performs additional analyses to support the main findings; and Section 6 concludes our paper.

2. Literature Review and Hypothesis Development

2.1 Theories on Capital Structure

Trade-off theory of capital structure suggests that firms have a target level of capital structure, which is set based on the trade-off of benefits and costs of debt financing. For instance, debt financing increases corporate tax saving (Graham, 1996; Korteweg, 2010) and intensifies external monitoring from debt investors (Berger et al., 1997; Morellec et al., 2012). While these benefits motivate firms to increase the leverage through debt financing, debt financing also comes with increased default risk, interest payment, and intensified debtholder-shareholder conflicts (Jensen and Meckling, 1976).
However, the assumption that firms maintain their leverage at the target level is often violated for various reasons, such as market timing of external financing and systematic change of leverage due to retained earnings (e.g., Baker and Wurgler, 2002; Faulkender et al., 2012). When the firm has higher leverage than the target level, it would issue equity or repay the debt to move leverage toward the target. Vice versa, when the firm has lower leverage than the target level, debt issuance or stock repurchase can increase leverage. However, firms sometimes adjust capital structure toward target levels slowly on purpose, raising the argument of the partial adjustment of capital structure (Fama and French, 2002; Strebulaev, 2007). Furthermore, the transaction costs related to agency problems often aggravate the delayed adjustment of capital structure (DeAngelo et al., 2010; Dang et al., 2019).
We note that there are two alternative theories which explain corporate capital structure. First, pecking order theory of capital structure suggests that there is a clear priority in choosing the source of external financing. Particularly, in the presence of adverse selection costs arising from information asymmetry between managers and outside investors, firms prefer issuing debt and use equity financing only as a last resort (Myers, 1984). Shyam-Sunder and Myers (1999) and Petacchi (2015) demonstrate that a positive association between financing deficit and debt financing is stronger when information asymmetry is higher, supporting pecking order theory.
Second, another stream of literature on capital structure focuses on market timing behavior of equity issuance. Loughran and Ritter (1995, 1997) document the market timing of equity issuance such as initial public offering as well as seasoned public offering (see also Dittmar and Thakor, 2007). Baker and Wurgler (2002) report that these market-timed equity issuances have a persistent effect on capital structure.

2.2 Media Coverage on Corporate Activities

The media plays an important role in business environment. First of all, media coverage on corporate activities enhances corporate governance by affecting managerial decisions (Dyck and Zingales, 2002; Miller, 2006; Dyck et al., 2008; Bushee et al., 2010; Dai et al., 2015). As media coverage draws attention from outside stakeholders, managers are often monitored not to pursue their private benefits (Joe et al., 2009; Bednar et al., 2013). In this regard, the media can mitigate the agency conflicts between managers and investors, which is often referred to as the monitoring role of the media. Relatedly, Miller (2006) and Dyck et al. (2010) report that firms under intensive media coverage are likely to be detected for corporate frauds. Dyck et al. (2008) argue that media coverage is negatively associated with the probability of corporate governance violations, while Dai et al. (2015) show that insider trading profits are smaller for firms under more media attention. Kuhnen and Niessen (2012) also document that media attention changes CEO compensation structures.
In addition, the media produces and disseminates information about firms as it processes publicly available corporate information as well as additional information obtained from private investigation. Delivering additional coverage on corporate activities enables the capital market participants to make more informed decisions (Miller, 2006; Dyck et al., 2008; Fang and Peress, 2009; Tetlock, 2010). Through these mechanisms, the media can mitigate the information asymmetry between manager and outside investors. As a result, media coverage reduces bid-ask spreads, a common indicator of information asymmetry (Bushee et al., 2010), and the cost of equity (Kothari et al., 2009).

2.3 Hypothesis Development

On the one hand, we expect positive tone of media coverage to facilitate faster adjustment of capital structure toward the target level. The information dissemination role of media coverage reduces the information asymmetry between the firm and outside investors (Bushee et al., 2010), thus reducing the cost of debt (Dang et al., 2022) and the cost of equity (Kothari et al., 2009). Since external financing costs are a primary obstacle of immediate leverage adjustment closer to the optimal level, smaller external financing costs attributable to positive media coverage will make firms move their leverage toward the optimal level faster (Dang et al., 2019).
On the other hand, the monitoring role of the media helps alleviate moral hazard problems and encourages firms to pursue value-maximizing activities in accordance with the interests of shareholders (Morellec et al., 2012). Compared to positive media coverage, negative media coverage works better as an external governance mechanism by attracting a greater public attention and having a larger impact on corporate behaviors (Baumeister et al., 2001; Soroka, 2006). This is because, while the positive news sentiment often clouds the true picture of corporate behaviors and allows the managers to stick to their current status (Gurun and Butler, 2012; Solomon, 2012; Ahern and Sosyura, 2014), the negative media coverage encourages managers to follow best practices. Thus, we can expect negative, rather than positive, tone of the media to lead firms to narrow the gap between the actual and target capital structure faster.
However, the negative coverage of media may not be effective in accelerating the firms’ capital structure adjustment under certain conditions. Firstly, if firms influence the media through close private relationships (Gurun and Butler, 2012; Solomon, 2012), the quality of the media scrutiny may be undermined, thereby hampering the role of the negative media sentiment in correcting firms’ behaviors. Secondly, even when the media provides thorough monitoring over the firms, the firms may not be responsive enough to correct their inefficiencies.
Collectively, how the media coverage affects firms’ leverage adjustment is an empirical question. Thus, we offer the null hypothesis on the effect of the sentiment of media coverage on leverage adjustment speed as follows:
H1: The sentiment of media coverage is not associated with the adjustment speed of leverage toward the target level.

