# The Effects of Operational Structure Change on Performance after Seasoned Equity Offerings

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Hypothesis

**Hypothesis**

**1.**

**Hypothesis**

**2.**

## 3. Materials and Methods

#### 3.1. Research Methodology

#### 3.1.1. Empirical Model of Operating Performance

- $\Delta RO{A}_{i,st}$ = the change rate of operating performance for firm i, from year s to year t,
- (1) $\Delta RO{A}_{i,01}=\frac{\left(RO{A}_{i,1}-RO{A}_{i,0}\right)}{RO{A}_{i,0}}$, (2) $\Delta RO{A}_{i,02}=\frac{\left(RO{A}_{i,2}-RO{A}_{i,0}\right)}{RO{A}_{i,0}}$, (3) $\Delta RO{A}_{i,03}=\frac{\left(RO{A}_{i,3}-RO{A}_{i,0}\right)}{RO{A}_{i,0}}$,
- $\Delta OPC{H}_{i,st}$ = the change rate of operational structure firm i, from year s to year t,
- (1) $\Delta N\_Se{g}_{i,st}$ = the change rate of the number of operating segments of firm i, from year s to year t,
- (2) $\Delta BH{I}_{i,st}$ = the change rate of the Berry–Herfindahl index by operating segment sales of firm i, from year s to year t = $\left[\left\{1-{\sum}_{j=1}^{j}{\left(\frac{Sale{s}_{i,t,j}}{Sale{s}_{i,t}}\right)}^{2}\right\}-\left\{1-{\sum}_{j=1}^{j}{\left(\frac{Sale{s}_{i,s,j}}{Sale{s}_{i,s}}\right)}^{2}\right\}\right]$, $Sale{s}_{i,t,j}$ = segment j sales for firm i in year t, $Sale{s}_{i,t}$ = total sales for firm i in year t;
- (3) $\Delta Cap\_Ex{p}_{i,st}$ = the change rate of capital expenditure for firm i, from year s to year t = $\frac{(Cap\_Ex{p}_{i,t}-Cap\_Ex{p}_{i,s})}{SEOs\_Amount{s}_{i,s}}$, $(Cap\_Ex{p}_{i,t}-Cap\_Ex{p}_{i,s})$ = accumulation the net investment in plant and equipment from year s to t, $SEO{s}_{Amount{s}_{i,s}}$ = total amount of SEOs of firm i in year s;
- $TA{C}_{i,s}$ = total accrual of firm i in year s = $\frac{(Netincom{e}_{i,s}-Operatingcashflo{w}_{i,s})}{Averagetotalasse{t}_{i,s}}$;
- $MT{B}_{i,s}$ = market-to-book ratio of firm i in year s = $\frac{MarketvalueofEquit{y}_{i,s}}{BookvalueofEquit{y}_{i,s}}$;
- $\Delta Sale{s}_{i,s}$ = sales growth rate of firm i in year s = $\frac{(Sale{s}_{i,s}-Sale{s}_{i,s-1})}{Sale{s}_{i,s-1}}$;
- $\mathsf{\Sigma}Dummies$ = year dummy, industry dummy.

_{i}

_{,s}, MTB

_{i}

_{,s}, and $\Delta $Sales

_{i}

_{,s}as control variables affecting operating performance.

_{i}

_{,st}). The second is the change in the Berry–Herfindahl index based on sales ($\Delta $BHI

_{i}

_{,st}). This is the sum of sales that are first divided according to each operating segment by total sales and squared. The Berry–Herfindahl index is a typical method used to measure the level of corporate diversification. If there is one operating segment, BHI

_{i}

_{,st}has the value of 1, and a higher level of corporate diversification results in convergence to 0. For convenience of interpretation, the Berry–Herfindahl index is deducted from 1 so that higher corporate diversification indicates the value closer to 1. Plant and equipment investment ($\Delta $Cap_Exp

_{i}

_{,st}) is calculated by accumulating the net investment in plant and equipment (=increase of plant asset − decrease of plant asset) from year s to t, and dividing it by the amount of SEOs in year s.

_{i,st}, $\Delta $BHI

_{i,st}and $\Delta $Cap_Exp

_{i,st}are not as predicted.

