CEO power and stock price crash risk in India: The moderating effect of insider trades

Kalia, Ankita Article CEO power and stock price crash risk in India: The moderating effect of insider trades Asian Journal of Economics and Banking (AJEB) Provided in Cooperation with: Ho Chi Minh University of Banking (HUB), Ho Chi Minh City Suggested Citation: Kalia, Ankita (2024) : CEO power and stock price crash risk in India: The moderating effect of insider trades, Asian Journal of Economics and Banking (AJEB), ISSN 2633-7991, Emerald, Leeds, Vol. 8, Iss. 2, pp. 169-198, https://doi.org/10.1108/AJEB-10-2023-0095 This Version is available at: https://hdl.handle.net/10419/334120 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ CEO power and stock price crash risk in India: the moderating effect of insider trades Ankita Kalia University Business School, Panjab University, Chandigarh, India Abstract Purpose –This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk. Design/methodology/approach –A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results’reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised. Findings –Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions. Originality/value –This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon. Keywords CEO power, Corporate governance, Stock price crash risk, Insider trades, India Paper type Research paper 1. Introduction Corporate governance mechanisms have evolved into a crucial element in advancing the welfare of diverse stakeholders. According to Kumar and Singh (2013), corporate governance holds a central position in guiding organisations through financial crises with success. Moreover, the presence of strong governance mechanisms can adeptly address conflicts of interest within organisations, thereby bolstering overall operational efficiency at the corporate level (Sami et al., 2011). It is worth noting that the risk of stock price crashes is intimately intertwined with these agency problems, as highlighted by Jin and Myers (2006). As such, the comprehensive analysis by Wu et al. (2020) regarding the pivotal role of CEO power and stock price crash risk in India 169 © Ankita Kalia. Published in Asian Journal of Economics and Banking. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ legalcode The author would like to thank Dr. Nguyen Ngoc Thach (Editor-in-Chief) and three anonymous reviewers for their helpful and constructive comments. Funding: The author declares that no funds, grants or other support were received during the preparation of this manuscript. Competing interests: The author has no relevant financial or non-financial interests to disclose. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2615-9821.htm Received 3 October 2023 Revised 25 October 2023 7 December 2023 Accepted 19 December 2023 Asian Journal of Economics and Banking Vol. 8 No. 2, 2024 pp. 169-198 Emerald Publishing Limited e-ISSN: 2633-7991 p-ISSN: 2615-9821 DOI 10.1108/AJEB-10-2023-0095 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 corporate governance in effectively mitigating the risk of stock price crashes holds significant importance. A multitude of factors can set off fluctuations in a company’s stock price (Cutler et al., 1989). However, the primary emphasis within the literature on crash risk centres on the agency framework (Jin and Myers, 2006). Within this framework, researchers assert that stock price crash risk primarily emanates from the unequal distribution of information between the company’s management and external stakeholders. This information asymmetry is directly correlated with the degree of control held by the management. According to the agency theory framework, the presence of such informational disparity provides managers with the means to withhold adverse company-specific information from investors to serve their personal interests (Graham et al., 2005;Kothari et al., 2009). Nevertheless, the strategy of concealing negative news possesses inherent constraints. When the accumulated adverse information surpasses a certain threshold, self-interested managers are compelled to disclose the concealed information all at once. This sudden revelation triggers a substantial decline in the company’s stock prices, a phenomenon extensively documented by Jin and Myers (2006) and Hutton et al. (2009). Usually, a company has command of officers; despite that, only a few are principally responsible for the company’soperations(Thompson, 1967). The chief executive officer (CEO) is often regarded as the most influential organisational member (Pearce, 1981;Hambrick and Fukutomi, 1991). Hence, most research works revolve around the company’sCEO(Norburn, 1989). The literature identifies various origins of CEO influence within an organisation. The seminal research by Finkelstein (1992) proposes four heads, namely structural, ownership, prestigeandexpert,as significant sources of CEO power. The structuraldimension represents a CEO’s hierarchical authority from the company’s organisational structure (Hambrick, 1981; Brass, 1984). This form of power is considered one of the most dominant and influential sources (Finkelstein, 1992). CEO duality (C_DUAL) is one of the constituents of structural power that represents where the CEO and the board chairperson are the same individuals (Mizruchi, 1983; Harrison et al.,1988;Finkelstein and D’aveni, 1994). CEO compensation (C_COMP)and directorship (C_DIRECTOR) are other examples of structural power (Molz, 1988;Lippert and Porter, 1997;Bonn and Pettigrew, 2009). On the other hand, ownership power reflects the CEO’s ownership stake in the company. CEOs who are founders or close relatives of the company’s founder (C_STATUS) have significant influence through their ownership power. This influence enables them to shape the organisation, exert control over director selection and safeguard their position within