3. Research Design

3.1 Model Specifications

To measure the target level of capital structure, we first use the following regression model which includes a set of firm-specific characteristics relevant to the costs and benefits of leverage and estimate the fitted value.
(1)
Levi,t+1=β0+β1Sizei,t+β2MedLevi,t+β3MBi,t+β4EBITi,t+β5Tangibilityi,t  +β6R&Di,t+ β7R&D_Dumi,t+ β8Dividendi,t+ β9Depi,t+εi,t+1
The dependent variable (Lev) in Equation (1) is the degree of debt financing captured by the ratio of total debt to total asset. To properly capture the determinants of the leverage ratio, we follow prior research on the partial adjustment of capital structure (e.g., Flannery and Rangan, 2006; Frank and Goyal, 2009) and include firm size (Size), industry median leverage ratio (MedLev), growth opportunity (MB), profitability (EBIT), asset tangibility (Tangibility), the degree of research and development (R&D), an indicator for reporting R&D expense (R&D_Dum), an indicator for dividend payment (Dividend), and non-debt tax shields (Dep) as independent variables. To mitigate endogeneity concerns, we model target leverage in year t+1 as a function of firm characteristics observed in year t.
The predicted value of leverage is used as the economically determined target level of leverage ratio (Lev*). While we use a Fama-MacBeth regression as our main method to estimate Equation (1), we also adopt various approaches including the pooled OLS regression with different sets of fixed-effects and the two-step generalized method of moments (GMM) in order to address possible measurement errors and omitted variable problems (see <Table 6>).
Next, the adjustment speed for leverage toward the target level is estimated using the following partial adjustment model.
(2)
ΔLevi,t+1=γ0+γ1ΔLev*i,t+1+γnControls+Industry Dummies+Year Dummies+εi,t
In Equation (2), the dependent variable is the leverage adjustment captured by the change in the leverage ratio from year t to year t+1 (∆Levi,t+1 = Levi,t+1 - Levi,t). The main independent variable is the deviation of actual leverage ratio from the target ratio (∆Lev*i,t+1 = Lev*i,t+1 - Levi,t). The coefficient γ1 on the deviation from the target level (∆Lev*) represents the adjustment speed of leverage. If firms perfectly adjust their leverage ratio toward the target, the adjustment speed γ1 would be equal to 1. However, if firms do not fully adjust for the deviation, γ1 would be less than 1. The firm characteristics used in Equation (1) are also included as control variables. To further address variations in leverage across industry and year, we control for industry and year-fixed effects. Particularly, we note that it is imperative to capture time-variant macroeconomic conditions through year-fixed effects because market-timing equity issuance has a persistent effect on capital structure (Baker and Wurgler, 2002), and macroeconomic conditions are critical in corporate equity issuance (Chen, 2010; Erel et al., 2011). We cluster the standard errors by firm to adjust for within-firm correlation of estimation residuals.
Based on Equation (2), we examine the effect of the media scrutiny on the adjustment speed of leverage by including the interaction term between the media variables (MediaTone) and the deviation variable (∆Lev*). Specifically, we estimate the following regression model to test our hypotheses.
(3)
ΔLevi,t+1=γ0+γ1ΔLev*i,t+1+γ2ΔLev*i,t+1×MediaTonei,t+γ3MediaTonei,t +γnControls+Industry Dummies+Year Dummies+εi,t           
We use MediaTone variables measured in the year t to estimate its effect on the leverage structure adjustment in the year t+1 in order to mitigate the concern of reverse causality. To construct the variables for media tone (MediaTone), we search news articles from two Korean economic daily journals (i.e., Maeil Business Newspaper, and The Korea Economic Daily) based on their sales and circulation.5) Using the translated version of the Harvard-IV-4 dictionary (Kim et al., 2017), we calculate the number of positive, negative, and total word counts in news articles in which a firm’s name or stock code is mentioned in the year (Cho et al., 2021).6) PosTone (NegTone) is measured as the sum of the number of positive (negative) word counts divided by the sum of the total number of words in the news articles in which a firm’s name or stock code is mentioned in each year. MediaTone is measured as the difference between NegTone and PosTone so that a positive value indicates net negative publicity of the firm.
The coefficient of our interest is γ2, the coefficient on the interaction term between negative media variables and the deviation from the target leverage. If the negative media coverage speeds up the leverage structure adjustment, the coefficient would be statistically significant and positive. In contrast, if the negative media coverage slows down the adjustment speed, we would observe a significantly negative value in the coefficient. In addition to the aforementioned control variables, we also include indicator variables for year and industry, and cluster the standard errors by firm.

