Post-IPO Performance Analysis: Governance, Compensation, and ESG
Vitor Fernandes M. Ferreira
Introduction
This study explores how newly public firms perform in the critical years following their initial public offering (IPO), with a particular focus on the role of corporate governance, executive leadership, and sustainability orientation.
While the IPO marks a pivotal shift—granting access to capital and market visibility—it also introduces increased accountability and strategic complexity. Drawing on upper echelons theory, we examine how characteristics of the top management team and board—such as board size, independence, skill composition, meeting frequency, and attendance—shape post-IPO outcomes.
Furthermore, we incorporate insights from agency theory, emphasizing the mechanisms firms use to align managerial actions with long-term value creation. This includes linking executive compensation to performance metrics like total shareholder return (TSR) and sustainability goals.
By integrating ESG scores, compensation structure, and governance practices, this study asks:
How do leadership composition, governance design, and sustainability commitment influence firm performance in the years following an IPO?
Hypotheses Development
Going public represents a pivotal moment in a firm’s lifecycle, marking a transition from private governance to public scrutiny. While an IPO provides firms with access to capital markets, it also introduces new accountability structures, managerial pressures, and strategic complexities. Understanding the mechanisms that influence firm performance in the post-IPO period is therefore critical.
Drawing on agency theory and upper echelons theory, this study explores how corporate governance characteristics, executive compensation structures, and ESG commitments shape post-IPO outcomes.
1 Board Independence and Firm Performance
Agency theory suggests that independent boards are more likely to monitor management effectively, thereby mitigating conflicts of interest and aligning managerial decisions with shareholder interests (Fama & Jensen, 1998). Following an IPO, the firm’s ownership base often becomes more diffuse, amplifying agency concerns. In this context, independent board members may play a particularly vital role in sustaining performance.
Hypothesis 1 (H1):
Board independence is positively associated with post-IPO financial
performance.
2 Long-Term Compensation and Profitability
The design of executive compensation plays a central role in agency theory. Compensation schemes that emphasize long-term objectives can reduce short-term opportunism and align managerial behavior with long-run firm value (Brauer, 2013). This alignment is especially critical post-IPO, when executives face new performance pressures and capital market expectations.
Hypothesis 2 (H2):
Executive compensation structures that include long-term performance
objectives are associated with higher post-IPO profitability.
3 Board Expertise and Operational Efficiency
Upper echelons theory posits that the experiences, knowledge, and values of top executives and board members significantly shape organizational outcomes (Hambrick & Mason, 1984). Boards composed of individuals with relevant industry or financial expertise are more likely to make informed strategic decisions and support management in executing efficient operations—an advantage during the volatile post-IPO adjustment phase.
Hypothesis 3 (H3):
A higher percentage of board members with domain-specific expertise
is positively associated with operational efficiency in the post-IPO
period.
4 CSR Structures and ESG Outcomes
Beyond traditional financial governance mechanisms, the adoption of formal corporate social responsibility (CSR) structures reflects an emerging approach to stakeholder engagement and legitimacy building. Legitimacy theory argues that organizations adopt socially acceptable structures and practices to gain legitimacy in the eyes of stakeholders (Suchman, 1995), while signaling theory suggests these structures help communicate long-term commitment to sustainability (Connelly et al., 2011).
Hypothesis 4 (H4):
Firms with established CSR governance structures exhibit higher ESG
scores in the post-IPO period.
5 ESG Performance and Market Valuation
From a strategic management perspective, firms with strong ESG performance may benefit from enhanced reputation, reduced risk, and improved access to resources, which in turn can translate into superior financial valuation (Eccles et al., 2014). These advantages are particularly important for firms navigating the heightened visibility and scrutiny that follow an IPO.
Hypothesis 5 (H5):
Higher ESG scores are associated with superior market-based
performance in the post-IPO period.