the company (Boeker, 1989;Fredrickson et al., 1988;Pfeffer, 1981). The third dimension, prestige power, arises from a CEO’s board service in other companies or attainment of qualifications from prestigious educational institutions (Finkelstein, 1992). Finally, expert power denotes the CEO’s ability to navigate and manage the organisation’s external environment (Hambrick, 1981). A significant source of expert power is CEO tenure (C_TENURE), as it implies that individuals who serve as a CEO for longer periods accumulate more power. CEOs with longer tenures draw on their deep knowledge of the company’s operational landscape, including industry dynamics, supply chain intricacies and market conditions (Firstenberg and Malkiel, 1994;Greve and Mitsuhashi, 2007). The existing literature establishes a strong link between managers’ability to conceal adverse, company-specific information and the extent of power they wield within an organisation. Feng et al.(2011)and Friedman (2014) reveal that influential CEOs pressure chief financial officers (CFOs) to manipulate accounting practices and present performance results in their favour. Moreover, Fracassi and Tate (2012) find that powerful CEOs face less scrutiny from boards of directors, as they often play a pivotal role in appointing directors and maintaining close relationships with them. Managers also use strategies such as earnings management and tax avoidance to conceal negative information, which significantly increases AJEB 8,2 170 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 the risk of a stock price crash (Hutton et al., 2009;Kim et al., 2011). Despite the limited research on the association between CEO power and stock price crash risk, the insights offered by their results are imperative for the current study. For instance, Shahab et al. (2020) assessed the relationship between CEO power and stock price crash risk in thecontext of Chinese companies and found that higher CEO power is associated with an increased risk of stock price crashes. Similarly, Al Mamun et al. (2020) also found a positive and significant association between powerful CEOs and stock price crash risk in the American context. The researchers also contemplated that the presence of robust external mechanisms weakened the examined relationship, but could not eliminate the effect of CEO power on crash risk. Since the relationship between CEO power and stock price crash risk is similar across developed and developing economies, thus it is expected to observe similar results in India. Another perspective on this matter suggests that managers do not always find it advantageous to keep bad news hidden. Upholding this notion some research studies indicate that managers may actively choose to disclose negative information sooner to reduce the risk of litigation (Skinner, 1994;Kasznik and Lev, 1995). Additionally, Hermalin and Wesibach (2001) argue that when managers gain power through their superior abilities, they have little incentive to withhold negative news. Founder CEOs, in particular, often aim to pursue strategies that maximise shareholder value, resulting in optimal stock market performance (Fahlenbrach, 2009). This tendency is particularly prominent in family-owned companies, where family members take on management, board and investor roles. In various roles and responsibilities, family members within a company often lack motivation to manipulate stock prices by hiding unfavourable information (Srinidhi and Liao, 2020). When family founders take on managerial roles, their personal identity becomes deeply intertwined with the company’sidentity(Dyer and Whetten, 2006). Graham et al. (2017) have shown that powerful CEOs, who feel secure in their positions despite poor financial performance, tend to promote greater corporate transparency and have fewer reasons to conceal bad news (Jiraporn et al., 2014). Together, these arguments from previous research suggest that companies led by powerful CEOs may face a reduced risk of stock price crashes, as these CEOs have minimal incentives to withhold negative company-specific news. Beyond investigating the connection between CEO power and stock price crash risk in India, this study extends to explore how insider trades moderate this relationship. It has been ascertained that insiders, primarily from the managerial group with significant ownership stakes, tend to trade based on advance knowledge of firm-specific negative news, potentially disadvantaging uninformed outside investors (Ke et al., 2003;Dechow et al., 2016). Consequently, it is anticipated that insider trades influence both CEO power and stock price crash risk. The present study is a novel attempt to analyse the nexus between CEO power, insider trades and stock price crash risk in India using a dataset comprising companies listed on the S&P BSE 500 Index in India for the period spanning from 2014 to 2023. As far as author’s knowledge extends, there is no similar study in India that has delved into this specific domain. Furthermore, the statistics underscore the fact that retail participation in India’s stock markets is at an early stage of development. As of now, only approximately three percent of India’s population has ventured into the stock markets, with the majority of investors continuing to lean towards safer investment options like bank deposits (Balwani et al., 2021). This highlights the vulnerability of investor confidence in the face of a substantial stock price decline, making it imperative to thoroughly investigate the research issue at hand. Moreover, while there are multiple factors that may contribute to an elevated risk of stock price crashes, the paramount position of the bad news hoarding theory in the study of crash risk and its association with self-serving managerial conduct underscore the significance of exploring the influence of CEO power in this