_{i,s}is total accrual in year s, and this amount has lower durability than cash flows; thus, profits in the next term are lower if the performance of the current term is adjusted according to the accounting choices made by the manager [4]. Therefore, the bigger the amount of the total accrual, the lower the operating performance is expected to be in the next term. MTB

_{i,s}is the measure of investment opportunities or growth, and, thus, the higher it is, the higher the operating performance is expected to be in the next term. $\Delta $Sales

_{i,s}is the sales growth rate, and the higher the growth rate in the current term is, the higher the future operating performance is expected to be. ΣDummies represents year and industry dummies.

#### 3.1.2. Empirical Model of Abnormal Return

- $BAH{R}_{i,st}$ = buy-and-hold returns for firm i, from year s to year t;
- $Num\_Issu{e}_{i,s}$ = the number of outstanding shares at SEO for firm i in year s;
- $\Delta Deb{t}_{i,st}$ = the change rate of debt ratio for firm i, from year s to year t = $\frac{\left(\frac{Total\text{}deb{t}_{i,t}}{Total\text{}asse{t}_{i,t}}-\frac{Total\text{}deb{t}_{i,s}}{Total\text{}asse{t}_{i,s}}\right)}{\frac{Total\text{}deb{t}_{i,s}}{Total\text{}asse{t}_{i,s}}}$;
- $Forfeitur{e}_{i,s}$ = old shareholder forfeiture rate at seasoned equity offering for firm i in year s;
- $Discoun{t}_{i,s}$ = market discount rate at seasoned equity offering for firm i in year s;
- $\mathsf{\Sigma}Controls$ = $\Delta RO{A}_{i,st},\text{}TA{C}_{i,s},\text{}MT{B}_{i,s},\Delta Sale{s}_{i,s}$ in Equation (1);

_{i,st}, $\Delta $BHI

_{i,st}and $\Delta $Cap_Exp

_{i,st}are not as predicted.

_{i,s}) is a variable to test the price pressure hypothesis proposed by Scholes [10], and the increase of stock supply leads to the fall of that stock price in the long run; thus, the bigger the rights issue size is, the more likely there is to be a fall of stock prices [1,24].

_{i,st}) is based on the substitution hypothesis proposed by Galai and Masulis [13]. If the debt ratio decreases owing to SEOs, existing creditors receive higher interest at lower risks. Therefore, the decrease of debt ratio according to SEOs results in the transfer of the wealth of existing shareholders to creditors. Thus, a higher debt ratio leads to lower stock returns.

_{i,s}) is to test the old shareholder interest hypothesis. According to Yoon [6], a higher old shareholder forfeiture rate leads to greater loss of old shareholders due to SEOs. Therefore, to make up for the loss, there must be higher net present value of new investments. Issuing an SEO means that the net present value of investment might bring profits even after making up for the loss of shareholders, and, thus, there is a positive correlation between the old shareholder forfeiture rate and the excess returns.

_{i,s}) is based on Jung [8] and Shin [9]. SEOs by the shareholder allotment method do not affect stock prices in the US, but they are perceived as a negative signal in Korea because of the market price discount rate, which is one of the institutional characteristics of SEOs in Korea. Therefore, there is evidence that if the stock split effect accompanied by excessive market price discount rate is controlled in Korea, SEOs might result in a fall of stock prices, as in the US.

#### 3.2. Sample Selection

#### 3.3. Descriptive Statistics

_{i,s}) is 30 billion KRW on average, and 680 billion KRW at maximum. $\Delta $ROA

_{i,}

_{01}is (−)1%, $\Delta $ROA

_{i,}

_{02}is 2%, and $\Delta $ROA

_{i,}

_{03}is 1%. This result is different from previous studies claiming that long-term operating performance falls after SEOs. BAHR

_{i,}

_{1}is (−)3%, BAHR

_{i,}

_{12}is 11%, and BAHR

_{i,}

_{13}is 7%, showing no long-term under-performance.