TobinsQ visualization
This is just to visuali observe that we do observe the effect of several companies getting negative Tobins’Q over the five years period.
knitr::include_graphics("C:/workspace/IPO/Fig/TobinsQ.png")
Load Data
# Load your merged IPO dataset
ipo_data <- read.csv("C:/workspace/IPO/data/merged_IPO_2014.csv")
# Ensure year and firm ID are treated as factors
#ipo_data$Year <- as.factor(ipo_data$Year)
ipo_data$ID <- as.factor(ipo_data$ID)Descriptive Statisitcs
# Define the variables used in your models
vars <- c(
"log_Net_Income", "Independent_Board", "log_Board_Size", "log_Debt_Total",
"log_Revenue", "log_Assets_Total", "Comp_LT_Objectives", "CEO_Comp_Link_TSR",
"log_Executives_Compensation", "Leverage", "Board_skills_percent", "log_Capex_Total",
"log_ESG_Score", "Board_Committee", "Audit_Expertise", "log_Market_Cap",
"log_Governance_Score", "log_Workforce_Score", "log_TobinsQ", "W_TobinsQ",
"W_Leverage", "log_Total_Liabilities", "log_Net_Cash_Flow_Opera", "ROA_Actual",
"CEO_Chairman_Duality", "CEO_Board_Member", "Founder_is_CEO", "CEO_Change_after_IPO",
"ESG_Score", "Governance_Score", "Comp_Controversies_Score", "W_ESG_Score",
"Num_Board_Meetings"
)
# Filter to available columns
vars <- intersect(vars, names(ipo_data))
# Select data and drop rows with missing values
desc_data <- ipo_data %>%
select(all_of(vars)) %>%
na.omit()
# Compute descriptive stats and round to 2 decimals
desc_stats <- desc_data %>%
summarise(across(everything(), list(
Mean = ~round(mean(.), 2),
SD = ~round(sd(.), 2),
Min = ~round(min(.), 2),
Max = ~round(max(.), 2)
), .names = "{.col}_{.fn}")) %>%
t() %>%
as.data.frame()
# Add readable variable names as a column
colnames(desc_stats) <- "Value"
desc_stats$Variable <- rownames(desc_stats)
rownames(desc_stats) <- NULL
# Reorder columns
desc_stats <- desc_stats %>%
tidyr::separate(Variable, into = c("Variable", "Statistic"), sep = "_(?=[^_]+$)") %>%
tidyr::pivot_wider(names_from = Statistic, values_from = Value)
#kable(desc_stats, digits = 2, caption = "Descriptive Statistics of Model Variables")
desc_stats %>%
kable(digits = 2, caption = "Descriptive Statistics of Model Variables") %>%
kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
scroll_box(height = "400px")| Variable | Mean | SD | Min | Max |
|---|---|---|---|---|
| log_Net_Income | 11.81 | 9.16 | 0.00 | 22.30 |
| Independent_Board | 76.35 | 13.79 | 33.33 | 93.33 |
| log_Board_Size | 2.22 | 0.23 | 1.61 | 2.71 |
| log_Debt_Total | 16.48 | 8.10 | 0.00 | 25.03 |
| log_Revenue | 19.75 | 4.16 | 0.00 | 25.60 |
| log_Assets_Total | 21.41 | 1.71 | 18.01 | 25.92 |
| Comp_LT_Objectives | 0.19 | 0.40 | 0.00 | 1.00 |
| CEO_Comp_Link_TSR | 0.20 | 0.40 | 0.00 | 1.00 |
| log_Executives_Compensation | 16.18 | 0.90 | 11.63 | 17.98 |
| Leverage | 0.28 | 0.25 | 0.00 | 1.04 |
| Board_skills_percent | 61.55 | 21.27 | 16.67 | 100.00 |
| log_Capex_Total | 17.17 | 2.01 | 11.16 | 22.27 |
| log_ESG_Score | 3.31 | 0.39 | 2.26 | 4.28 |
| Board_Committee | 0.97 | 0.16 | 0.00 | 1.00 |
| Audit_Expertise | 0.97 | 0.16 | 0.00 | 1.00 |