context. The investigation employs various indicators of CEO power, including C_COMP,C_ DUAL,C_STATUS,C_TENURE and C_DIRECTOR to elucidate the impact of CEO power CEO power and stock price crash risk in India 171 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 on the future stock price crash risk. To empirically gauge stock price crash risk, the study employs two proxies viz., negative conditional skewness (NCSKEW tþ1 ) and down-to-up volatility (DUVOL tþ1 ), both of which are measured for a future time period tþ1. The study’s findings conclude that greater CEO power is associated with a reduction in stock price crash risk over the subsequent year. These results carry statistical significance, particularly with respect to C_DUAL,C_STATUS and C_DIRECTOR as the chosen proxies for CEO power. Furthermore, these results withstand scrutiny when alternative methodologies and stock price crash risk measure are considered. Importantly, the findings remain robust even after accounting for potential endogeneity concerns. Additionally, moderation analysis reveals that insider trades moderate the relationship between select dimensions of CEO power (C_ DUAL,C_STATUS and C_DIRECTOR) and stock price crash risk, providing crucial insights in this area. The rest of the paper is organised as follows. Section 2 discusses the review of literature and hypotheses development. Section 3 outlines the research methodology, while section 4 presents the results and discussions emerging from the study. Lastly, Section 5 concludes the paper. 2. Review of literature and hypotheses development 2.1 CEO power and stock price crash risk The foundation of stock price crash risk is primarily grounded in the perspective of bad news hoarding theory (Jin and Myers, 2006). This theory is based on the influential role of CEO power within top management. According to agency theorists, there are two key conditions that can exacerbate the risk of a stock price crash. The first condition involves management selectively withholding unfavourable news, while the second pertains to the eventual release of this news all at once (Hutton et al., 2009;Kothari et al., 2009). The selective hoarding of bad news by powerful managers is often driven by motives such as the desire to increase personal wealth (Andreou et al., 2016) or concerns related to their own careers (Baginski et al., 2018). When powerful managers are unable to continue withholding bad news beyond a certain threshold, it results in a one-time release of this information, which, in turn, leads to a significant decline in stock prices (Jin and Myers, 2006). In contrast, proponents of stewardship theory challenge the conclusions drawn by agency theory. Stewardship theory posits that managers can be seen as trustworthy stewards of the company who find intrinsic satisfaction in tackling challenging tasks (Donaldson, 1990). Consequently, it is less likely that the presence of powerful managers in corporate management will lead to an increased risk of stock price crashes. Moreover, greater CEO power has been associated with improved decision-making speed, which can be beneficial for overall organisational development (Donaldson and Davis, 1991). Therefore, in uncertain situations where stock prices experience significant fluctuations, powerful management can utilise their quick decision-making abilities to respond effectively and mitigate stock price crashes (Tan and Liu, 2016). Despite the various perspectives found in the literature regarding the influence of CEO power on a company, the predominant view in mainstream research leans towards a negative assessment. Powerful managers have been consistently shown to pursue objectives that do not align with the goal of maximising shareholder wealth (Daily and Johnson, 1997). Additionally, the presence of CEOs hailing from the founding family have also been associated with unfavourable corporate outcomes due to the potential lack of entrepreneurial talent (Morck et al., 1988;Adams et al., 2005). Furthermore, several issues, including the exertion of excessive influence on board members to secure extraordinary managerial compensation packages, the increased likelihood of corporate fraud and engagement in accounting manipulation, have all been linked to the presence of powerful top management AJEB 8,2 172 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 (Grabke-Rundell and Gomez-Mejia, 2002;Bebchuk and Fried, 2004;Grinstein and Hribar, 2004;Feng et al., 2011;Morse et al., 2011;Khanna et al., 2015). As a result, the existence of opportunistic managerial behaviour has been linked with a positive impact on the risk of stock price crashes (Al Mamun et al., 2020;Shahab et al., 2020). A CEO can accumulate power through various means, including their corporate ownership stake, structural position within the organisation, prestige, or by showcasing their expertise (Finkelstein, 1992;Daily and Johnson, 1997). As a result, research studies have adopted multifaceted frameworks to explore the connection between different sources of managerial power and their relationship with stock price crash risk. First and foremost, C_ COMP has been extensively studied as a dimension of CEO power, and a significant portion of research has identified a notable and positive influence of CEO pay on stock price crash risk (Xu et al., 2014;Cui et al., 2019;Shahab et al., 2020). In a similar vein, Andreou et al. (2016) have also found a significant positive impact of CEO compensation on stock price crash risk, particularly for younger CEOs. Their research indicates that young CEOs engage in significant bad news hoarding to enhance their immediate financial incentives with the expectation that their current poor performance will be offset by strong future performance. On the other hand, a study by Kim et al. (2011) took a unique approach by examining the role of financial incentives