3.1. Definition of variables

In this study, Tobin's Q is used as a measure of firm value. Tobin's Q is the market value of assets divided by the replacement cost. The higher the value, the higher the firm value because the market value of the enterprise is evaluated higher than the replacement cost in the market. The market value of assets is measured as the sum of the market value of equity and the book value of debt, and the replacement cost is calculated as the book value of the asset.

Dividend pressure of NPS is measured by the following method. First, because NPS does not directly disclose a list of low dividend companies, it uses the level of cash dividend and whether or not NPS owns more than 5% stake because it sees a baseline stake, typically 5%, that can control management and influence the company. In addition, in Korea, it is obligatory to report related contents when holding more than 5% of the shares of a listed corporation, so information on the proportion of NPS's investment can be obtained. We consider the dummy variable (NPF) which distinguishes the cases where NPS has more than 5% stake. The dividend level is measured by cash dividend to total asset ratio (DIVTA), cash dividend to net income ratio (DIVOUT), and cash dividend to paid-in capital ratio (DIVRATE). In addition, if a company with a high NPS stake increases its cash dividend level in the following period, it is highly likely that NPS has given dividend pressure to the company. Therefore, we also consider the change variable of the dividend levels (Δ DIVTA, Δ DIVOUT, Δ DIVRATE). In this chapter, we use the intersection term between the dividend level variables (DIVTA, DIVOUT, DIVRATE, Δ DIVTA, Δ DIVOUT, Δ DIVRATE, and the dummy variable (NPS)) to measure the probability of NPS dividend pressure.

## 3.2. Research model

In this study, the dividend pressure of NPS is measured using whether NPS invests in equity of 5% or more. Therefore, the nature of a company that NPS has invested in a large amount can affect the outcome. We use propensity score matching (PSM) to control self-selection bias. PSM has the advantage of reducing the impact of certain variables and providing more accurate statistics when analyzing groups [10]. Ref. [9] found some accounting variables that significantly differed between firms with large shareholdings in NPS and those without. These variables are profitability (ROA), debt ratio (LEV), price-earnings ratio (PER), net income growth rate (NIR), dividend payout ratio (DIVOUT), and market. Therefore, in this study, PSM is performed using these variables. The concrete model is as follows.

First logistics model for propensity score matching:

increase in dividends due to the external pressure of NPS may reduce the investment motivation of companies, leading to a decrease in investment, which may adversely affect the firm value. While the opposite effect of NPS dividend pressure is expected, we will examine how

In this study, Tobin's Q is used as a measure of firm value. Tobin's Q is the market value of assets divided by the replacement cost. The higher the value, the higher the firm value because the market value of the enterprise is evaluated higher than the replacement cost in the market. The market value of assets is measured as the sum of the market value of equity and the book

Dividend pressure of NPS is measured by the following method. First, because NPS does not directly disclose a list of low dividend companies, it uses the level of cash dividend and whether or not NPS owns more than 5% stake because it sees a baseline stake, typically 5%, that can control management and influence the company. In addition, in Korea, it is obligatory to report related contents when holding more than 5% of the shares of a listed corporation, so information on the proportion of NPS's investment can be obtained. We consider the dummy variable (NPF) which distinguishes the cases where NPS has more than 5% stake. The dividend level is measured by cash dividend to total asset ratio (DIVTA), cash dividend to net income ratio (DIVOUT), and cash dividend to paid-in capital ratio (DIVRATE). In addition, if a company with a high NPS stake increases its cash dividend level in the following period, it is highly likely that NPS has given dividend pressure to the company. Therefore, we also consider the change variable of the dividend levels (Δ DIVTA, Δ DIVOUT, Δ DIVRATE). In this chapter, we use the intersection term between the dividend level variables (DIVTA, DIVOUT, DIVRATE, Δ DIVTA, Δ DIVOUT, Δ DIVRATE, and the dummy variable (NPS)) to measure the