3.2 Sample

Our initial sample includes all Korean listed firms during the period 2001 to 2018. We obtain accounting and financial data in the DataGuide database provided by FnGuide, the major data provider of Korean companies. The data for media variables are collected from two major Korean economic daily journals (Maeil Business Newspaper and The Korea Economic Daily). We retain only non-financial firms due to the concern that companies in the financial industry may possess significantly different leverage from those in other industries. Also, we restrict the sample to firms with December fiscal year-end to measure media coverage variable for the same period across firms. Lastly, we delete firms with missing variables required in Equation (1). The final sample consists of 27,456 firm-year observations. All continuous variables are winsorized at the 1% and 99% levels to mitigate the effects of outliers.
<Table 1>
Sample Selection
This table summarizes sample selection procedure for firm-year observations.
Firm-years listed on the Korean Stock Exchange or Korean Securities left Automated Quotations (2001-2018) 59,058

Less:
 Firm-years in financial-industry (3,170)
 Firm-years with non-December year-end (4,152)
 Firm-years without market value variables (17,747)
 Firm-years without firm-specific accounting variables (6,533)
Final sample 27,456

4. Empirical Results

4.1 Descriptive Statistics

<Table 2> Panel A presents summary statistics for variables used in the analyses. The mean value of Levi,t+1 is 0.445, indicating that the total liabilities account for, on average, 44.5% of the total assets. The average change in the capital structure (∆Levi,t+1) is -0.015, which suggests that firms tend to reduce their leverage ratio during the sample period with mean values of PosTone, NegTone, and MediaTone being 0.078, 0.144 and 0.066, respectively. These suggest that the media tends to convey more negative news than positive news for the firm-year observations in our sample.7) <Table 2> Panel B presents the annual mean values of our primary variable, MediaTone, which spans from 0.036 in 2018 to 0.099 in 2007. While the annual mean values generally demonstrate only minor deviations from the sample mean of 0.066 during most periods, it is noteworthy that the highest values are concentrated in the period of the 2007-2009 financial crisis identified by the National Bureau of Economic Research (https://www.nber.org/cycles.html).
<Table 2>
Descriptive Statistics
This table reports the descriptive statistics of variables used in the analyses in Panel A, and yearly average of net negative media tone (MediaTone) in Panel B. All variables are winsorized at the top and bottom one percentile. Variable definitions are presented in the Appendix.
Panel A: Descriptive Statistics
Variable Mean SD Min Q1 Median Q3 Max
Levi,t+1 0.445 0.222 0.054 0.270 0.440 0.598 1.145
Levi,t+1 0.445 0.114 -0.023 0.367 0.440 0.517 1.064
Levi,t+1 -0.015 0.157 -0.862 -0.034 0.005 0.042 0.396
Sizei,t 18.819 1.529 16.144 17.762 18.551 19.589 23.864
MedLevi,t 0.440 0.082 0.242 0.397 0.437 0.481 0.707
MBi,t 1.539 1.655 0.111 0.605 1.012 1.798 10.597
EBITi,t 0.027 0.161 -0.745 -0.003 0.047 0.098 0.425
Tangibilityi,t 0.287 0.190 0.003 0.134 0.270 0.416 0.778
R&Di,t 0.012 0.023 0.000 0.000 0.001 0.014 0.128
R&D_ Dumi,t 0.632 0.482 0.000 0.000 1.000 1.000 1.000
Dividendi,t 0.543 0.498 0.000 0.000 1.000 1.000 1.000
Depi,t 0.009 0.013 0.000 0.002 0.005 0.010 0.085
PosTonei,t 0.078 0.022 0.000 0.064 0.078 0.091 0.141
NegTonei,t 0.144 0.032 0.000 0.127 0.141 0.159 0.244
MediaTonei,t 0.066 0.035 -0.010 0.043 0.065 0.088 0.163
Chaeboli,t 0.129 0.336 0.000 0.000 0.000 0.000 1.000