for the CFO instead of the CEO. The study concluded that equity incentives encourage managers, including CFOs, to engage in bad news hoarding, ultimately leading to a stock price crash. Notably, the role of financial incentives received by CFOs appears to be more influential in such situations compared to those received by CEOs. Lastly, Xu and Zou (2019) presented an inconsistent finding as their research could not discern a significant association between the CEO’s share of pay and stock price crash risk. In summary, the following hypothesis has been formulated based on these diverse research findings. H1. CEO compensation is positively related to future stock price crash risk. Furthermore, another significant dimension of CEO power, C_DUAL has gained traction in the literature. Srinidhi and Liao (2020) found substantial evidence to suggest that C_DUAL enhances managerial incentives, which in turn may lead to the artificial inflation of performance and an increased risk of a stock price crash among companies listed in the United States. Similarly, Le et al. (2022) made similar observations, as they identified a significant positive impact of CEO duality on stock price crash risk. Additionally, Tran et al. (2023) proposed that the risk of a stock price crash increases significantly among Vietnamese family-owned companies with C_DUAL compared to other family companies without it. This suggests that in such cases, it becomes exceedingly challenging for the board of directors to remove powerful CEOs from the company, even when they may be inefficient. However, it is worth noting that contrasting perspectives have also emerged in the literature. Hunjra et al. (2020) reached a different conclusion, suggesting that C_DUAL may have more favourable effects than adverse consequences, as it could signify better knowledge and expertise on the part of the CEO. Considering the extant literature, the following hypothesis has been formulated. H2. CEO duality is positively related to future stock price crash risk. CEOs can be classified into two broad categories viz., founders or their relatives (referred to as family CEOs), and those recruited externally (considered as agent CEOs). Family CEOs typically have long-standing relationships with board members and other key stakeholders, which often translates into greater power (Finkelstein, 1992). Additionally, family CEOs and agent CEOs have different outlook and behaviours. Family CEOs tend to engage in riskier ventures because they are less concerned about being fired (Dalton and Daily, 2001). Agent CEOs, on the other hand, may not face potential capital losses, but they often worry about job security CEO power and stock price crash risk in India 173 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 (Fahlenbrach, 2009). Based on these arguments, one might reasonably expect agent CEOs to more likely engage in bad news hoarding to protect their careers. This suggests that C_STATUS may lower stock price crash risk. Supporting this view, Yang et al. (2023a,b) found that family CEOs strengthen the negative relationship between corporate social responsibility (CSR) and stock price crash risk, since family firms experience lower Type I agency problems, which limits family managers from pursuing self-interested goals. Similarly, Tran et al. (2023) suggests that higher corporate ownership among CEOs makes them more risk-averse and leads them to engage in activities that reduce shareholder risk, thereby avoiding bad news hoarding. However, Long et al. (2020) and Al Mamun et al. (2020) found conflicting results, suggesting that even powerful managers, including family CEOs, can engage in bad news hoarding. Based on the majority of the literature, the following hypothesis has been formulated. H3. CEO status is negatively related to future stock price crash risk. C_TENURE carries significant implications for organisational performance (Hambrick and Fukutomi, 1991). The economic outcomes associated with C_TENURE can vary, resulting in either positive (Wu et al., 2005) or negative (Miller, 1991) consequences for a firm. However, when focussing on the context of stock price crash risk, the present study aligns with empirical evidence that primarily supports the positive impact of C_TENURE (Cui et al., 2019;Al Mamun et al., 2020). CEOs s with longer tenures may become excessively entrenched in their perspectives of the firm, rendering them less adaptable to changes in the external environment (Levinthal and March, 1993). Therefore, based on the aforementioned explanation following hypothesis has been formulated. H4. CEO tenure is positively related to future stock price crash risk. Notably, not all attributes of CEO power have received considerable attention in the crash risk literature, and one such attribute is C_DIRECTOR, which has been largely overlooked. From the agency perspective, it is argued that to mitigate agency problems, the roles of the CEO and the chairman of the board should be separate (Fama and Jensen, 1983). Voordeckers et al. (2007) have asserted that the CEO’s presence on the board serves as a source of CEO power and may potentially weaken the board’s control. Thus, accordingly the following hypothesis has been formulated. H5. CEO directorship is positively related to future stock price crash risk. 2.2 Moderating effect of insider trades on CEO power and stock price crash risk The consequences of insider trades are not only linked with CEO power but also with the stock price crash risk (Jaffe, 1974;Elliott et al., 1984;Fernandes and Ferreira, 2009;Kothari et al., 2009). Several research studies have illustrated that powerful insiders usually indicated by CEOs or CFOs tend to incur insider sales prior to financial difficulties. For instance, higher insider sales have been witnessed prior to events such as filing of bankruptcy petition, dividend announcements