In this study, the dividend pressure of NPS is measured using whether NPS invests in equity of 5% or more. Therefore, the nature of a company that NPS has invested in a large amount can affect the outcome. We use propensity score matching (PSM) to control self-selection bias. PSM has the advantage of reducing the impact of certain variables and providing more accurate statistics when analyzing groups [10]. Ref. [9] found some accounting variables that significantly differed between firms with large shareholdings in NPS and those without. These variables are profitability (ROA), debt ratio (LEV), price-earnings ratio (PER), net income growth rate (NIR), dividend payout ratio (DIVOUT), and market. Therefore, in this study, PSM is

performed using these variables. The concrete model is as follows.

value of debt, and the replacement cost is calculated as the book value of the asset.

the dividend pressure of NPS affects the firm.

3. Research design

3.1. Definition of variables

58 Firm Value - Theory and Empirical Evidence

probability of NPS dividend pressure.

3.2. Research model

$$\text{NPF}\_{i,t} = \beta\_0 + \beta\_1 \text{LEV}\_{i,t} + \beta\_2 \text{ROA}\_{i,t} + \beta\_3 \text{NIR}\_{i,t} + \beta\_4 \text{Market}\_{i,t} + \beta\_5 \text{PER}\_{i,t} + \beta\_6 \text{DIVOLT}\_{i,t} + \epsilon\_{i,t} \tag{1}$$

NPF is an indicator variable equal to 1 if NPS owns more than 5% of the company's stake and 0 otherwise. LEV is debt ratio and ROA is net income divided by average total assets. NIR is growth rate of net income. Market is an indicator variable equal to 1 if the company is listed on the KOSPI and 0 otherwise. PER is price-earnings ratio and DIVOUT is cash dividend divided by net income.

In order to analyze the effect of NPS's dividend pressure on firm value, we analyze the following equation, Eq. (2), using the sample matched in Eq. (1).

Second OLS regression model for main analysis:

$$Q\_{i,t} = \beta\_0 + \beta\_1 \text{DIV}\_{i,t} + \beta\_2 \text{DIV}\_{i,t} \* \text{NPF}\_{i,t-1} + \beta\_3 \text{NPF}\_{i,t-1} + \sum \beta\_{ij} \text{Controls}\_{i,t-1} \tag{2}$$

Q is Tobin's Q, which measures firm value. DIV is the dividend level of the company, measured by DIVTA, DIVOUT, DIVRATE, ΔDIVTA, ΔDIVOUT, and ΔDIVRATE. The main interest variable of this study is the cross-section of Eq. (2), which is the variable indicating the dividend pressure of NPS. If β<sup>2</sup> has a statistically significant positive (+) value, then NPS's dividend pressure will increase the firm value. On the other hand, if β<sup>2</sup> has a statistically significant negative (�) value, the dividend pressure of NPS would decrease the firm value.

We use variables that are known to affect investment in previous studies as control variables [11, 12]. Since the firm size and profitability affect investment, SIZE, which takes natural logarithm of total assets, and ROA, which shows profitability, are used as control variables. In the case of firms with high debt ratios, investment activity decreases due to the high bankruptcy risk [13]. We use LEV, which represents the debt ratio and Z, which measures the bankruptcy risk by Altman's Z-score. We use CFO, which divides cash flow from operating activities into total assets, and TANG, which divides the tangible assets into total assets, LOSS, which means net loss. We use MB, which is the market-to-book value ratio of equity, OPCYLCE, which takes natural logarithm of operating cycle, and volatility (STD\_CFO, STD\_SALES) as control variables.

#### 3.3. Sample selection

The sample in this study is all companies listed on the Korean Stock Exchange from 2011 to 2016 which meet the following criteria.


Of the original 11,350 firm-year observations from 2011 to 2016, we lose 616 for financial companies and 182 for companies with negative net assets. 1039 observations are companies that have more than 5% shares of NPS and the control group matched using PSM has 2978 observations. In order to exclude the effect of extreme values, 1% of the upper and lower values are winsorized. The final company-year observations, including all financial information required for the analysis, are identified as 2194.
