Corporate Governance as a Safeguard Against Sentiment-Driven Stock Price Crashes: Evidence from the KSE-100 Index

Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 319 ©2025 PJSS, Bahauddin Zakariya University Multan Pakistan Corporate Governance as a Safeguard Against Sentiment-Driven Stock Price Crashes: Evidence from the KSE-100 Index a Anum Durrani, b Muhammad Abbass a PhD (Management Sciences) Scholar, Air University Islamabad, Multan Campus, Pakistan Email: [email protected] b Professor, Air University Islamabad, Multan Campus, Pakistan Email: [email protected] ARTICLE DETAILS ABSTRACT History: Accepted: 08 December, 2025 Available Online: 18 December, 2025 Purpose: The study looks at how corporate governance moderates Investor sentiment’s effect on likelihood of crashes. The research goal is to identify whether robust governance practices can shield businesses from crash risk and sentiment-driven price distortions. Design/ Methodology: Five proxies are used to measure investor sentiment, as suggested by Baker and Wurgler (2006, 2007). Two components of the corporate governance process are the ownership and board structures. In contrast, two metrics are used to calculate the danger of a stock price meltdown, i.e., NSKEW and DUVOL, using KSE-100 index companies from 2010 and 2022. Both the direct and moderating effects are tested using panel regression models. Findings: The noise trader theory is largely supported by findings. Consistent with previous research, the findings show that investor sentiment positively affects SPCR. Additionally, research indicates that corporate governance reduces the correlation between investor sentiment and the risk of a financial crisis, which is in line with the agency theory. Implications: The results will have a big impact on investors, legislators, and regulators. If market players understand how attitude affects crash risk, they will be better equipped to assess firm-level vulnerabilities. Strengthening governance mechanisms, especially ownership and board structures, can increase market stability, decrease information hoarding, and improve transparency. © 2025 The authors. Published by PJSS, BZU. This is an open-access research paper under the Creative Commons Attribution-Non-Commercial 4.0 Keywords: Stock Price Crash Risk Investor Sentiments Board Structure Ownership Structure Recommended Citation: Durrani, A., & Abbas, M. (2025). Corporate Governance as a Safeguard Against Sentiment-Driven Stock Price Crashes: Evidence from the KSE-100 Index. Pakistan Journal of Social Sciences, 45(4), 319-326. DOI: 10.5281/zenodo.17973852 *Corresponding Author’s email address: [email protected] 1. Introduction The crash risk has garnered attention from researchers, investors, and decision makers because it has substantial ramifications for market efficiency and financial stability. It is a sharp drop in stock price brought on by the unexpected disclosure of unfavorable information that managers have hoarded. According to Jin and Myers (2006), crash risk is important for academics and professionals because it destroys shareholders' wealth and decreases their confidence in financial stability. Pakistan Journal of Social Sciences ISSN (E) 2708-4175 ISSN (P) 2074-2061 Volume 45: Issue 4 December 2025 