linked with lower growth opportunities, public declaration of material internal control weaknesses or accounting irregularities (John and Lang, 1991; Seyhun and Bradley, 1997;Beneish, 1999;Johnson et al., 2009;Thevenot, 2012;Skaife et al., 2013;Agrawal and Cooper, 2015). Although the literature acknowledges the presence of studies examining the impact of CEO power and insider trades on stock price crash risk (e.g. Shahab et al., 2020;He et al., 2021), however the moderating effect of insider trades on the relationship between CEO power and stock price crash risk remains unexplored. Accordingly, driven by the outcomes of related literature the following hypothesis is proposed. H6. Insider trades moderate the association between CEO power and future stock price crash risk. AJEB 8,2 174 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 3. Research methodology 3.1 Sample selection and data sources The study is based on a sample of companies listed on the S&P BSE 500 Index, covering a ten-year period from April 1, 2013, to March 31, 2023. The sample was meticulously refined through a series of data filtering conditions, resulting in a final count of 236 companies, representing 2,360 firm-year observations. First and foremost, all banking and financial services companies were excluded from the sample due to their unique regulatory oversight under laws including the RBI Act, 1934 and the Banking Regulation Act, 1949. Secondly, public sector undertakings were also omitted from the sample, given their distinct social obligations. Thirdly, companies with fiscal year-ends other than March 31 were excluded from the final dataset. Fourthly, companies that were not consistently part of the Index throughout the study period were also eliminated. Lastly, companies undergoing corporate restructuring during the study period were likewise removed from consideration. Data for the variables under investigation in this study has been collected from diverse sources. Specifically, proxies for CEO power have been obtained from the annual reports of the respective companies. In contrast, data related to stock price crash risk and various control variables have been sourced from the ProwessIQ database, which is meticulously maintained by the Centre for Monitoring Indian Economy (CMIE). Specifically, the data relating to the additional control variable in the robustness section, namely analyst coverage has been procured from the website of Trendlyne (https://trendlyne.com/). Lastly, the data concerning the moderating variable insider trades has been incorporated from the official website of BSE (https://bseindia.com/). 3.2 Variable measurement 3.2.1 Stock price crash risk. Building on the groundwork laid by prior research (Kim et al., 2011;Xu et al., 2014;Al Mamun et al., 2020;Shahab et al., 2020), this study employed two proxies, viz., NCSKEW tþ1 and DUVOL tþ1 to gauge stock price crash risk. These variables are measured in the time period tþ1, reflecting future stock price crash risk. To commence, the study adopts the approach proposed by Fama and MacBeth (1973) for calculating firmspecific weekly returns (W i,t ). This calculation method involves estimating W i,t as the natural logarithm of one plus the residual returns derived from the market model outlined in Equation (1). ri;t¼ α iþβ1;irm;t−2þβ2;irm;t−1þβ3;irm;tþβ4;irm;tþ1þβ5;irm;tþ2þ ε i;t(1) where r i,t signifies the return of stock ion trading day t, and r m,t denotes the return on the S&P BSE 500 index on the same trading day t.NCSKEW tþ1 is derived by computing the negative of the third moment of abnormal weekly returns divided by the standard deviation of abnormal weekly returns raised to the third power (Chen et al., 2001). A greater value for NCSKEW tþ1 signifies a heightened level of stock price crash risk. Equation (2) provides the mathematical formula for its calculation: NCSKEWi;tþ1¼−nðn1Þ 3 =2Pt¼1Wi;t;Wi;t3 ðn1Þðn2ÞPt¼1ðWi;t;Wi;t2 3 = 2 (2) where, W i,t 5firm-specific weekly return in the fiscal year, n5number of observations in the year t. DUVOL tþ1 is the natural logarithm of the ratio of the standard deviation of the “down” weeks and the “up”weeks (Chen et al., 2001). “Down”weeks are when the returns are lower than the annual mean, while “up”weeks are those when the returns are more than the annual CEO power and stock price crash risk in India 175 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 mean. A higher value of DUVOL tþ1 indicates a greater risk of a stock price crash. The formula for calculating DUVOL tþ1 is highlighted through Equation (3). DUVOLi;tþ1¼lognðnu–1ÞƩdown w2 i;t.ðnd1ÞƩup w2 i;to(3) where, n u 5number of “up”weeks in a year t, n d 5number of “down”weeks in a year t, W i,t 5firm-specific weekly returns in the fiscal year. 3.2.2 Measures of CEO power. CEO power is a multi-dimensional concept, as acknowledged in previous research (Finkelstein, 1992). Building upon this understanding, Peni (2014) notes that CEOs occupy a pivotal position as the most influential executives within a company. Consequently, this study defines the diverse attributes of managerial power associated with a CEO. Drawing upon the various sources of CEO power identified by Finkelstein (1992), this study employs five distinct measures. Firstly, the study employs C_COMP, representing an aspect of structural power. It encompasses the monetary value of total compensation, including salary, perquisites, commission, bonuses and similar components, paid to a CEO in the current financial year (Parthasarathy et al., 2006). Another aspect of structural power, C_DUAL, is also examined. C_DUAL arises when a single individual simultaneously holds the positions of chairman of the board and CEO within a company (Herman, 1981;Firstenberg and Malkiel, 1994). This binary variable takes the value of one when the chairman of the board and the CEO roles are held by the