Panel B: Yearly Average of Negative Media Tone

Variable = MediaTone

Year N Mean SD Year N Mean SD

2001 1,099 0.059 0.022 2010 1,518 0.058 0.027
2002 1,219 0.061 0.026 2011 1,562 0.066 0.030
2003 1,271 0.068 0.027 2012 1,560 0.063 0.027
2004 1,302 0.065 0.027 2013 1,609 0.070 0.031
2005 1,376 0.066 0.030 2014 1,661 0.063 0.035
2006 1,429 0.075 0.029 2015 1,744 0.061 0.036
2007 1,490 0.099 0.034 2016 1,821 0.064 0.039
2008 1,485 0.092 0.031 2017 1,877 0.057 0.041
2009 1,476 0.083 0.036 2018 1,957 0.036 0.029
<Table 3> shows the Pearson correlation among variables used in the analyses. In this univariate analysis, Levi,t+1 has a positive and statistically significant (p<0.01) correlation with MediaTone. ∆Levi,t+1 is negatively associated with MediaTone. It suggests that the more negative coverage about a firm is, the more the firm reduces leverage ratio. Lastly, the overall correlations between Levi,t+1 and independent variables used in Equation (1) are statistically significant at the 1% level, supporting the use of these variables as economic determinants for the corporate capital structure.
<Table 3>
Correlation Matrix
This table presents the Pearson correlation matrix among variables used in the analyses. Coefficients in bold indicate the statistical significance at 1% level. Variable definitions are presented in the Appendix.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)
(1) Levi,t+1 1.000
(2) Lev*i,t+1 0.513 1.000
(3) Levi,t+1 -0.018 -0.196 1.000
(4) Sizei,t 0.178 0.346 0.130 1.000
(5) MedLevi,t 0.271 0.529 -0.018 0.217 1.000
(6) MBi,t 0.024 0.047 -0.171 -0.182 -0.207 1.000
(7) EBITi,t -0.240 -0.469 0.353 0.222 0.021 -0.167 1.000
(8) Tangibilityi,t 0.163 0.318 0.042 0.186 0.153 -0.157 0.072 1.000
(9) R & Di,t -0.123 -0.240 0.003 -0.164 -0.179 0.236 -0.028 -0.131 1.000
(10) R & D_ Dumi,t 0.016 0.031 -0.006 0.034 -0.058 0.081 -0.017 -0.027 0.401 1.000
(11) Dividendi,t -0.238 -0.465 0.241 0.316 0.050 -0.211 0.437 0.090 -0.075 -0.026 1.000
(12) Depi,t 0.001 0.003 -0.107 -0.168 -0.169 0.186 -0.162 -0.141 0.158 0.063 -0.138 1.000
(13) PosTonei,t -0.062 -0.060 0.113 0.243 -0.005 -0.026 0.177 0.091 -0.006 0.006 0.160 -0.058 1.000
(14) NegTonei,t 0.046 0.027 -0.017 -0.064 0.037 -0.069 -0.060 0.045 -0.028 -0.001 -0.054 -0.095 0.195 1.000
(15) Tonei,t 0.081 0.062 -0.089 -0.214 0.037 -0.047 -0.168 -0.016 -0.024 -0.006 -0.150 -0.051 -0.436 0.790 1.000
(16) Chaeboli,t 0.114 0.232 0.052 0.565 0.080 -0.039 0.069 0.058 -0.101 -0.018 0.144 -0.001 0.102 -0.055 -0.117 1.000

4.2 Negative Media Tone and Speed of Leverage Adjustment

<Table 4> reports the main test results. To estimate the first-stage regression using Equation (1), we adopt the Fama-MacBeth’s cross-sectional estimation for each year and report the mean values and t-statistics of the slopes of the 18 annual regressions in Column (1).8) Macroeconomic conditions and consequent market-timing financing activities have persistent effects on capital structure (Baker and Wurgler, 2002). We expect that Fama-MacBeth cross-sectional estimation of the first-stage regression would mitigate the concern that our second-stage estimation is influenced by market-timing financing activities.
<Table 4>
Negative Media Tone and Speed of Leverage Adjustment
This table presents the result of estimating Equations (1), (2), and (3). Column (1) reports the first-stage regression result of Equation (1) using the Fama-MacBeth estimation. Column (2) reports the baseline second-stage results of Equation (2). Column (3) shows the second-stage results of Equation (3) which examines the impact of media scrutiny on leverage adjustment speed. In addition, we split the sample based on the firm’s business group affiliation (non-chaebol in Column (4) and chaebol firms in Column (5)) and separately rerun Equation (3). We cluster standard errors at the firm level. t-statistics are presented in the parentheses. *, **, and *** indicate statistical significance at 10%, 5% and 1% levels, respectively. Variable definitions are presented in the Appendix.
(1) (2) (3) (4) (5)