| log_Market_Cap | 21.52 | 1.27 | 18.16 | 25.22 |
| log_Governance_Score | 3.26 | 0.74 | 0.76 | 4.55 |
| log_Workforce_Score | 3.48 | 0.74 | 0.76 | 4.48 |
| log_TobinsQ | 0.97 | 0.73 | 0.00 | 3.37 |
| W_TobinsQ | 2.32 | 2.57 | 0.00 | 9.47 |
| W_Leverage | 0.28 | 0.25 | 0.00 | 1.04 |
| log_Total_Liabilities | 20.88 | 1.97 | 16.51 | 25.84 |
| log_Net_Cash_Flow_Opera | 14.56 | 8.35 | 0.00 | 23.65 |
| ROA_Actual | -0.02 | 0.23 | -1.60 | 0.35 |
| CEO_Chairman_Duality | 0.54 | 0.50 | 0.00 | 1.00 |
| CEO_Board_Member | 0.98 | 0.14 | 0.00 | 1.00 |
| Founder_is_CEO | 0.28 | 0.45 | 0.00 | 1.00 |
| CEO_Change_after_IPO | 0.28 | 0.52 | 0.00 | 2.00 |
| ESG_Score | 28.58 | 12.30 | 8.57 | 71.23 |
| Governance_Score | 31.76 | 20.50 | 1.14 | 93.52 |
| Comp_Controversies_Score | 51.62 | 0.21 | 51.45 | 52.70 |
| W_ESG_Score | 28.58 | 12.30 | 8.57 | 71.23 |
| Num_Board_Meetings | 7.39 | 3.35 | 3.00 | 23.00 |
Correlation
selected_data <- ipo_data[, c('W_Leverage',
'log_Total_Liabilities',
'log_Net_Cash_Flow_Opera',
'log_Revenue',
'ROA_Actual',
'CEO_Comp_Link_TSR',
'CEO_Chairman_Duality',
'Independent_Board',
'CEO_Board_Member',
'Num_Board_Meetings',
'Founder_is_CEO',
'CEO_Change_after_IPO',
'Comp_LT_Objectives',
'ESG_Score',
'Governance_Score',
'Comp_Controversies_Score',
'Audit_Expertise')]
# Check if all columns are numeric
# Convert all columns to numeric, handling conversion issues and NAN
selected_data[] <- lapply(selected_data, function(x) {
x <- as.numeric(as.character(x))
x[is.infinite(x)] <- NA
x
})
# Recompute the correlation matrix handling NA values
correlation_matrix <- cor(selected_data, use = "pairwise.complete.obs")
if (all(is.finite(correlation_matrix))) {
corrplot(correlation_matrix, method = "number", type = "upper", order = "hclust",
tl.cex = 0.8, tl.col = "black", tl.srt = 45,
number.cex = 0.7, addCoef.col = "black", cl.cex = 0.8)
}
H1: Board independence and post-IPO financial performance
Theory: Agency theory posits that more independent boards improve monitoring and reduce agency costs, enhancing firm performance.
# Fixed effects model
model_h1 <- plm(log_Net_Income ~ Independent_Board + log_Board_Size +
log_Debt_Total + log_Revenue + log_Assets_Total + Year,
data = ipo_data, index = c("ID", "Year"), model = "within")
# Robust SEs clustered at firm level
coeftest(model_h1, vcov = vcovHC(model_h1, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Independent_Board -0.0120843 0.0233630 -0.5172 0.6056
## log_Board_Size 3.1626000 2.0874434 1.5151 0.1314
## log_Debt_Total -0.0073064 0.0222650 -0.3282 0.7431
## log_Revenue 0.2040940 0.1806011 1.1301 0.2598
## log_Assets_Total 1.4851345 0.9823235 1.5119 0.1322
## Year2015 -0.0099398 0.7645572 -0.0130 0.9896
## Year2016 0.0119556 0.5489715 0.0218 0.9826
## Year2017 -0.5304535 0.8070391 -0.6573 0.5118
## Year2018 -0.6470294 1.0483780 -0.6172 0.5378
## Year2019 -0.6610927 1.1405166 -0.5796 0.5628H2: Long-term incentive alignment and profitability
Theory: Linking executive pay to long-term goals aligns managerial incentives with shareholder interests (Agency theory).