same person and zero otherwise. Additionally, C_STATUS represents another crucial aspect of structural power intertwined with ownership stakes (Daily and Johnson, 1997). This variable is also a binary measure, that takes the value of one if the CEO is the founder of the company or a relative of the founder and zero otherwise (Jayaraman et al., 2000;Adams and Ferreira, 2009). C_TENURE serves as another proxy for CEO power, falling within the dimension representing the CEO’s expertise. It quantifies the CEO’s length of service in years within the company (Simsek, 2007;Lewellyn and Muller-Kahle, 2012). Finally, C_DIRECTOR constitutes another dimension of CEO power within the structural category. It is an indicator variable that takes the value of one if the current CEO also holds a position on the board of the company and zero otherwise (Tien et al., 2013;Ting and Huang, 2018). As recognised by Finkelstein (1992), the structural source of CEO power is considered one of the most predominant and influential sources of power. Therefore, this study places substantial emphasis on this dimension to define CEO power. 3.2.3 Insider trades. In compliance with India’s stock market regulator’s SEBI (Prohibition of Insider Trading) Regulations, 2015, insiders, classified as connected persons or those in possession of unpublished price-sensitive information, are mandated to disclose share transactions exceeding Rs. 10 lakhs within two trading days each quarter. These disclosures are submitted to both the respective companies and the stock exchanges where the stocks are listed. Information on this variable is sourced from the official BSE website (https://bseindia. com/). The variable is dichotomously measured, taking the value of one for insider trades within a financial year and zero otherwise, aligning with the approach used by Hasnan et al. (2022). 3.2.4 Control variables. To ensure a comprehensive understanding of the impact of CEO power on future stock price crash risk and to account for individual company characteristics, this analysis incorporates several control variables. This approach aligns with established research practices (Kim et al., 2011;Xu et al., 2014;Yeung and Lento, 2018;Al Mamun et al., 2020;Shahab et al., 2020;Srinidhi and Liao, 2020). Firstly, the control variable SIZE reflects the size of the company and is measured by taking the natural logarithm of the market value of the firm’s equity, consistent with prior AJEB 8,2 176 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 coefficients depicted through panel data fixed-effects regression models was higher than the baseline model. Additionally, other CEO power sources, C_COMP {NCSKEW tþ1 (β52.37, p>0.05, DUVOL tþ1 (β53.17, p>0.05)} and C_TENURE {NCSKEW tþ1 (β50.09, p>0.05, DUVOL tþ1 (β50.03 and p>0.05)}, also presented results in line with baseline section. Thus, the observation that higher CEO power leads to lower future stock price crash risk in India is maintained. 4.3.1.2 Bayesian regression. Beyond employing traditional frequentist methodological approaches, this study extends its analysis by incorporating Bayesian methods rooted in Bayes’ theorem of probability (Bayes, 1763). In this approach, aprioribeliefs about the association between CEO power and stock price crash risk are formulated. These beliefs are then combined with assumptions about the likelihood of observing the given data, forming a posterior distribution. The observed data serve to update andrefine the initial beliefs, providing a nuanced perspective on the relationship between the variables under investigation. Contrastingly, classical null hypothesis significance testing has faced criticism for its disproportionate emphasis on statistical significance to meet publication standards (Trafimow and Earp, 2017). As an alternative, Bayesian analysis has gained prominence in the social sciences (e.g. Hansen et al., 2004;Block and Wagner, 2014;Cerqueti and Ventura, 2015;Kwon et al., 2016;Segnon et al., 2018;Jiang and Liu, 2020), offering a more nuanced perspective. However, Bayesian analysis introduces challenges, particularly in the selection of prior distributions. Recognising the subjectivity associated with informative priors, this study aligns with the approach advocated by Jiang and Liu (2020), opting for the more cautious choice of non-informative prior distributions. Additionally, to explore the posterior distribution of parameters, the study employs Markov Chain Monte Carlo (MCMC) simulation (Gelman and Rubin, 1992), drawing a total of 11,000 samples and discarding the initial 1,000 to ensure convergence and reliability in the analysis. Table 5 reports the regression results from the Bayesian analysis. The findings were similar in terms of directional impact of various dimensions of CEO power on future stock Variable NCSKEW tþ1 DUVOL tþ1 Posterior mean 95% C.I. Posterior mean 95% C.I. C_COMP 5.17 [8.47, 1.88] 1.09 [1.23, 3.89] C_DUAL 0.06 [0.13, 0.01] 0.00 [0.02, 0.01] C_STATUS 0.06 [0.13, 0.01] 0.00 [0.01, 0.01] C_TENURE 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] C_DIRECTOR 0.27 [0.39, 0.14] 0.02 [0.04, 0.00] DTURNOVER 0.00 [0.00, 5.21] 2.63 [3.34, 8.59] RET 16.90 [12.12, 21.68] 2.77 [1.86, 3.69] M/B 0.00 [0.01, 0.00] 0.00 [0.00, 0.00] SIZE 0.04 [0.02, 0.06] 0.01 [0.00, 0.01] LEV 0.88 [0.66, 1.09] 0.01 [0.05, 0.03] Constant 1.49 [1.99, 0.99] 0.21 [0.31, 0.12] N 2,360 2,360 2,360 2,360 Note(s): C_COMP 5CEO compensation; C_DUAL 5CEO duality; C_STATUS 5CEO status; C_ TENURE 5CEO tenure; C_DIRECTOR 5CEO directorship; NCSKEW tþ1 5negative conditional skewness measured at tþ1;DUVOL tþ1 5down-to-up-volatility measured at tþ1; DTURNOVER 5de-trended turnover; RET 5company-specific weekly returns; M/B 5market to book value of equity; SIZE 5size of company measured by taking the natural log of market capitalisation; LEV 5leverage. Bayesian regression methodology based on the Markov Chain Monte Carlo (MCMC) simulation has been applied to obtain the said test results. Total number of draws 11,000 (of which 1,000 discarded) Source(s): Author’s calculations based on using Stata 14 Table 5. Robustness resultsalternative methodology (Bayesian regression) CEO power and stock price crash risk in India 183 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 price crash risk. To illustrate, majority of the CEO power dimensions, viz., C_COMP,C_ DUAL,C_STATUS and C_DIRECTOR were found to negatively impact stock price crash risk measured by NCSKEW tþ1 and DUVOL tþ1. Only C_TENURE was found to exhibit a positive impact on the crash risk measures. The results concerning the control variables also remained in line with the baseline frequentist results. 