_{i,}

_{01}is 38%, $\Delta $N_Seg

_{i,}

_{02}is 52%, and $\Delta $N_Seg

_{i,}

_{03}is 31%. $\Delta $BHI

_{i,}

_{01}is (−)4%, $\Delta $BHI

_{i,}

_{02}is 2%, and $\Delta $BHI

_{i,}

_{03}is 7%, showing an increase. $\Delta $Cap_Exp

_{i,}

_{01}increased to 95%, $\Delta $Cap_Exp

_{i,}

_{02}to 140%, and $\Delta $Cap_Exp

_{i,}

_{03}to as high as 192%.

_{i,st}) did not decrease, which suggests that SEOs and debt issuance are carried out at the same time. The market-to-book value (MTB

_{i,s}) is 1.59 on average. The sales growth rate of SEO issuers ($\Delta $Sales

_{i,s}) is on average 21%, and the maximum is 1453%.

## 4. Results of Multi-Regression Analysis

_{i,}

_{01}) and operational structure change one year after SEOs. Operational structure change is measured by the level of corporate diversification and plant and equipment investment. Model (1) used $\Delta $Seg

_{i,}

_{01}as the first corporate diversification variable. The coefficient of $\Delta $Seg

_{i,}

_{01}is statistically significant and negative at (−)0.042. This implies that the operating performance immediately after SEOs is lower because of the investment that occurred in the process of building a new operational structure through corporate diversification. The coefficients of $\Delta $BHI

_{i,}

_{01}, which is the second measurement variable of corporate diversification, and of $\Delta $Cap_Exp

_{i,}

_{01}, which is the measure of plant and equipment investment coefficient, turn out not to be significant. Model (4), which considers all values of operational structure change, shows that the coefficient of ΔSeg

_{i,}

_{01}is statistically significant and negative, thereby implying that operational structure change due to the increase of operating segments has a negative correlation with the operating performance of the current term.

_{i,}

_{0}is statistically significant and negative, whereas the coefficients of MTB

_{i,}

_{0}and ΔSales

_{i,}

_{0}are statistically significant and positive.

_{i,}

_{02}) and operational structure change two years after SEOs. Contrary to the results in Table 2, the coefficient of ΔSeg

_{i,}

_{02}in Model (1) is statistically significant and positive at 0.027. Model (4), which considers all values of operational structure change, shows that only the coefficient of ΔSeg

_{i,}

_{02}is statistically significant and positive. This implies that operational structure change through SEOs is positively correlated with long-term operating performance, especially in terms of corporate diversification.

_{i,}

_{03}in Model (2) is statistically significant and positive at 0.043. Model (4), which considers all values of operational structure change, also shows that the coefficient of ΔBHI

_{i,}

_{03}is statistically significant and positive.

_{i,}

_{01}) and operational structure change one year after SEOs. Models (1), (2), and (3), which individually use △Seg

_{i,}

_{01}, △BHI

_{i,}

_{01}, ΔCap_Exp

_{i,}

_{01}, all show values that are not significant. However, Model (4), which considers all values of operational structure change, shows that the coefficient of △BHI

_{i,}

_{01}is statistically significant and negative at (−)0.164. This implies that there is under-performance in stock returns in terms of operational structure change.

_{i,}

_{02}) and operational structure change two years after SEOs. Similar to Table 5, the correlation between operational structure change and stock returns is not significant in Models (1), (2), and (3). In particular, Model (4), which considers all values of operational structure change, shows no significant correlation between operational structure change and stock returns.

_{i,}

_{03}) and operational structure change three years after SEOs. △BHI

_{i,}

_{03}in Model (2) has a statistically significant and positive correlation with stock returns, and Cap_Exp

_{i,}

_{03}in Model (3) has a statistically significant and positive correlation with stock returns. Therefore, the correlation between operational structure change and stock returns is not formed when SEOs are issued, but appears afterward.

## 5. Discussion

_{i,}

_{0}is the major cause of adverse effects on operating performance after SEOs.

_{i,}

_{01}) in the model of stock returns (BAHR

_{i,st}) shows a statistically significant and negative correlation with stock returns after one year, while operational structure change shows no significant correlation with stock returns after two years. However, the coefficients of △BHI

_{i,}

_{03}and △Cap_Exp

_{i,}

_{03}are statistically significant and positive in the analysis after three years. This indicates that some time must pass before operational structure change increases operating performance. Furthermore, disclosed accounting information is actually information from the past in principle, and, thus, economic benefits of the current term are reflected in stock prices but might not be reflected in the financial statements. In other words, time must pass before plant and equipment investment after SEOs leads to operating performance. Thus, in the results, there is no significant stock price response at first, and the operating performance in the next term is perceived as a favorable factor after three years. In summary, there is a time lag in the stock market regarding operational structure change.