model_h2 <- plm(log_Net_Income ~ Comp_LT_Objectives + CEO_Comp_Link_TSR +
log_Executives_Compensation + log_Assets_Total +
log_Debt_Total + Leverage + Year,
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h2, vcov = vcovHC(model_h2, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Comp_LT_Objectives 0.239037 0.968552 0.2468 0.8053
## CEO_Comp_Link_TSR -0.490636 0.545292 -0.8998 0.3694
## log_Executives_Compensation -0.052332 0.367021 -0.1426 0.8868
## log_Assets_Total 1.421051 1.017654 1.3964 0.1642
## log_Debt_Total 0.059863 0.050070 1.1956 0.2334
## Leverage -5.712078 5.074847 -1.1256 0.2618
## Year2015 0.759982 0.787614 0.9649 0.3358
## Year2016 0.042319 0.807731 0.0524 0.9583
## Year2017 0.235419 0.905719 0.2599 0.7952
## Year2018 0.295758 1.081112 0.2736 0.7847
## Year2019 0.464886 1.008986 0.4607 0.6455H3: Board skills and operational efficiency
Theory: Upper echelons theory suggests skilled boards enhance strategic and operational outcomes post-IPO.
model_h3 <- plm(log_Net_Cash_Flow_Opera ~ Board_skills_percent +
log_Board_Size + log_Assets_Total + log_Capex_Total +
+ Year,
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h3, vcov = vcovHC(model_h3, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Board_skills_percent -0.0090961 0.0316291 -0.2876 0.7740
## log_Board_Size -4.7667777 2.9691874 -1.6054 0.1101
## log_Assets_Total 0.8479998 1.9607778 0.4325 0.6659
## log_Capex_Total 0.2327062 0.2427400 0.9587 0.3390
## Year2015 -1.2641683 3.8548320 -0.3279 0.7433
## Year2016 2.3245969 1.7548544 1.3247 0.1869
## Year2017 1.8267980 1.8040742 1.0126 0.3126
## Year2018 0.9636565 2.2471987 0.4288 0.6685
## Year2019 0.4180883 2.5871197 0.1616 0.8718H4: CSR structures and ESG score
Theory: Firms with formal CSR mechanisms signal legitimacy and improve ESG outcomes (CSR/Legitimacy theory).
model_h4 <- plm(log_ESG_Score ~ Board_Committee + Audit_Expertise +
Comp_LT_Objectives + CEO_Comp_Link_TSR +
log_Market_Cap + log_Governance_Score + log_Workforce_Score +
factor(Year),
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h4, vcov = vcovHC(model_h4, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Board_Committee -0.097179 0.066922 -1.4521 0.14813
## Audit_Expertise 0.048501 0.034958 1.3874 0.16696
## Comp_LT_Objectives 0.049489 0.026066 1.8986 0.05914 .
## CEO_Comp_Link_TSR -0.018885 0.031824 -0.5934 0.55360
## log_Market_Cap -0.032896 0.022738 -1.4467 0.14964
## log_Governance_Score 0.356585 0.038239 9.3251 < 2.2e-16 ***
## log_Workforce_Score 0.143160 0.028870 4.9588 1.573e-06 ***
## factor(Year)2015 0.226203 0.044993 5.0275 1.149e-06 ***
## factor(Year)2016 0.262262 0.048914 5.3617 2.387e-07 ***
## factor(Year)2017 0.280766 0.053808 5.2179 4.734e-07 ***
## factor(Year)2018 0.339575 0.054595 6.2199 3.124e-09 ***
## factor(Year)2019 0.405347 0.055867 7.2556 1.004e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1H5: ESG performance and financial valuation
Theory: Strong ESG performance enhances reputation and valuation (Strategic management theory).
model_h5 <- plm(log_TobinsQ ~ log_ESG_Score + log_Governance_Score +
log_Workforce_Score + log_Assets_Total + log_Revenue +
log_Leverage + log_Capex_Total + factor(Year),
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h5, vcov = vcovHC(model_h5, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## log_ESG_Score -0.1599907 0.1027682 -1.5568 0.121233
## log_Governance_Score 0.0025102 0.0590237 0.0425 0.966124
## log_Workforce_Score 0.0336742 0.0505504 0.6662 0.506149
## log_Assets_Total -0.3127441 0.1065171 -2.9361 0.003748 **
## log_Revenue 0.0089442 0.0195580 0.4573 0.647982
## log_Leverage -0.0444814 0.4635939 -0.0959 0.923666
## log_Capex_Total -0.0191040 0.0386630 -0.4941 0.621814
## factor(Year)2015 -0.0627083 0.1000249 -0.6269 0.531484
## factor(Year)2016 -0.2165614 0.0710663 -3.0473 0.002649 **
## factor(Year)2017 -0.1149640 0.0766387 -1.5001 0.135308
## factor(Year)2018 -0.1987927 0.0844140 -2.3550 0.019578 *
## factor(Year)2019 -0.1256507 0.1059835 -1.1856 0.237321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Alternative Modeling considering TobinsQ as DV
Hypotheses Development
Going public represents a pivotal moment in a firm’s lifecycle, marking a transition from private governance to public scrutiny. While an IPO provides firms with access to capital markets, it also introduces new accountability structures, managerial pressures, and strategic complexities. Understanding the mechanisms that influence firm valuation in the post-IPO period is therefore critical.