Journal homepage: https://pjss.bzu.edu.pk Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 320 By emphasizing the conflict of interest between management and shareholders, the Agency Theory (Jensen & Meckling, 1976) offers important information for crash risk. Managers may hide the business's unfavorable information for their own benefits. Investors face information asymmetry due to intentional concealment. As the unfavorable information is made public, the market reacts and the stock prices go down (Kothari, Shu, & Wysocki, 2009). Hence, in a weak governance framework, the managers make false statements. Conversely, the strong corporate governance framework monitors the behavior of managers and reduces information asymmetry (Bushman & Smith, 2001). Additionally, behavioral aspects also make the stock prices move. According to the Noise Trader Theory (DeLong et al., 1990), investors make their trades on sentiment rather than fundamentals. These sentimental investors have the ability to take the asset prices away from their intrinsic values. The overly optimistic investor takes the prices above the fundamentals, and the correction causes the crash (Baker & Wurgler, 2006). The behavioral finance and agency theory intersect to determine that sentiments and governance work together to reduce crash risk. Strong governance procedures can moderate this link by preventing managerial opportunism and guaranteeing increased openness. The study contributes to the literature by using governance as a moderator and sentiment index at a firm level. 2. Literature and Hypothesis Development A growing body of research has looked into several underlying elements that contribute to increased risk of a stock price fall. The casual impact of investor sentiments on capital market behavior has been determined by earlier research by Baker and Wurgler (2006). Alnafea and Chubbi (2021) studied the relationship between investor mood and stock meltdown and discovered a positive correlation. Wu, Cai & Zhang (2021) investigate the link between sentiments and crash risk and identifying a positive link. Additionally, if we connect corporate governance with the danger of price fall, we may find that corporate governance has a fundamental function of monitoring and resolving agency issues (Meckling & Jensen, 1976; Fama, 1970; Fama & Jensen, 1983). The poor corporate governance helps the managers in availing opportunities for their own benefits and hiding unfavorable news until the crisis (Nguyen, 2023). According to Jensen et al. (2022), poor governance mechanisms may result in information asymmetry and the price crash. In their study, Wang et al. (2015) examined the connection between company risk and corporate governance practices. The strong corporate governance helps in reducing crash risk. Li and Zhao (2024) argue that larger boards contribute to better transparency in financial reporting. Companies with larger boards are less likely to experience stock market crashes because they are better at managing management and ensuring that bad news is shared quickly, claim Xu et al. (2023). The following hypotheses are put out by this study in light of the above discussion: H1: Investor sentiment positively affects SPCR H2: Board structure negatively affects SPCR