_{i,}

_{0}) is negatively correlated with stock returns, but this is not significant. Therefore, the price pressure hypothesis proposed by Scholes [11] is not supported. The increased rate of debt (ΔDebt

_{i,st}) had a negative or positive correlation, depending on the model, but none is significant. Therefore, the substitution hypothesis proposed by Galai and Masulis [13] is not supported. The old shareholder forfeiture rate (Forfeiture

_{i,}

_{0}) showed a statistically significant and positive correlation with the stock returns in all models for one year after SEOs, thereby supporting the old shareholder interest hypothesis. The market price discount rate (Discount

_{i,}

_{0}) showed a statistically significant and positive correlation with stock returns only two years after SEOs.

## 6. Conclusions

## Author Contributions

## Conflicts of Interest

## Appendix A

Variables | Definition |

$\Delta RO{A}_{i,st}$ | the change rate of operating performance for firm i, from year s to year t, (1) $\Delta RO{A}_{i,01}=\frac{\left(RO{A}_{i,1}-RO{A}_{i,0}\right)}{RO{A}_{i,0}}$, (2) $\Delta RO{A}_{i,02}=\frac{\left(RO{A}_{i,2}-RO{A}_{i,0}\right)}{RO{A}_{i,0}}$, (3) $\Delta RO{A}_{i,03}=\frac{\left(RO{A}_{i,3}-RO{A}_{i,0}\right)}{RO{A}_{i,0}}$ . |

$\Delta OPC{H}_{i,st}$ | the change rate of operational structure firm i, from year s to year t, (1) $\Delta N\_Se{g}_{i,st}$ = the change rate of the number of operating segments of firm i, from year s to year t, (2) $\Delta BH{I}_{i,st}$ = the change rate of the Berry–Herfindahl index by operating segment sales of firm i, from year s to year t = $\left[\left\{1-{\sum}_{j=1}^{j}{\left(\frac{Sale{s}_{i,t,j}}{Sale{s}_{i,t}}\right)}^{2}\right\}-\left\{1-{\sum}_{j=1}^{j}{\left(\frac{Sale{s}_{i,s,j}}{Sale{s}_{i,s}}\right)}^{2}\right\}\right]$ $Sale{s}_{i,t,j}$ = segment j sales for firm i in year t, $Sale{s}_{i,t}$ = total sales for firm i in year t, (3) $\Delta Cap\_Ex{p}_{i,st}$ = the change rate of capital expenditure for firm i, from year s to year t = $\frac{(Cap\_Ex{p}_{i,t}-Cap\_Ex{p}_{i,s})}{SEOs\_Amount{s}_{i,s}}$, $(Cap\_Ex{p}_{i,t}-Cap\_Ex{p}_{i,s})$ = accumulation of the net investment in plant and equipment from year s to t = (increase of plant asset − decrease of plant asset) from year s to t, $SEOs\_Amount{s}_{i,s}$ = total amount of SEOs of firm i in year s. |

$TA{C}_{i,s}$ | total accrual of firm i in year s = $\frac{(Netincom{e}_{i,s}-Operatingcashflo{w}_{i,s})}{Averagetotalasse{t}_{i,s}}$ |

$MT{B}_{i,t}$ | market-to-book ratio of firm i in year s = $\frac{MarketvalueofEquit{y}_{i,s}}{BookvalueofEquit{y}_{i,s}}$ |

$\Delta Sale{s}_{i,s}$ | sales growth rate of firm i in year s = $\frac{(Sale{s}_{i,s}-Sale{s}_{i,s-1})}{Sale{s}_{i,s-1}}$ |

$BAH{R}_{i,st}$ | buy-and-hold returns for firm i, from year s to year t |

$Num\_Issu{e}_{i,s}$ | the number of outstanding shares at SEO for firm i in year s |