Drawing on agency theory, upper echelons theory, and legitimacy theory, this study explores how governance characteristics, executive incentives, and sustainability commitments shape post-IPO market performance, as captured by Tobin’s Q—a forward-looking measure of firm valuation.
Board Independence and Market Valuation
Agency theory suggests that independent boards improve oversight and reduce agency costs by aligning managerial actions with shareholder interests (Fama & Jensen, 1998). Post-IPO, when ownership becomes more dispersed, the presence of independent directors may enhance market confidence and firm valuation.
Hypothesis 1 (H1):
Board independence is positively associated with Tobin’s Q in the
post-IPO period.
# Fixed effects model
model_h1 <- plm(W_TobinsQ ~ Independent_Board + Leverage+ log_Board_Size +
log_Debt_Total + log_Revenue + log_Assets_Total + factor(Year),
data = ipo_data, index = c("ID", "Year"), effect = "individual", model = "within")
# Robust SEs clustered at firm level
coeftest(model_h1, vcov = vcovHC(model_h1, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Independent_Board -0.0013378 0.0090777 -0.1474 0.88299
## Leverage 1.4902166 1.2562664 1.1862 0.23698
## log_Board_Size -0.7345796 0.6204840 -1.1839 0.23790
## log_Debt_Total -0.0406256 0.0265354 -1.5310 0.12739
## log_Revenue 0.0285921 0.0748111 0.3822 0.70274
## log_Assets_Total -1.6108363 0.3793151 -4.2467 3.356e-05 ***
## factor(Year)2015 0.0024719 0.3241539 0.0076 0.99392
## factor(Year)2016 -0.5715180 0.2340653 -2.4417 0.01551 *
## factor(Year)2017 -0.1714678 0.2226472 -0.7701 0.44215
## factor(Year)2018 -0.3875821 0.2304865 -1.6816 0.09425 .
## factor(Year)2019 -0.1844452 0.2783775 -0.6626 0.50839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Long-Term Incentives and Market Confidence
Executive compensation structured around long-term goals reduces short-termism and aligns managers’ incentives with sustained value creation (Brauer, 2013). Investors may reward firms that implement such mechanisms, interpreting them as signals of long-term strategic focus.
Hypothesis 2 (H2):
The use of long-term performance-based executive compensation is
positively associated with Tobin’s Q after the IPO.
#log_Assets_Total when included makes Comp_LT_Objectives insignificant
model_h2 <- plm(W_TobinsQ ~ Comp_LT_Objectives + CEO_Comp_Link_TSR + Leverage +
log_Executives_Compensation + log_Revenue +
factor(Year),
data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")
coeftest(model_h2, vcov = vcovHC(model_h2, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Comp_LT_Objectives -0.602500 0.257636 -2.3386 0.020408 *
## CEO_Comp_Link_TSR -0.275867 0.210804 -1.3086 0.192253
## Leverage 0.264936 1.822542 0.1454 0.884577
## log_Executives_Compensation -0.077478 0.161933 -0.4785 0.632879
## log_Revenue -0.039047 0.076625 -0.5096 0.610937
## factor(Year)2015 -0.288006 0.485334 -0.5934 0.553614
## factor(Year)2016 -0.769588 0.298784 -2.5757 0.010771 *
## factor(Year)2017 -0.475469 0.350757 -1.3555 0.176869
## factor(Year)2018 -0.902514 0.358422 -2.5180 0.012636 *
## factor(Year)2019 -0.897563 0.332134 -2.7024 0.007514 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Board Expertise and Strategic Decision Quality
According to upper echelons theory (Hambrick & Mason, 1984), the background and competencies of board members shape strategic outcomes. Boards with more domain-specific expertise can enhance firm decision-making and adaptability, thereby improving market valuation.