H3: Ownership structure negatively affects SPCR H4: Board structure moderate the impact of investor sentiments on SPCR H5: Ownership structure moderate the impact of investor sentiment on SPCR 3. Methodology 3.1 Sample of the study This study uses panel data setting with 66 cross sections and quarterly observations of 13 years. It has taken the KSE 100 index sample includes 66 nonfinancial companies (excluding the financial firms from the 100 index). The primary reason for the exclusion is that the capital structure of financial institutions is different from those of non- Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 321 financial businesses. Quarterly data in the time period of 2010 Q1 to 2022 Q4 has been derived from the financial reports of companies available on the DataStream database. Data about the stock market activities were collected from scstrading.com and investing.com. 3.2 Measuring the variables 3.2.1 Measuring stock price crash risk Depending on the weekly resumption of the value of stocks, the investigation measured the risk of a stock price crash using negative skewness (NSKEW) and fluctuations in stock prices at different times (down to up volatility). Chen et al. (2001) employed NSKEW and DUVOL as indicators of the danger of a stock market collapse. The study estimated each firm's weekly return using the expanded market model regression as follows: 𝑅𝑖,𝑡 =𝑎𝑖+𝐵1,𝑖𝑅𝑚,𝑡−2+𝐵2,𝑖𝑅𝑚,𝑡−1+𝐵3,𝑖𝑅𝑚,𝑡 +𝐵4,𝑖𝑅𝑚,𝑡+1+𝐵5,𝑖𝑅𝑚,𝑡+2+ℇ𝑖,𝑡 (1) The stock’s increase during week t is denoted by 𝑅𝑖,𝑡 , while the value-weighted market index for quarter t is denoted by 𝑅𝑚,𝑡. Equation 1's error term is utilized to determine the weekly return at the firm-specific level: 𝑊𝑖,𝑡 =ln⁡(1+𝜀𝑖,𝑡) (2) This study employed the adverse coefficient of skewness as the initial measure of the likelihood of a share price implosion, relying on the company-specific per-week yield. According to Chen et al. (2001), the minus value of the last instant of a particular company's per-week return is the minus sign of skewness. The NCSKEW is calculated using the following formula: 𝑁𝑆𝐾𝐸𝑊=−[ 𝑛(𝑛−1)3 2∑𝑊𝑗,𝑡 3] [(𝑛−1)(𝑛−2)(∑𝑊𝑗,𝑡 2)]]3/2 (3) where t is the time, and n is the amount of data for the weekly return particular to the firm. The down-to-up volatility (DUVOL) of firm-specific weekly returns is the second indicator of the danger of a stock market meltdown. The following formula is used to determine down-to-up volatility (DUVOL): 𝐷𝑈𝑉𝑂𝐿={[𝑙𝑛(𝑛𝑢−1)[𝑙𝑛(𝑛𝑑−1)]} (4) Throughout time t, the quantity of rising weeks is denoted by 𝑛𝑢, whereas the number of down weeks is denoted by 𝑛𝑑. In general, it is found that the crash risk increases with NCSKEW and DUVOL values. 3.2.2 Measuring Investor Sentiments According to Baker and Wugler (2006) and Wu et al. (2021), the study employs principal component analysis, or PCA, to develop a composite index for investor attitudes at the firm-specific level. The investor sentiment is calculated as: 𝐼𝑛𝑣𝑒𝑠𝑡𝑜𝑟⁡𝑠𝑒𝑛𝑡𝑖𝑚𝑒𝑛𝑡=0.096(𝑃/𝐸)+⁡−0.665(𝑇𝐴)+⁡0.040(𝑂𝑅)+0.370(𝐶𝐸𝐹𝐷)+0.639(𝐷𝑃) The price-earnings ratio is the proportion of share price to earnings per share. According to Han and Li's (2017) research, the price-earnings ratio—which rises in optimistic markets and falls in negative ones—can be used to gauge investor sentiment. The price earnings