$\Delta Deb{t}_{i,st}$ | the change rate of debt ratio for firm i, from year s to year t = $\frac{\left(\frac{Totaldeb{t}_{i,t}}{Totalasse{t}_{i,t}}-\frac{Totaldeb{t}_{i,s}}{Totalasse{t}_{i,s}}\right)}{\frac{Totaldeb{t}_{i,s}}{Totalasse{t}_{i,s}}}$ |

$Forfeitur{e}_{i,s}$ | old shareholder forfeiture rate at seasoned equity offering for firm i in year s |

$Discoun{t}_{i,s}$ | market discount rate at seasoned equity offering for firm i in year s |

$\mathsf{\Sigma}Controls$ | $\Delta RO{A}_{i,st},\text{}TA{C}_{i,st},MT{B}_{i,st},\Delta Sale{s}_{i,st}$ |

$\mathsf{\Sigma}Dummies$ | year dummy, industry dummy |

## Appendix B. The Implication of the Berry–Herfindahl Index (BHI)

^{2}+ 0.3820

^{2}+ 0.2427

^{2}+ 0.2332

^{2}+ 0.005

^{2}) = 0.7191, which means the higher the score of BHI is the higher the level of diversification is.

The Segments of Samsung Electronics in 2009 | Sales Ratio |
---|---|

Television, Monitor, Refrigerator, Washing Machine, Air Conditioner, Medical Devices, etc. | 14.71% |

HandHeld Player, Network System, Computer, etc. | 38.20% |

DRAM (Dynamic Random-Access Memory), NAND (Not AND) Flash, M-AP (Mobile Application Processor), TFT-LCD (Thin Film Transistor Liquid Crystal Display), OLED (Organic Light Emitting Diodes), etc. | 24.27% |

Head units, Infotainment, Telematics, Speaker, etc. | 23.32% |

Others | 0.50% |

Total | 100.00% |

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Variables | Mean | Std. Dev. | Minimum | 1st Quartile | Median | 3rd Quartile | Maximum |
---|---|---|---|---|---|---|---|