Model = First-stage Second-stage
Dep. Var. = Levi,t+1 ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1
Sample All All All Non-chaebol Chaebol
ΔLev*i,t+1 0.385*** 0.210*** 0.240*** 0.082
(46.32) (13.34) (14.78) (1.52)
ΔLev*i,t+1 2.315*** 2.143*** 1.752**
×MediaTonei,t (12.97) (11.91) (2.38)
MediaTonei,t 0.071** 0.096*** -0.051
(2.31) (2.96) (-0.67)
Sizei,t 0.035*** 0.002** 0.003** 0.005*** -0.004**
(16.28) (1.73) (2.54) (3.83) (-2.47)
MedLevi,t 0.585*** -0.266*** -0.249*** -0.273*** -0.132***
(37.61) (-11.60) (-10.98) (-10.97) (-3.62)
MBi,t 0.011*** -0.006*** -0.006*** -0.006*** -0.000
(8.04) (-8.55) (-8.30) (-7.95) (-0.10)
EBITi,t -0.280*** 0.165*** 0.162*** 0.160*** 0.089***
(-10.97) (20.35) (20.36) (19.66) (3.18)
Tangibilityi,t 0.145*** 0.011 0.011* 0.016** -0.002
(9.91) (1.59) (1.65) (2.14) (-0.16)
R&Di,t -0.774*** 0.199*** 0.175*** 0.204*** -0.075
(-9.50) (3.82) (3.48) (3.88) (-0.52)
R&D_ Dumi,t 0.021*** -0.002 -0.003 -0.002 -0.001
(8.19) (-0.80) (-1.02) (-0.60) (-0.36)
Dividendi,t -0.109*** 0.016*** 0.017*** 0.014*** 0.036***
(-27.94) (6.88) (7.24) (5.49) (8.65)
Depi,t 0.347*** -0.200** -0.177** -0.143 -0.045
(2.96) (-2.23) (-2.05) (-1.47) (-0.32)
Intercept -0.466*** -0.024 -0.030 0.020 0.063**
(-9.93) (-1.09) (-1.41) (0.47) (2.15)
Fixed effects - Industry/Year Industry/Year Industry/Year Industry/Year
Cluster - Firm Firm Firm Firm
Observations 27,456 27,456 27,456 23,902 3,554
Adjusted R2 0.249 0.486 0.501 0.519 0.259
We find that all the coefficients in the model are significant at the 1% level, consistent with the findings from prior studies.9) After deriving the target leverage ratio from the predicted value of Equation (1), we estimate the second-stage baseline regression using Equation (2) and report the results in Column (2). The coefficient on the deviation from the target level (∆Lev*) is 0.385 with statistical significance at the 1% level, which indicates that firms close the gap between the target and actual leverage ratio by 38.5% each year during the sample period.
In Column (3), we report the results of estimating Equation (3) which includes the media scrutiny variable. The coefficient on the interaction term between ∆Lev* and MediaTone is significantly positive (coefficient=2.315, t-value=12.97), indicating that the adjustment speed of capital structure is faster for firms receiving more negative media coverage.10) This finding suggests that negative media coverage is an effective trigger that increases various stakeholders’ recognition about firms’ inefficiencies. In other words, the negative publicity can serve as a monitoring role, which effectively mitigates managers’ inefficient financing decisions.11) This is quite opposite to Dang et al. (2019) who show that more positive media sentiment facilitates faster leverage adjustment. Considering that the difference mainly relies on several discrepancies in empirical specifications between an international setting and a single-country setting, we cautiously argue that our results may better reflect the cases in a Korean business environment, which is in line with the monitoring role of the media. Negative information attracts a greater attention and has a larger impact than positive information (Baumeister et al., 2001). In the capital market, negative news is more useful in revealing the firms’ inefficiency or wrongdoing (Joe et al., 2009; Liu and McConnell, 2013; Li et al., 2021). Thus, compared to Dang et al. (2019), our finding that negative rather than positive tone of media coverage plays a monitoring role in corporate behavior is more closely aligned with prior studies on the media’s role in the capital market.12)
In Columns (4) and (5), we spilt the sample based on the firms’ business group affiliations (namely, chaebol or non-chaebol firms) and rerun Equation (3) to examine the differential effect of media tone on leverage adjustment tone depending on the firms’ private relationships with media companies. We find that the coefficient on ∆Lev* and MediaTone for chaebol firms is smaller than that for non-chaebol firms, indicating that the monitoring role of negative media coverage is compromised when the media has close relationships with the firm. Put differently, given that chaebols have power over the media company to obtain positive coverage on themselves through the private relationship (e.g., ownership or the marriages with family members) (Cho et al., 2021), the effectiveness of the media’s monitoring role in the capital structure adjustment may be less pronounced for the chaebol firms.

5. Additional Analyses

In this section, we conduct several additional tests to support our main findings.

5.1 Cross-sectional Analyses

We further conduct cross-sectional analyses to examine whether the negative media coverage complements or substitutes other disciplinary mechanisms in corporate monitoring. On one hand, the monitoring role of media may be more effective when firms have external or internal mechanisms to promptly react to the negative media coverage, leading to a complementary relationship of media coverage and other disciplinary mechanisms. On the other hand, strong external or internal disciplinary mechanisms may weaken the media’s monitoring role, leading to a substitutive relationship between media coverage and other disciplinary mechanisms. To examine these conflicting expectations, we use outside stakeholders’ scrutiny (e.g., analyst coverage and Big4 auditors) and corporate governance (e.g., foreign ownership and board independence) to proxy other corporate disciplinary mechanisms.
First, we divide the sample into two subsamples based on the sample median value of the number of analysts following and into firms audited by small non-Big4 audit firms and those audited by large Big4 audit firms. Next, we divide the sample into two subsamples based on the sample median value of the foreign shareholder ownership and into the other two subsamples based on the sample median value of the percentage of outside directors. Using the four sets of the subsamples, we re-estimate Equation (3).
In Panel A of <Table 5>, the results indicate that the association between negative media coverage and leverage adjustment speed is more pronounced when firms are covered by more analysts and audited by Big4 auditors. Also, in Panel B, the results show that firms speed up the leverage adjustment in response to the media scrutiny when they have greater foreign ownership and a more independent board of directors. These results suggest that firms with outside stakeholders’ stronger scrutiny and enhanced corporate governance become more reactive to negative news coverage. Overall, the findings indicate that negative media coverage plays a complementary role with other disciplinary mechanisms in corporate governance.
<Table 5>
Cross-Sectional Tests
This table presents the result of estimating Equation (3) for cross-sectional analyses. Panel A reports the results of the subsample analyses based on outside stakeholders’ scrutiny and Panel B reports the results of the subsample analyses based on corporate governance. We cluster standard errors at the firm level. t-statistics are presented in the parentheses. *, **, and *** indicate statistical significance at 10%, 5% and 1% levels, respectively. Variable definitions are presented in the Appendix.
Panel A: Outside stakeholders’ scrutiny
(1) (2) (3) (4)