ratio is calculated by using the following formula: 𝑃𝑟𝑖𝑐𝑒𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑅𝑎𝑡𝑖𝑜= 𝑆ℎ𝑎𝑟𝑒𝑃𝑟𝑖𝑐𝑒 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑃𝑒𝑟𝑆ℎ𝑎𝑟𝑒 (5) According to the study, the turnover ratio is the proportion of shares that are traded compared to the total number of shares. 𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟𝑅𝑎𝑡𝑖𝑜= 𝑆ℎ𝑎𝑟𝑒𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟 𝑇𝑜𝑡𝑎𝑙𝑁𝑢𝑚𝑏𝑒𝑟𝑂𝑓𝑆ℎ𝑎𝑟𝑒𝑠 (6) Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 322 The study defines the overnight rate as the rate at which lending occurs at the end of the day. The overnight rate is calculated by taking the close-to-close rate and the intraday rate. The close-to-close rate is calculated through the formula given below: 𝑅𝑐𝑙𝑜𝑠𝑒⁡𝑡𝑜⁡𝑐𝑙𝑜𝑠𝑒 =𝑃𝑐𝑙𝑜𝑠𝑒,𝑡 −𝑃𝑐𝑙𝑜𝑠𝑒,𝑡−1/𝑃𝑐𝑙𝑜𝑠𝑒,𝑡−1 (7) The intraday rate is calculated through the formula given below: 𝑅𝑖𝑛𝑡𝑟𝑎𝑑𝑎𝑦 =𝑃ℎ𝑖𝑔ℎ−𝑃𝑙𝑜𝑤/𝑃𝑐𝑙𝑜𝑠𝑒 (8) The overnight return is calculated through the formula given below: 𝑅𝑜𝑣𝑒𝑟𝑛𝑖𝑔ℎ𝑡 =1+𝑅𝑐𝑙𝑜𝑠𝑒⁡𝑡𝑜⁡𝑐𝑙𝑜𝑠𝑒/1+𝑅𝑖𝑛𝑡𝑟𝑎𝑑𝑎𝑦 −1 (9) The close end fund discount is calculated through the formula given below: 𝐶𝑙𝑜𝑠𝑒𝐸𝑛𝑑𝐹𝑢𝑛𝑑𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡=(𝑆ℎ𝑎𝑟𝑒𝑃𝑟𝑖𝑐𝑒 𝑁𝐴𝑉 )−1 (10) Baker and Wurgler (2004) defined dividend premium as the gap between the average market to book ratio of those paying dividends and those that don't pay. 3.2.3 Measuring corporate Governance The moderating variable under test is corporate governance, which consists of board structure and ownership structure. We created a board structure index using principal components analysis (PCA). Four proxies were utilized for board structure, and they agree with earlier research by Kamran and Shah (2014). According to Bhagat and Bolton (2008), board independence (𝑖𝑛𝑑_𝑏𝑜𝑎𝑟𝑑𝑖,𝑡) is evaluated as the ratio of autonomous directors to total directors. The number of board directors is used to calculate board size (𝐵𝑜𝑎𝑟𝑑_𝑠𝑖𝑧𝑒𝑖,𝑡). A dummy variable called CEO duality (𝐶𝐸𝑂_𝐷𝑢𝑎𝑙𝑖𝑡𝑦𝑖,𝑡) is used to calculate CEO duality. When the CEO happens to be in charge of board chairman, its value is one; otherwise, it is zero. The study explains the concept of gender board diversity by having women on a board to change trends. Cumming, Leung, and Rui (2015) explained in their study that gender diversity helps reduce fraud and stock price crash risk. Board structure is calculated as: Board Structure = -0.563 (Board Gender Diversity) + 0.701 (Board Size) + -0.275 (CEO. Duality) + 0.339 (Independent Directors) The percentage of shares held by institutional shareholders (𝑖𝑛𝑠𝑡_𝑖𝑛𝑣𝑖,𝑡); the percentage of shares held by the board (𝐵𝑜𝑎𝑟𝑑_𝑖𝑛𝑣𝑖,𝑡); the percentage of shares held by foreign investors (𝑓𝑜𝑟𝑒𝑖𝑔𝑛_𝑖𝑛𝑣𝑖,𝑡); and the percentage of shares held by the blockholders are the four ownership structure measures used in our analysis (Alam & Ali Shah, 2013; Kamran & Shah, 2014). Ownership structure is calculated as Ownership Structure = -0.645 (Block-holders) + 0.403(Inst. Investors) + 0.574(M. Ownership) + 0.302 (Foreign Ownership) 4. Analysis By highlighting the key characteristics of the variables, descriptive statistics offer a summary of the data utilized in this study. They draw attention to the dataset's mean, standard deviation, min, and max values, which aid in understanding