Hypothesis 3 (H3):
A higher proportion of directors with industry or financial
expertise is positively associated with Tobin’s Q post-IPO.
#log_Assets_Total when included makes Comp_LT_Objectives insignificant
model_H3 <- plm(W_TobinsQ ~ Board_skills_percent + log_Board_Size + log_Revenue +log_Capex_Total + Leverage,
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_H3, vcov = vcovHC(model_H3, type = "HC1"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## Board_skills_percent -0.0103418 0.0045798 -2.2581 0.02507 *
## log_Board_Size -0.5959233 0.8479911 -0.7027 0.48307
## log_Revenue -0.0588356 0.0804434 -0.7314 0.46544
## log_Capex_Total 0.0690236 0.0551695 1.2511 0.21242
## Leverage -0.1892797 1.9441155 -0.0974 0.92254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# If we include Year we got Capex significant, but we lose Board Skills
# model_H3 <- plm(W_TobinsQ ~ Board_skills_percent + log_Board_Size + log_Revenue +log_Capex_Total + Leverage + Year,
# data = ipo_data, index = c("ID", "Year"), model = "within")
# coeftest(model_H3, vcov = vcovHC(model_H3, type = "HC1", cluster = "group"))CSR Governance and Market Perception
Legitimacy theory (Suchman, 1995) and signaling theory (Connelly et al., 2011) suggest that formal CSR structures signal commitment to broader stakeholder goals, environmental responsibility, and social ethics. These structures may be viewed favorably by investors seeking sustainable, forward-thinking firms.
Hypothesis 4 (H4):
Firms with formal CSR governance structures exhibit higher Tobin’s Q
in the post-IPO period.
model_h4 <- plm(W_TobinsQ ~ log_ESG_Score + Board_Committee + Audit_Expertise +
Comp_LT_Objectives + CEO_Comp_Link_TSR + log_Revenue+ Leverage+
log_Market_Cap + log_Governance_Score + log_Workforce_Score,
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h4, vcov = vcovHC(model_h4, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## log_ESG_Score -0.372125 0.357964 -1.0396 0.29986
## Board_Committee 0.637127 0.538914 1.1822 0.23858
## Audit_Expertise -0.156231 0.352025 -0.4438 0.65769
## Comp_LT_Objectives -0.476167 0.186847 -2.5484 0.01161 *
## CEO_Comp_Link_TSR -0.349125 0.207291 -1.6842 0.09377 .
## log_Revenue -0.117180 0.059131 -1.9817 0.04895 *
## Leverage -0.416639 1.576244 -0.2643 0.79182
## log_Market_Cap 1.261915 0.259624 4.8605 2.436e-06 ***
## log_Governance_Score -0.135487 0.272207 -0.4977 0.61924
## log_Workforce_Score 0.160173 0.148339 1.0798 0.28160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Year diminish the significance
#However it shows that ESG matters in some years more than others, trends are changing over time
# model_h4 <- plm(W_TobinsQ ~ log_ESG_Score + Board_Committee + Audit_Expertise +
# Comp_LT_Objectives + CEO_Comp_Link_TSR + log_Revenue+ Leverage+
# log_Market_Cap + log_Governance_Score + log_Workforce_Score +
# factor(Year)+log_ESG_Score * factor(Year),
# data = ipo_data, index = c("ID", "Year"), model = "within")
# coeftest(model_h4, vcov = vcovHC(model_h4, type = "HC1", cluster = "group"))
#
# esg_effects <- c(2014, 2015, 2016, 2017, 2018)
# estimates <- c(0.288, 0.288 - 2.086, 0.288 + 0.326, 0.288 - 0.044, 0.288 + 0.116)
# plot(esg_effects, estimates, type = "b", ylab = "Marginal Effect of log_ESG_Score on Tobin's Q", xlab = "Year")
#abline(h = 0, lty = 2)ESG Performance and Firm Valuation
Strong ESG performance can serve as a strategic asset—enhancing reputation, reducing regulatory risk, and attracting capital. From a strategic management perspective, these factors contribute to competitive advantage and superior market valuation (Eccles et al., 2014).