4.3.2 Alternative measure of stock price crash risk. The robustness of the test results has been further assessed by incorporating an alternative measure of stock price crash risk. In consistence with previous research studies (e.g. Kim et al., 2011;Cui et al., 2019;Xu and Zou, 2019;Al Mamun et al., 2020;Srinidhi and Liao, 2020) a binary CRASH variable has been employed as an alternative measure of stock price crash risk to assess the relationship between CEO power and future stock price crash risk. The CRASH indicator variable takes the value of one if a firm has encountered one or more crash weeks during a fiscal year and zero otherwise. Crash weeks are identified as those weeks in which a firm’s firm-specific weekly returns (W i,t ) fall 3.2 standard deviations below the mean firm-specific weekly returns over the entire fiscal year. This alternative measure allows for a binary assessment of whether a firm experienced a stock price crash during the year, providing an additional perspective on the relationship between CEO power and stock price crash risk. The binary logistic regression model gauges the effect of CEO power on future stock price crash risk. Table 6 presents the results relating to the CRASH binary variable. The results upheld that there is a significant negative impact of C_DUAL (β50.15, p<0.01),C_ STATUS (β50.33, p<0.01) and C_DIRECTOR (β50.36, p<0.10) on future stock price crash risk. Furthermore, the association of C_COMP and C_TENURE remained insignificantly negative and positive respectively with respect to crash risk. 4.3.3 Additional control variables. This section delves into an examination of the baseline regression results, scrutinising their sensitivity subsequent to the inclusion of two Variable CRASH tþ1 C_COMP 2.08 (2.34) C_DUAL 0.15*** (2.09) C_STATUS 0.33*** (4.09) C_TENURE 0.07 (10.7) C_DIRECTOR 0.36* (2.18) DTURNOVER 0.01*** (8.30) RET 92.22*** (3.49) M/B 0.46*** (5.40) SIZE 0.02*** (5.93) LEV 0.29*** (2.76) Constant 0.21* (0.16) N 2,360 R 2 0.68 Year effects Yes Industry effects Yes Note(s): C_COMP 5CEO compensation; C_DUAL 5CEO duality; C_STATUS 5CEO status; C_ TENURE 5CEO tenure; C_DIRECTOR 5CEO directorship; CRASH tþ1 5binary variable crash measured at tþ1; DTURNOVER 5de-trended turnover; RET 5company-specific weekly returns; M/B 5market to book value of equity; SIZE 5size of company measured by taking the natural log of market capitalisation; LEV 5leverage. ***, ** * indicates the level of significance at 1%, 5% and 10% respectively. t-statistics reported in the parentheses are based on robust standard errors. Binary logistic regression methodology has been applied to obtain the said test results Source(s): Author’s calculations based on using Stata 14 Table 6. Robustness resultsalternative stock price crash risk measure AJEB 8,2 184 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 supplementary control variables, namely, financial constraints (FC) and analyst coverage (AC). FC, encapsulates the financial predicament of a firm hindering it from financing its intended investments (Lamont et al., 2001). Consequently, such firms are anticipated to resort to external sources for fulfilling their funding needs. In the pursuit of external financing, financially constrained firms tend to withhold more bad news, thereby encountering an escalated risk of future stock price crashes (He and Ren, 2023). The measurement of FC commonly relies on cash flow sensitivity, proxied by the ratio of a firm’s net cash flow from operating activities to total assets (Srinivasan and Thampy, 2017). On the other hand, numerous research studies have identified a correlation between the influence of analyst coverage (AC) and the risk of stock price crashes (e.g. Xu et al., 2013;He et al., 2019;Kim et al., 2019;Yang et al., 2023a,b). The foundation of literature on stock price crash risk revolves around the concept of managerial behaviour characterised by the hoarding of negative news, driven by factors such as informational opacity and agency conflicts (Jin and Myers, 2006;Hutton et al., 2009). In this context, financial analysts or brokerages play a crucial role as information intermediaries. It is posited that they can mitigate the tendency of managers to hoard bad news, consequently reducing the risk of a stock price crash. Analysts, with their sophisticated ability to acquire and process firm-specific information, are wellpositioned to timely identify and communicate negative developments through brokerage reports or media coverage. This proactive communication is believed to contribute to a diminished stock price crash risk. AC is operationalised as a dummy variable, taking the value of one if a firm is covered by any financial analyst and zero otherwise. The pooled OLS regression results, detailed in Table 7, align closely with the baseline findings. The outcomes consistently affirmed the significantly negative association of three Variable NCSKEW tþ1 DUVOL tþ1 C_COMP 3.73** (1.66) 5.48* (3.25) C_DUAL 0.06* (0.03) 0.04* (0.00) C_STATUS 0.06* (0.04) 0.02* (0.01) C_TENURE 0.00 (0.01) 0.00 (0.01) C_DIRECTOR 