its variability and distribution. The descriptive statistics for all the variables are shown in the table below. Table 1 Descriptive statistics variables Obs. Mean std. dev. min median max 𝑁𝐶𝑆𝐾𝐸𝑊𝑡+1 3432 0.029 0.075 -0.454 -0.010 0.381 𝐷𝑈𝑉𝑂𝐿𝑡+1 3432 0.357 0.058 -0.554 0.090 1.819 𝐼𝑆𝑡 3432 0.142 0.092 -10.134 0.017 5.994 𝐵𝑜𝑎𝑟𝑑𝑆𝑖𝑧𝑒𝑡 3432 8.74 1.862 6.000 8.000 15.000 𝐵𝐺𝐷𝑡 3432 0.095 0.072 0.000 0.000 0.430 Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 323 𝐵_𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡 3432 0.237 0.147 0.000 0.250 0.750 𝐶𝐸𝑂_𝐷𝑢𝑎𝑙𝑖𝑡𝑦𝑡 3432 0.334 0.1833 0.000 0.000 1.000 𝐵_𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑡 3432 0.202 0.242 0.000 0.064 0.880 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙𝑡 3432 0.0635 0.014 0.000 0.016 0.755 𝑓𝑜𝑟𝑒𝑖𝑔𝑛𝑡 3432 0.066 0.156 0.000 0.002 0.847 𝐵𝑙𝑜𝑐𝑘ℎ𝑜𝑙𝑑𝑒𝑟𝑠𝑡 3432 0.423 0.298 0.000 0.462 0.880 𝑆𝐼𝑍𝐸𝑡 3432 7.037 0.612 5.048 7.143 8.972 𝑅𝑂𝐴𝑡 3432 0.090 0.068 -0.557 0.021 0.604 𝑅𝑂𝐸𝑡 3432 0.215 0.068 -73.883 0.043 12.504 The statistical quarterly data for the main factors are displayed in the above table in the time period of 2010 to 2022 for 66 firms. Therefore, there are 3432 observations of the sample. The correlation matrix for the variables included in our primary regression models is shown in Table 2 Investor sentiment shows a negative correlation with some of the control variables but a positive correlation with two of the proxies of SPCR. Table 2 Pearson and Spearman Correlation matrix variables 𝑁𝐶𝑆𝐾𝐸𝑊𝑡 𝐷𝑈𝑉𝑂𝐿𝑡 𝑆𝐸𝑁𝑇𝑡 𝐵𝑆𝑡 𝑂𝑆𝑡 𝑆𝐼𝑍𝐸𝑡 𝑅𝑂𝐴𝑡 𝑅𝑂𝐸𝑡 𝑁𝐶𝑆𝐾𝐸𝑊𝑡+1 1 𝐷𝑈𝑉𝑂𝐿𝑡+1 0.710*** 1 𝑆𝐸𝑁𝑇𝑡 0.410** 0.817* 1 𝐵𝑆𝑡 -0.030** -0.024* 0.063 1 𝑂𝑆𝑡 -0.018* -0.003* 0.150*** -0.219*** 1 𝑆𝐼𝑍𝐸𝑡 -0.048*** 0.278* -0.127*** 0.088*** 0.021 1 𝑅𝑂𝐴𝑡 0.078*** 0.501* -0.115 -0.029* -0.042* -0.027 1 𝑅𝑂𝐸𝑡 -0.015 0.040* -0.015 -0.012 -0.043* 0.706*** 0.075*** 1 Note: The primary variables' correlation matrix is displayed in Table 2 *, **, and *** stand for significance levels of 10%, 5%, and 1%, respectively. When analyzing time series and panel data, a statistical test called the unit root test is used to identify whether a variable is stationary or non-stationary. The results of unit root test are given below: Table 3 Panel Unit root test result Variables z-stats p-value Decision 𝑁𝐶𝑆𝐾𝐸𝑊𝑡+1 -0.698*** 0.000 Stationary I(0) 𝐷𝑈𝑉𝑂𝐿𝑡+1 -16.478*** 0.000 Stationary I(0) 𝑆𝐸𝑁𝑇𝑡 -3.460*** 0.000 Stationary I(0) 𝐵𝑜𝑎𝑟𝑑𝑆𝑖𝑧𝑒𝑡 -3.099*** 0.001 Stationary I(0) 𝐵𝐺𝐷𝑡 -3.270*** 0.000 Stationary I(0) Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 324 𝐵𝑜𝑎𝑟𝑑𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡 -2.302*** 0.000 Stationary I(0) 𝐶𝐸𝑂𝐷𝑢𝑎𝑙𝑖𝑡𝑦𝑡 -3.903*** 0.000 Stationary I(0) 𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙𝑡 -4.305*** 0.000 Stationary I(0) 𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑡 -2.551** 0.005 Stationary I(0) 𝐵𝑙𝑜𝑐𝑘ℎ𝑜𝑙𝑑𝑒𝑟𝑠𝑡 -4.281*** 0.000 Stationary I(0) 𝑆𝐼𝑍𝐸𝑡 -5.900*** 0.000 Stationary I(0) 𝑅𝑂𝐴𝑡 -4.255*** 0.000 Stationary I(0) 𝑅𝑂𝐸𝑡 -5.469*** 0.000 Stationary I(0) Note: levin, lin and chu test assumes unit root process. ***, **, * denote significance at 1%, 5% and 10%. NSKEW and DUVOL are stationary at 1% significance and negative z values, i.e., -0.698 and -16.478, respectively. The z-stat value of investor sentiment is -3.460 at 1% significance. All