Hypothesis 5 (H5):
Higher ESG scores are positively associated with Tobin’s Q in the
post-IPO period.
# If we include + log_Assets_Total we got no significance
# log_Governance_Score largely diminish the significance of W_ESG_Score
#+ factor(Year) Makes the results insignificant
model_H5 <- plm(W_TobinsQ ~ W_ESG_Score + log_Revenue + Leverage + log_Workforce_Score +log_Capex_Total,
data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_H5, vcov = vcovHC(model_H5, type = "HC1"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## W_ESG_Score -0.0278063 0.0087265 -3.1864 0.001683 **
## log_Revenue -0.0615562 0.0785512 -0.7836 0.434220
## Leverage -0.1308543 1.8670913 -0.0701 0.944200
## log_Workforce_Score 0.1975716 0.1857475 1.0637 0.288827
## log_Capex_Total 0.0615151 0.0511171 1.2034 0.230305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(model_H5, vcov = vcovHC(model_H5, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## W_ESG_Score -0.0278063 0.0087265 -3.1864 0.001683 **
## log_Revenue -0.0615562 0.0785512 -0.7836 0.434220
## Leverage -0.1308543 1.8670913 -0.0701 0.944200
## log_Workforce_Score 0.1975716 0.1857475 1.0637 0.288827
## log_Capex_Total 0.0615151 0.0511171 1.2034 0.230305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Using LASSO to select the best model
# Load required libraries
# STEP 1: Select relevant variables and remove missing values
lasso_data <- ipo_data %>%
select(W_TobinsQ, W_Leverage, log_Total_Liabilities, log_Net_Cash_Flow_Opera,
log_Revenue, ROA_Actual, CEO_Comp_Link_TSR, CEO_Chairman_Duality,
CEO_Board_Member, Founder_is_CEO, CEO_Change_after_IPO, Comp_LT_Objectives,
Board_Size, Board_skills_percent, Independent_Board, Num_Board_Meetings,
ESG_Score, Governance_Score, Comp_Controversies_Score, Audit_Expertise) %>%
na.omit()
# STEP 2: Prepare predictor matrix (X) and response vector (y)
x <- model.matrix(W_TobinsQ ~ ., data = lasso_data)[, -1] # remove intercept column
y <- lasso_data$W_TobinsQ
# STEP 3: Run cross-validated LASSO
set.seed(123)
cv_lasso <- cv.glmnet(x, y, alpha = 1, nfolds = 10) # alpha = 1 for LASSO
# Plot CV errors and selected lambda
plot(cv_lasso)
abline(v = log(cv_lasso$lambda.min), col = "red", lty = 2)
title("LASSO Cross-Validation", line = 2.5)
# STEP 4: Fit LASSO with best lambda
best_lambda <- cv_lasso$lambda.min
lasso_model <- glmnet(x, y, alpha = 1, lambda = best_lambda)
# STEP 5: Show coefficients retained by LASSO
selected_coefs <- coef(lasso_model)
nonzero_coefs <- selected_coefs[which(selected_coefs != 0), , drop = FALSE]
print(nonzero_coefs)## 10 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.384581215
## W_Leverage -1.082310588
## log_Total_Liabilities -0.603190165
## log_Net_Cash_Flow_Opera 0.014566154
## CEO_Comp_Link_TSR -0.567857062
## Comp_LT_Objectives 0.608577311
## Board_skills_percent 0.010718057
## Independent_Board 0.012848014
## Num_Board_Meetings -0.005566579
## Audit_Expertise 1.097483594# STEP 6: Refit a standard linear model using selected variables
# Get variable names of selected predictors (excluding intercept)
selected_vars <- rownames(nonzero_coefs)[-1]
# Build formula dynamically
lasso_formula <- as.formula(paste("W_TobinsQ ~", paste(selected_vars, collapse = " + ")))
# Refit model on original data (you can optionally standardize predictors here)
final_model <- lm(lasso_formula, data = lasso_data)
# STEP 7: Summary of the final model
summary(final_model)##
## Call:
## lm(formula = lasso_formula, data = lasso_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6329 -1.3302 -0.4024 0.8820 6.0812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.569597 1.956000 6.426 1.62e-09 ***