Sub-sample = Small # of Analyst Large # of Analyst Non-Big4 Auditor Big4 Auditor
Dep. Var. = ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1
ΔLev*i,t+1 0.318*** 0.064** 0.290*** 0.135***
(17.75) (2.52) (15.33) (6.15)
ΔLev*i,t+1 × MediaTonei,t 1.519*** 2.525*** 1.914*** 2.465***
(7.97) (6.41) (9.30) (9.00)
MediaTonei,t 0.063* 0.029 0.087** 0.060
(1.77) (0.63) (2.01) (1.55)
Diff. test of
ΔLev*i,t+1 × MediaTonei,t
Prob > Chi2 0.0210** 0.0993*
Control variables Included Included Included Included
Fixed effects Industry/Year Industry/Year Industry/Year Industry/Year
Cluster Firm Firm Firm Firm
Observations 16,906 10,550 13,710 13,656
Adjusted R2 0.556 0.268 0.559 0.405

Panel B: Corporate governance

(1) (2) (3) (4)

Sub-sample = Low %
Foreign
Shareholding
High %
Foreign
Shareholding
Small # of
Outside
Director
Large # of
Outside
Director
Dep. Var. = ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1

ΔLev*i,t+1 0.333*** 0.083*** 0.236*** 0.178***
(17.15) (4.22) (12.38) (7.26)
ΔLev*i,t+1 × MediaTonei,t 1.396*** 3.259*** 1.994*** 2.741***
(6.50) (12.96) (9.12) (9.78)
MediaTonei,t 1.396*** 3.259*** 1.994*** 2.741***
(6.50) (12.96) (9.12) (9.78)
Diff. test of
ΔLev*i,t+1 × MediaTonei,t
Prob > Chi2 0.0000 *** 0.0320 **
Control variables Included Included Included Included
Fixed effects Industry/Year Industry/Year Industry/Year Industry/Year
Cluster Firm Firm Firm Firm
Observations 14,051 13,384 12,874 9,233
Adjusted R2 0.565 0.455 0.487 0.524

5.2. Robustness Tests

We conduct several robustness tests. First, while we use the predicted value from the Fama-MacBeth regression as our main target capital structure in the previous empirical analyses, we employ alternative methods to estimate the target leverage ratio.
In Panel A of <Table 6>, we report the results using pooled OLS estimation with industry- and firm-fixed effects (Column (1)), that with firm- and year-fixed effects (Column (2)), and dynamic GMM estimation (Column (3)) (Faulkender et al., 2012; Öztekin and Flannery, 2012).13)
<Table 6>
Robustness tests
This table presents the results of robustness tests. Panel A shows the results of estimating Equation (3) using alternative methods in the first-stage regression. Columns (1) and (2) use a pooled OLS estimation with industry/year fixed effects and firm/year fixed effects, respectively, and Column (3) uses the two-step dynamic GMM estimation. Panel B shows the results of estimating Equation (3) with including additional control variables. We cluster standard errors at the firm level. t-statistics are presented in the parentheses. *, **, and *** indicate statistical significance at 10%, 5% and 1% levels, respectively. Variable definitions are presented in the Appendix.
Panel A: Using alternative methods in the first-stage regression
(1) (2) (3)

First-stage regression = OLS OLS GMM
Dep. Var. = ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1
ΔLev*i,t+1 0.201*** 0.215*** 0.568***
(12.69) (14.45) (31.31)
ΔLev*i,t+1 × MediaTonei,t 2.374*** 2.167*** 1.441***
(13.11) (13.17) (7.83)
MediaTonei,t 0.066** 0.069** 0.021
(2.13) (2.23) (0.88)
Controls variables Included Included Included
Fixed effects Industry/Year Firm/Year Industry/Year
Cluster Firm Firm Firm
Observations 27,456 27,456 24,926
Adjusted R2 0.495 0.502 0.600

Panel B: Using additional control variables

(1) (2) (3) (4)

First-stage regression = FM OLS OLS GMM
Dep. Var. = ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1 ΔLevi,t+1