SEOs_Amount_{i,s} | 30.39 | 88.96 | 1.10 | 5.18 | 9.66 | 19.00 | 680.64 |

$\Delta $ROA_{i,01} | −0.01 | 0.25 | −2.65 | −0.04 | −0.01 | 0.03 | 1.25 |

$\Delta $ROA_{i,02} | 0.02 | 0.23 | −1.74 | −0.04 | 0.00 | 0.06 | 1.23 |

$\Delta $ROA_{i,03} | 0.01 | 0.23 | −1.64 | −0.05 | 0.00 | 0.07 | 1.33 |

BAHR_{i,1} | −0.03 | 0.54 | −0.87 | −0.40 | −0.12 | 0.19 | 2.38 |

BAHR_{i,12} | 0.11 | 0.84 | −0.93 | −0.50 | −0.11 | 0.41 | 3.12 |

BAHR_{i,13} | 0.08 | 0.99 | −0.94 | −0.58 | −0.19 | 0.40 | 4.64 |

$\Delta $N_Seg_{i,01} | 0.38 | 1.00 | −0.60 | 0.00 | 0.00 | 0.25 | 4.00 |

$\Delta $N_Seg_{i,02} | 0.52 | 1.53 | −0.67 | 0.00 | 0.00 | 0.33 | 9.00 |

$\Delta $N_Seg_{i,03} | 0.31 | 0.89 | −0.67 | 0.00 | 0.00 | 0.33 | 4.00 |

$\Delta $BHI_{i,01} | −0.04 | 0.41 | −1.00 | −0.20 | −0.03 | 0.08 | 1.90 |

$\Delta $BHI_{i,02} | 0.02 | 0.49 | −0.79 | −0.23 | −0.01 | 0.16 | 2.27 |

$\Delta $BHI_{i,03} | 0.07 | 0.56 | −0.70 | −0.28 | 0.00 | 0.26 | 2.15 |

$\Delta $Cap_Exp_{i,01} | 0.95 | 1.73 | −2.32 | 0.10 | 0.40 | 1.21 | 9.66 |

$\Delta $Cap_Exp_{i,02} | 1.40 | 2.66 | −3.77 | 0.15 | 0.63 | 1.66 | 14.43 |

$\Delta $Cap_Exp_{i,03} | 1.92 | 3.53 | −3.72 | 0.16 | 0.80 | 2.51 | 19.52 |

TAC_{i,s} | −0.04 | 0.14 | −0.72 | −0.10 | −0.02 | 0.05 | 0.28 |

Num_Issue_{i,s} | 14.85 | 1.25 | 11.56 | 14.17 | 14.89 | 15.70 | 18.68 |

$\Delta $Debt_{i,01} | 0.13 | 0.62 | −0.64 | −0.08 | 0.02 | 0.15 | 4.67 |

$\Delta $Debt_{i,02} | 0.13 | 0.55 | −0.80 | −0.10 | 0.03 | 0.19 | 3.76 |

$\Delta $Debt_{i,03} | 0.13 | 0.59 | −0.84 | −0.15 | 0.04 | 0.24 | 3.35 |

Forfeiture_{i,s} | 0.01 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | 0.31 |

Discount_{i,s} | 17.68 | 13.70 | 0.00 | 0.00 | 25.00 | 30.00 | 50.00 |

MTB_{i,s} | 1.59 | 1.86 | 0.09 | 0.64 | 1.11 | 1.82 | 14.55 |

$\Delta $Sales_{i,s} | 0.21 | 0.95 | −0.83 | −0.05 | 0.10 | 0.28 | 14.53 |

ΔROA_{i,st} (s = 0, t = 1) = α_{0} + β_{1}·ΔOPCH_{i,st} + β_{2}·TAC_{i,s} + β_{3}·MTB_{i,s} + β_{4}·ΔSales_{i,s} + Σdummies + ε | |||||||||
---|---|---|---|---|---|---|---|---|---|

Variables | Model (1) | Model (2) | Model (3) | Model (4) | |||||

ΔOPCH_{i,01} | ΔSeg_{i,01} | −0.042 ** | (−2.53) | - | - | - | - | −0.048 ** | (−2.51) |

ΔBHI_{i,01} | - | - | 0.024 | (0.64) | - | - | −0.028 | (−0.65) | |

ΔCap_Exp_{i,01} | - | - | - | - | 0.004 | (0.45) | 0.002 | (0.27) | |

TAC_{i,0} | −0.600 *** | (−5.81) | −0.603 *** | (−5.76) | −0.612 *** | (−5.85) | −0.607 *** | (−5.83) | |

MTB_{i,0} | 0.018 ** | (2.32) | 0.016 ** | (2.07) | 0.017 ** | (2.15) | 0.018 ** | (2.39) | |

ΔSales_{i,0} | 0.032 ** | (2.06) | 0.032 ** | (2.02) | 0.031 * | (1.96) | 0.031 ** | (1.98) | |

F-value | 3.61 *** | 3.31 *** | 3.30 *** | 3.36 *** | |||||

Adjusted R^{2} | 0.198 | 0.180 | 0.179 | 0.194 | |||||

# of obs. | 286 | 286 | 286 | 286 |

ΔROA_{i,st} (s = 0, t = 2) = α_{0} + β_{1}·ΔOPCH_{i,st} + β_{2}·TAC_{i,s} + β_{3}·MTB_{i,s} + β_{4}·ΔSales_{i,s} + ΣDummies + ε | |||||||||
---|---|---|---|---|---|---|---|---|---|

Variables | Model (1) | Model (2) | Model (3) | Model (4) | |||||

ΔOPCH_{i,02} | ΔSeg_{i,02} | 0.027 ** | (2.51) | - | - | - | - | 0.033 *** | (2.89) |

ΔBHI_{i,02} | - | - | 0.017 | (0.56) | - | - | 0.048 | (1.52) | |

ΔCap_Exp_{i,02} | - | - | - | - | −0.004 | (−0.87) | −0.005 | (−0.94) | |

TAC_{i,0} | −0.707 *** | (−7.50) | −0.692 *** | (−7.24) | −0.692 *** | (−7.27) | −0.697 *** | (−7.39) | |