the measures of board structure and ownership structure are stationary at 1% significance with negative z stats value. Table 4 presents the findings of pooled OLS regressions and explores the moderating role of board and ownership structure in the relationship between sentiments and crash risk. Table 4 Pooled OLS Regression Analysis Variables 𝐷𝑈𝑉𝑂𝐿𝑡+1 𝑁𝑆𝐾𝐸𝑊𝑡+1 𝑆𝐸𝑁𝑇𝑡 0.073*** (8.911) 0.027** (1.917) 𝐵𝑆𝑡 -0.095* (2.560) -0.002* (-1.794) 𝑂𝑆𝑡 -0.040*** (-6.201) -0.018** (-1.775) 𝑆𝐼𝑍𝐸𝑡 0.162*** (13.521) -0.006*** (-3.011) 𝑅𝑂𝐴𝑡 -0.066 (-0.739) 0.011*** (2.526) 𝑅𝑂𝐸𝑡 0.010*** (5.000) 0.009*** (2.652) 𝐼𝑆𝑡∗𝐵𝑆𝑡 -0.0138*** (-2.339) -0.151* (-1.961) 𝐼𝑆𝑡∗𝑂𝑆𝑡 -0.037*** (-4.015) 0.039** (2.456) Constant 0.046* (1.891) 0.034 (1.421) 𝑅2 0.197 0.156 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑⁡𝑅2 0.187 0.143 Obs. 3432 3432 Pakistan Journal of Social Sciences, Vol. 45(4) 2025, 319-326 325 Note: The standard error categorized by firm and year is the basis for the t-statistic value in parenthesis. ***, **, and * stand for noteworthy values at 1%, 5%, and 10%, respectively. Regardless of whether the crash risk is represented by NSKEW or DUVOL, the calculated coefficients of investor attitudes show positive similarities at the 1% and 5% levels, respectively. The regression results show investor sentiment has a positive impact on DUVOL (0.073, p<0.05) and NSKEW (0.027, p<0.05). Board structure has a negative impact on DUVOL (-0.095, p<0.10) and NSKEW (-0.002, p<0.10). Ownership Structure has a negative impact on DUVOL (-0.095, p<0.10) and NSKEW (-0.002, p<0.10) which means 1% change in ownership structure decreases 0.095 percent DUVOL and 0.002 percent NSKEW. The negative interaction terms confirm board structure and ownership structure moderate the relation between sentiment and crash risk. Control variables have a significant impact on crash risk. The R-square for the model is 0.197 which shows 19.7% variation in DUVOL is caused by moderation and independent variables. The R-square with the second measure of model is 0.156 which shows 15.6% variation in NSKEW is caused by the concerned variables. 5. Conclusion According to the study's findings, investor sentiment dramatically raises the SPCR, corroborating the idea that overconfidence and speculative trading cause the market to become overvalued and then experience abrupt corrections. Wu et al. (2017) supports this finding in their study. Alnafea & chebbi (2021) also confirmed in their study that sentiments deviate the stock prices from fundamentals and causes the crash risk. Conversely, strong corporate governance (board and ownership structure) helps in reducing crash risk. Additionally, the findings support the moderating link of governance in sentiment-driven crashes. The study used the secondary data and non-financial KSE100 companies from 2010Q1-2022Q2. Further studies should include the qualitative characteristics of governance and the financial firms. References Alam, A., & Ali Shah, S. Z. (2013). Corporate governance and its impact on firm risk. 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Her research is focussed on stock market. https://orcid.org/0000-0001-6423-6627 Muhammad Abbas is an Assistant Professor at Department of Management Sciences, Air University, Multan Campus.