## W_Leverage -1.113075 0.658181 -1.691 0.09287 .
## log_Total_Liabilities -0.745676 0.095625 -7.798 9.55e-13 ***
## log_Net_Cash_Flow_Opera 0.049539 0.022062 2.245 0.02619 *
## CEO_Comp_Link_TSR -0.933147 0.403193 -2.314 0.02199 *
## Comp_LT_Objectives 1.130549 0.418112 2.704 0.00764 **
## Board_skills_percent 0.020538 0.007983 2.573 0.01106 *
## Independent_Board 0.026139 0.011468 2.279 0.02405 *
## Num_Board_Meetings -0.004318 0.051452 -0.084 0.93323
## Audit_Expertise 1.775518 1.012935 1.753 0.08166 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.93 on 151 degrees of freedom
## Multiple R-squared: 0.458, Adjusted R-squared: 0.4257
## F-statistic: 14.18 on 9 and 151 DF, p-value: < 2.2e-16References
Brauer, M. F. (2013). The effects of short-term and long-term oriented managerial behavior on medium-term financial performance: Longitudinal evidence from Europe. Journal of Business Economics and Management, 14(2), 386–402. https://doi.org/10.3846/16111699.2012.703965
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835–2857. https://doi.org/10.1287/mnsc.2014.1984
Fama, E. F., & Jensen, M. C. (1998). Separation of ownership and control. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.94034
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193–206. https://doi.org/10.2307/258434
Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. The Academy of Management Review, 20(3), 571–610. https://doi.org/10.2307/258788
Extras
# Check for any infinite or NA values in the correlation matrix
if(any(is.infinite(correlation_matrix), na.rm = TRUE) || any(is.na(correlation_matrix), na.rm = TRUE)) {
print("Correlation matrix still contains Inf or NA values")
} else {
# Perform hierarchical clustering if the correlation matrix is clean
dist_matrix <- as.dist(1 - correlation_matrix)
hc <- hclust(dist_matrix, method = "complete")
plot(hc) # Plot the dendrogram
}
NOTES
VIF was tested multiple times none of them was above 5
# management <- plm(W_TobinsQ ~
# W_Leverage+
# log_Total_Liabilities+
# log_Net_Cash_Flow_Opera+
# log_Revenue+
# ROA_Actual+
# #log_Capex_Total+
#
#
# #management
# CEO_Comp_Link_TSR+
# #log_Executives_Compensation+
#
# CEO_Chairman_Duality+
# CEO_Board_Member+
# Founder_is_CEO+
# CEO_Change_after_IPO+
#
# Comp_LT_Objectives+
#
# #governance
#
# Board_Size+
# Board_skills_percent+
# #Board_Committee+
#
#
# Independent_Board+
# Num_Board_Meetings+
#
# #ESG Sustainability
# #log_Workforce_Score+
#
# ESG_Score+
# Governance_Score+
# Comp_Controversies_Score+
# Audit_Expertise,
#
#
#
# data = ipo_data, effect="twoways", index=c("ID", "Year"), model="within")
# summary(management) GVIF Df GVIF^(1/(2*Df))W_Leverage 1.594694 1 1.262812 log_Total_Liabilities 3.006988 1 1.734067 log_Net_Cash_Flow_Opera 2.153921 1 1.467624 log_Revenue 3.205346 1 1.790348 ROA_Actual 3.056225 1 1.748206 CEO_Comp_Link_TSR 1.320819 1 1.149269 CEO_Chairman_Duality 1.340014 1 1.157590 Independent_Board 1.514847 1 1.230791 CEO_Board_Member 1.292200 1 1.136750 Num_Board_Meetings 1.451529 1 1.204794 Founder_is_CEO 1.562795 1 1.250118 CEO_Change_after_IPO 1.501618 1 1.225405 Comp_LT_Objectives 1.496082 1 1.223144 ESG_Score 2.616260 1 1.617486 Governance_Score 3.516537 1 1.875243 Comp_Controversies_Score 2.282947 1 1.510942 Audit_Expertise 1.148574 1 1.071715 factor(Year) 3.108593 4 1.152313