MTB_{i,0} | 0.005 | (0.67) | 0.005 | (0.66) | 0.004 | (0.59) | 0.004 | (0.53) | |

ΔSales_{i,0} | 0.026 * | (1.80) | 0.029 ** | (2.00) | 0.028 ** | (1.97) | 0.027 * | (1.91) | |

F-value | 3.79 *** | 3.49 *** | 3.51 *** | 3.65 *** | |||||

Adjusted R^{2} | 0.209 | 0.191 | 0.192 | 0.213 | |||||

# of obs. | 286 | 286 | 286 | 286 |

ΔROA_{i,st} (s = 0, t = 3) = α_{0} + β_{1}·ΔOPCH_{i,st} + β_{2}·TAC_{i,s} + β_{3}·MTB_{i,s} + β_{4}·ΔSales_{i,s} + ΣDummies + ε | |||||||||
---|---|---|---|---|---|---|---|---|---|

Variables | Model (1) | Model (2) | Model (3) | Model (4) | |||||

ΔOPCH_{i,03} | ΔSeg_{i,03} | 0.001 | (0.04) | - | - | - | - | 0.016 | (0.78) |

ΔBHI_{i,03} | - | - | 0.043 * | (1.73) | - | - | 0.052 * | (1.90) | |

ΔCap_Exp_{i,03} | - | - | - | - | 0.000 | (−0.09) | −0.001 | (−0.18) | |

TAC_{i,0} | −0.495 *** | (−5.04) | −0.488 *** | (−5.01) | −0.494 *** | (−5.04) | −0.488 *** | (−4.98) | |

MTB_{i,0} | 0.012 | (1.63) | 0.011 | (1.56) | 0.012 | (1.62) | 0.010 | (1.45) | |

ΔSales_{i,0} | 0.015 | (1.02) | 0.015 | (1.04) | 0.015 | (1.02) | 0.015 | (1.02) | |

F-value | 1.92 *** | 2.05 *** | 1.92 *** | 1.92 *** | |||||

Adjusted R^{2} | 0.080 | 0.091 | 0.080 | 0.086 | |||||

# of obs. | 286 | 286 | 286 | 286 |

BAHR_{i,st} (s = 0, t = 1) = α_{0} + β_{1}·ΔOPCH_{i,st} + β_{2}·Num_Issue_{i,s} + β_{3}·ΔDebt_{i,st} + β_{4}·Forfeiture_{i,s} + β_{5}·Discount_{i,s}+ ΣControls + ΣDummies + ε | |||||||||
---|---|---|---|---|---|---|---|---|---|

Variables | Model (1) | Model (2) | Model (3) | Model (4) | |||||

ΔOPCH_{i,01} | ΔSeg_{i,01} | −0.013 | (−0.35) | - | - | - | - | −0.044 | (−1.07) |

ΔBHI_{i,01} | - | - | −0.116 | (−1.45) | - | - | −0.164 * | (−1.80) | |

ΔCap_Exp_{i,01} | - | - | - | - | 0.029 | (1.55) | 0.027 | (1.48) | |

Num_Issue_{i,1} | −0.020 | (−0.78) | −0.023 | (−0.91) | −0.012 | (−0.46) | −0.019 | (−0.71) | |

ΔDebt_{i,01} | 0.072 | (1.30) | 0.073 | (1.33) | 0.062 | (1.13) | 0.067 | (1.21) | |

Forfeiture_{i,0} | 1.749 ** | (2.20) | 1.600 ** | (2.02) | 1.795 ** | (2.28) | 1.725 ** | (2.18) | |

Discount_{i,0} | 0.003 | (0.93) | 0.003 | (1.09) | 0.003 | (0.85) | 0.003 | (1.02) | |

F-value | 3.02 *** | 3.10 *** | 3.12 *** | 3.04 *** | |||||

Adjusted R^{2} | 0.185 | 0.191 | 0.192 | 0.196 | |||||

# of obs. | 286 | 286 | 286 | 286 |

BAHR_{i,st} (s = 0, t = 2) = α_{0} + β_{1}·ΔOPCH_{i,st} + β_{2}·Num_Issue_{i,s} + β_{3}·ΔDebt_{i,st} + β_{4}·Forfeiture_{i,s} + β_{5}·Discount_{i,s}+ ΣControls + ΣDummies + ε | |||||||||
---|---|---|---|---|---|---|---|---|---|