ΔLev*i,t+1 1.026*** 1.014*** 0.879*** 1.523***
(12.76) (12.60) (12.04) (15.26)
ΔLev*i,t+1 × MediaTonei,t 0.609*** 0.661*** 0.601*** 0.579***
(4.64) (4.97) (4.94) (3.27)
ΔLev*i,t+1 ×Overi,t 0.231*** 0.223*** 0.225*** 0.074**
(12.93) (12.27) (12.02) (2.51)
ΔLev*i,t+1 × Defi,t 0.011 0.015 0.012 0.054***
(0.93) (1.27) (1.12) (3.32)
ΔLev*i,t+1 ×Overi,t × Defi,t 0.009 0.015 0.004 0.058**
(0.52) (0.81) (0.26) (2.52)
ΔLev*i,t+1 × MeidaCoveragei,t 0.062*** 0.067*** 0.071*** 0.074***
(3.97) (4.17) (4.83) (3.42)
Standalone controls variables Included Included Included Included
Controls variables interacted with Included Included Included Included
Fixed effects Industry/Year Industry/Year Firm/Year Industry/Year
Cluster Firm Firm Firm Firm
Observations 27,456 27,456 27,456 24,926
Adjusted R2 0.607 0.602 0.608 0.636
Second, in Panel B, we add additional control variables in the model to mitigate the confounding effects of other capital structure determinants. We add the variable of having leverage higher than the optimal level (Over) because financing activities and consequent capital structure dynamics could be different across firms having higher and lower leverage than the optimal level (Byoun, 2008). We construct and add the variables of financing deficit (Def) to address the concern that pecking order theory would bias our empirical results. This is because pecking order theory of capital structure suggests that information asymmetry between managers and outside investors determines the pecking order of external financing channel through which firms fund their financing deficit (Myers, 1984; Shyam-Sunder and Myers, 1999). We also add the number of media coverage (MediaCoverage) as an additional control variable to address the possible effect of media attention itself rather than its tone.
The result in Panel B of <Table 6> shows the positive and significant coefficients on ∆Lev*×Over, suggesting that the over-leveraged firms are more likely to speed up their capital structure adjustment than the under-leveraged firms. Also, the coefficients on ∆Lev*×MediaCoverage are significantly positive, implying that more media coverage could spur firms to adjust their capital structure more rapidly. Interaction terms of financing deficit (∆Lev*×Def and ∆LevOver×Def) are largely insignificant, suggesting that pecking order theory does not dominate in our sample. More importantly, the coefficients on ∆Lev* and MediaTone remain to be significantly positive in all columns. This indicates that our findings are robust as to the consideration of different financing behaviors of high- vs. low-leverage firms, financing deficit in the spirit of pecking order theory, and the degree of media coverage.
Until now, we measure MediaTone variables using the aggregated coverage from the two major economic newspapers in Korea, Maeil Business Newspaper and Korea Economic Daily. However, there is a possibility that the aggregated measure could fail to properly capture the media’s general role in the corporate environment due to the two companies’ different incentive structures.14) Thus, we use an alternative measure for media sentiment by separately using news articles of each business press. Using the media company-specific attention variables, we confirm that results are not different across the two media companies (untabulated).
Lastly, we conduct an analysis to assess whether our results are driven by specific extreme macroeconomic events, particularly the 2007-2009 financial crisis. As demonstrated in <Table 2> Panel B, we observe an increase in net negative media tone during the financial crisis period. Furthermore, the cost associated with adjusting capital structure may have changed significantly during this period, potentially impacting the relation between media tone and the speed of capital structure adjustment (Chen, 2010; Erel et al., 2011). To address this concern, we partition our sample into three groups based on the sample period; prior to 2007, from 2007 to 2009, and since 2010, and rerun Equation (3) for each of the subgroups. In untabulated results, our main findings remain robust across different periods.15)

6. Conclusion

This study investigates how the media influences firms’ leverage adjustment practices by focusing on the effect of negative media coverage. Using Korean data, we find that firms exhibit faster adjustment of capital structure toward the target level after receiving more negative media coverage, advocating the critical role of negative media sentiment on corporate behaviors. We further find that faster speeds of leverage adjustments under more negative media attention are more pronounced for firms with stronger disciplinary mechanisms. Overall, our findings support the view that the negative publicity brought by the negative media coverage facilitates the adjustment of corporate behaviors toward optimality.
Despite our concrete results, several caveats are in order. First, our findings may not be generalized to countries with different institutional backgrounds and information environments. Second, while we examine the effect of the media coverage on the adjustment speed of capital structure, we rely on general media attention measures rather than on specific news coverage on capital structure (leverage). Lastly, although the Korean translated version of the Harvard-IV-4 dictionary is used for the construction of the media tone variables, there is no consensus to construct media tone variables using accounting and finance documents written in Korean thus far. Therefore, we caution readers that the aforementioned caveats could affect our findings. Nevertheless, our study provides practitioners, regulators, and academics with critical insights on the monitoring role of negative media coverage in capital structure adjustment, because, to the best of our knowledge, this is the first empirical evidence on the positive relation between negative media coverage and leverage adjustment speed. We anticipate future research to further examine the effects of negative media coverage on various corporate decisions.

Notes

1) One example is the article by The Korean Times on 16 August, 2022. Available at https://www.koreatimes.co.kr/www/tech/2022/11/129_334505.html (accessed on 26 Nov, 2022).

2) While positive media coverage often justifies the current status, negative media coverage serves as an external governance mechanism by revealing firms’ inefficiencies and correcting corporate behaviors (Baumeister et al., 2001).

3) While Dang et al. (2019) show empirical evidence using international data, institutional setting and media’s role may be different in each country. Thus, the empirical analysis using international data is subject to delicate consideration of correlated omitted factors such as country-level variation in corporate governance or legal regime (e.g., La Porta et al., 1999; 2000). Holding these factors constant in a single capital market, we provide more direct evidence on the media’s roles in corporate leverage decision.

4) Prior studies document that the effect of media coverage differs significantly across countries because the unbiasedness of media coverage is affected by the degree of media freedom (Wang and Ye, 2014; Chen et al., 2022).

5) The RavenPack contains comprehensive global news articles released through the Dow Jones Newswire, regional editions of the Wall Street Journal, Baron’s and other internet sources including financial sites, blogs, local and regional newspapers. Dow Jones have over 2,000 journalists reporting worldwide who publish local language news and then quickly translate the news into English for their financial newswires. However, in the press lists of RavenPack, two major Korean economic daily journals (i.e., Maeil Business Newspaper and The Korea Economic Daily) are not included; and some translated news articles in the Dow Jones Newswire may not fully convey the contents of news articles and their readers are also likely to understand in different way to how they are originally written. In this sense, using media coverage written in local language of all public firms in the single country enables us to overcome the limitation of using RavenPack database and provides a purified setting to examine the media’s role.