Variables | Model (1) | Model (2) | Model (3) | Model (4) | |||||

ΔOPCH_{i,02} | ΔSeg_{i,02} | −0.052 | (−1.32) | - | - | - | - | −0.051 | (−1.21) |

ΔBHI_{i,02} | - | - | 0.063 | (0.58) | - | - | 0.014 | (0.12) | |

ΔCap_Exp_{i,02} | - | - | - | - | 0.015 | (0.80) | 0.015 | (0.81) | |

Num_Issue_{i,0} | −0.038 | (−0.98) | −0.036 | (−0.91) | −0.032 | (−0.80) | −0.032 | (−0.80) | |

ΔDebt_{i,02} | −0.072 | (−0.82) | −0.078 | (−0.88) | −0.085 | (−0.96) | −0.078 | (−0.88) | |

Forfeiture_{i,0} | −0.257 | (−0.21) | −0.317 | (−0.25) | −0.280 | (−0.22) | −0.199 | (−0.16) | |

Discount_{i,0} | 0.010 ** | (2.23) | 0.011 ** | (2.25) | 0.010 ** | (2.19) | 0.010 ** | (2.12) | |

F-value | 3.42 *** | 3.36 *** | 3.37 *** | 3.22 *** | |||||

Adjusted R^{2} | 0.214 | 0.210 | 0.210 | 0.210 | |||||

# of obs. | 286 | 286 | 286 | 286 |

BAHR_{i,st} (s = 0, t = 3) = α_{0} + β_{1}·ΔOPCH_{i,st} + β_{2}·Num_Issue_{i,s} + β_{3}·ΔDebt_{i,st} + β_{4}·Forfeiture_{i,s} + β_{5}·Discount_{i,s}+ ΣControls + ΣDummies + ε | |||||||||
---|---|---|---|---|---|---|---|---|---|

Variables | Model (1) | Model (2) | Model (3) | Model (4) | |||||

ΔOPCH_{i,03} | ΔSeg_{i,03} | −0.014 | (−0.18) | - | - | - | - | 0.054 | (0.65) |

ΔBHI_{i,03} | - | - | 0.213 ** | (2.01) | - | - | 0.227 ** | (2.01) | |

ΔCap_Exp_{i,03} | - | - | - | - | 0.062 *** | (3.76) | 0.060 *** | (3.68) | |

Num_Issue_{i,0} | −0.059 | (−1.24) | −0.056 | (−1.19) | −0.021 | (−0.45) | −0.018 | (−0.38) | |

ΔDebt_{i,03} | −0.119 | (−1.17) | −0.103 | (−1.01) | −0.151 | (−1.52) | −0.132 | (−1.32) | |

Forfeiture_{i,0} | −2.136 | (−1.45) | −2.022 | (−1.39) | −1.594 | (−1.11) | −1.456 | (−1.01) | |

Discount_{i,0} | 0.006 | (1.10) | 0.006 | (1.07) | 0.004 | (0.70) | 0.004 | (0.65) | |

F-value | 2.7 *** | 2.86 *** | 3.29 *** | 3.24 *** | |||||

Adjusted R^{2} | 0.160 | 0.173 | 0.204 | 0.211 | |||||

# of obs. | 286 | 286 | 286 | 286 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ahn, C.; Kim, M.-O.; Jung, H.-R.
The Effects of Operational Structure Change on Performance after Seasoned Equity Offerings. *Sustainability* **2018**, *10*, 88.
https://doi.org/10.3390/su10010088

**AMA Style**

Ahn C, Kim M-O, Jung H-R.
The Effects of Operational Structure Change on Performance after Seasoned Equity Offerings. *Sustainability*. 2018; 10(1):88.
https://doi.org/10.3390/su10010088

**Chicago/Turabian Style**

Ahn, Chihyoun, Mi-Ok Kim, and Hyung-Rok Jung.
2018. "The Effects of Operational Structure Change on Performance after Seasoned Equity Offerings" *Sustainability* 10, no. 1: 88.
https://doi.org/10.3390/su10010088