6) Kim et al. (2017) translated the Harvard-IV-4 dictionary into Korean, and the authors provide the positive and negative word lists, which are available at https://sites.google.com/site/andyyhankim/my-papers/data. However, we acknowledge its limitation since the translation completeness of the set of lexicons is not guaranteed.

7) In contrast, the mean value of Korean news articles from RavenPack database used in Dang et al. (2019) suggests positive tone of media coverage.

8) We note that our results remain largely unchanged when we replace the measure for the book-value based leverage ratio with the market-value based leverage ratio.

9) We observe that firms with large size, high growth opportunities, high tangible assets ratios, R&D expenditures, and high depreciation expenses tend to have high leverage ratios. Also, as predicted, the industry median leverage ratio is also positively associated with the firm-level leverage ratio. By contrast, firms with high profits, huge R&D expenditures, and large dividend payments tend to have low leverage.

10) We also observe that firms with large size, high profits, R&D expenditures, and large dividend payments tend to increase the leverage ratio; however, firms with high growth opportunities and high depreciation expenses tend to decrease the leverage ratio. In addition, the industry median leverage ratio is negatively associated with the firm-level leverage adjustment.

11) When replacing MediaTone with PosTone and NegTone, we find that the coefficient on the interaction term between ∆Lev* and PosTone is significantly negative and the coefficient on the interaction term between ∆Lev* and NegTone is significantly positive, suggesting that the adjustment speed toward the target capital structure becomes faster (slower) if the tone of media coverage is more negative (positive).

12) In an addition test, we test the effect of media coverage frequency instead of media tone. Specifically, we replace MediaTone with the annual decile rank transformed media coverage frequency (MediaCoverage) in Equation (3) and find that greater media coverage (more frequent media attention) is associated with faster adjustment of capital structure toward the target level, which is consistent with Dang et al.’s (2019) findings. However, as aforementioned, our paper is different from Dang et al. (2019) in that we focus on the negative media coverage.

13) In the dynamic GMM analyses, the sample size decreases to 24,926 firm-years.

14) As aforementioned, Korean business groups (chaebols) often have close relationships with the media companies; the founder family of Maeil Business Newspaper has a marriage tie with one of the chaebol groups, the Samyang Group, and the Korea Economic Daily is owned by firms affiliated with chaebols. In particular, the controlling shareholder of The Korea Economic Daily is Hyundai Motor Company, one of the largest chaebol groups, and its other shareholders consist of approximately 190 for-profit firms affiliated with major chaebol groups (e.g., Samsung, LG, SK). Thus, two business presses can have different views on the same news events.

15) We also employ the alternative sample selection procedure. We initially restrict the sample to firm-year observations with December fiscal year-ends to ensure the consistency with the measurement period of the media variable. Nonetheless, after relaxing this constraint and including observations with non-December fiscal year-ends, we confirm that our findings remain consistent for the broader sample of firms across all the different estimation methods in the first stage regression (untabulated), alleviating concerns about the generalizability of the results.

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Appendices

<Appendix>

Variable Definitions

Variables Definition
Lev The leverage ratio calculated by the total liabilities divided by total assets;
∆Lev The difference between Lev and Lev in the previous year (∆Levi,t+1 = Levi,t+1 - Levi,t);
∆Lev* The difference between target leverage ratio and Lev in the previous year with the target ratio estimated from Equation (1) (∆Lev*i,t+1 = Lev*i,t+1 - Levi,t);
PosTone Positive tone of media coverage, measured as sum of the number of positive word counts divided by the sum of the total number of words in the news articles in which a firm’s name or stock code is mentioned in each year;
NegTone Negative tone of media coverage, measured as sum of the number of negative word counts divided by the sum of the total number of words in the news articles in which a firm’s name or stock code is mentioned in each year;
MediaTone The tone of media coverage, measured as sum of the number of negative word counts minus the sum of the number of positive word counts, divided by the sum of the total number of words in the news articles in which a firm’s name or stock code is mentioned anywhere in each year;
Size The natural logarithm of total assets;
MedLev Industry median leverage ratio, calculated as the median debt ratio of the industry where industry is defined by the two-digit KSIC code of the firm;
MB Market-to-book ratio, calculated as the market value of equity divided by book value of equity;
EBIT Earnings before income and taxes scaled by total assets;
Tangibility Property, plant, and equipment divided by total assets;
R&D R&D expenditure divided by total assets;
R&D_Dum An indicator variable that equals to one if the firm has R&D expenditure in the year, and zero otherwise;
Dividend An indicator variable that equals to one if the firm pays common dividend, and zero otherwise;
Dep Depreciation expenses scaled by total assets;
Chaebol An indicator variable that equals to one if the firm belongs to one of the business conglomerates which is defined by the Korea Fair Trade Commissions (KFTC), and zero otherwise;
Over An indicator variable that equals to one if the firm is over-levered relative to target leverage ratio, and zero otherwise;
Def An indicator variable that equals to one if the firm has a financing deficit, and zero otherwise;
MediaCoverage The annual decile rank transformation ranging from 0 to 1 for the number of news articles in which a firm’s name or stock code is mentioned anywhere in each year.
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