0.22*** (0.06) 0.03** (0.02) DTURNOVER 0.00* (0.00) 0.00 (0.00) RET 18.59*** (2.47) 3.01*** (0.48) M/B 0.08*** (0.00) 0.00* (0.09) SIZE 0.05*** (0.01) 0.01*** (0.00) LEV 0.72*** (0.11) 0.02* (0.00) FC 1.38*** (0.17) 0.06* (0.03) AC 0.08** (0.03) 0.02*** (0.01) Constant 1.63*** (0.25) 0.19*** (0.05) N 2,360 2,360 R 2 0.63 0.76 Year effects Yes Yes Industry effects Yes Yes Note(s): C_COMP 5CEO compensation; C_DUAL 5CEO duality; C_STATUS 5CEO status; C_ TENURE 5CEO tenure; C_DIRECTOR 5CEO directorship; NCSKEW tþ1 5negative conditional skewness measured at tþ1;DUVOL tþ1 5down-to-up-volatility measured at tþ1; DTURNOVER 5de-trended turnover; RET 5company-specific weekly returns; M/B 5market to book value of equity; SIZE 5size of company measured by taking the natural log of market capitalisation; LEV 5leverage; FC 5financial constraints; AC 5analyst coverage. ***, ** * indicates the level of significance at 1, 5 and 10%, respectively. t-statistics reported in the parentheses are based on robust standard errors. Pooled OLS regression methodology has been applied to obtain the said test results Source(s): Author’s calculations based on using Stata 14 Table 7. Robustness resultsadditional control variables CEO power and stock price crash risk in India 185 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 CEO power metrics—namely, C_DUAL,C_STATUS and C_DIRECTOR and stock price crash risk proxied by NCSKEW tþ1 and DUVOL tþ1 . Additionally, it was observed that C_ COMP exerted a significant and negative influence on future stock price crash risk. Notably, the impact of C_TENURE on crash risk remained statistically insignificant and positive. Turning attention to the newly incorporated control variables, the analysis revealed that financially constrained firms exhibited an elevated risk of stock price crashes. Furthermore, companies with stocks covered by analysts demonstrated a mitigated stock price crash risk. This broader set of variables not only fortified the robustness of the findings but also provided fresh insights into the nuanced dimensions of crash risk. 4.3.4 Addressing endogeneity concerns. To account for potential endogeneity in the relationship between CEO power and future stock price crash risk, this study employs the instrumental variables with the two-stage least squares (IV-2SLS) and difference-indifference (DiD) methodologies. For the IV-2SLS technique, this study in line with previous research studies (Xu et al., 2014;Yeung and Lento, 2018) employs industry averages of the various proxies of CEO power as the instrumental variables. The instrumental variables are selected based on the belief that they are correlated with the subsequent CEO power proxy of the relevant company, satisfying the selection criteria for instruments. Simultaneously, these industry averages are expected not to be associated with the relevant company’s future stock price crash risk, satisfying the exclusion criteria for the instruments. Equation (6) outlines the regression model conducted for the first stage of the IV-2SLS regression, which is designed to estimate the relationship between the instrumental variables and CEO power: CEOPoweri;t¼ α þβ1AVG CEOPoweri;tþβ2Controlsi;tþ ε i;t(6) where AVG_CEOPower i,t includes the individual industry averages of C_COMP, C_DUAL, C_STATUS, C_TENURE and C_DIRECTOR. The control variables are similar to those in Model 4. The predicted values obtained from Model 6 replace the instrumental variable AVG_ CEOPower i,t in the second stage of IV-2SLS regression model specified through following model: Crash Riski;tþ1¼ α þβ1PREDICT CEOPoweri;tþβ2Controls þ ε i;t(7) The Crash_Risk i,tþ1 includes both NCSKEW tþ1 and DUVOL tþ1 measures. PREDICT_ CEOPower i,t represents the predicted values of various CEO power proxies (PREDICT_ COMP, PREDICT_DUAL, PREDICT_STATUS, PREDICT_TENURE and PREDICT_ DIRECTOR) from Model 6. The list of control variables remains the same as in former regression models. Table 8 presents the results of regression Models 6 and 7. The significant negative association between select proxies of CEO power (C_DUAL, C_STATUS and C_ DIRECTOR) and future stock price crash risk was maintained even after addressing the endogeneity concerns. Similarly, the regression results for C_COMP and C_TENURE also remained the same. Although this research suggests a significant negative effect of CEO power on one-year ahead stock price crash, however the potential endogenous relation between the said variables can be a cause of concern. Endogeneity may arise from unobservable heterogeneity, as there could be hidden firm-specific factors that simultaneously impact both CEO power proxies and crash risk. Thus, drawing cues from Al Mamun et al. (2020) and follows DiD methodology to alleviate endogeneity concerns. The adoption of DiD approach tackles omitted variable bias that can be linked with CEO power and stock price crash risk. Further, this technique helps in establishing causality that is not ambiguous since it is conducted around exogenous shock to the core variable, i.e. CEO power. This section investigates the role of CEO turnover as an exogeneous event in assessing the changes in stock price crash risk. CEO turnover is considered to be related to CEO power, since powerful CEOs face lower AJEB 8,2 186 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 First stage Second stage Variable C_COMP C_DUAL C_STATUS C_TENURE C_DIRECTOR Variable NCSKEW tþ1 DUVOL tþ1 AVG_COMP 10.11*** (11.2) ––––PREDICT_COMP 3.12 (2.01) 1.98 (4.56) ACG_DUAL –6.88*** (18.06) –––PREDICT_DUAL 0.15* (2.35) 0.23** (3.12) AVG_STATUS ––14.34*** (25.04) ––PREDICT_ STATUS 0.55* (2.33) 0.40 (7.61) AVG_TENURE –––8.77*** (14.11) –PREDICT_ TENURE 0.09 (0.65) 0.08 (6.43) AVG_ DIRECTOR ––––8.21*** (24.76) PREDICT_ DIRECTOR 0.78*** (1.29) 0.65*** (5.61) DTURNOVER 2.12*** (1.17) 0.05 (2.75) 0.14 (2.13) 0.28 (2.59) 0.27 (0.71) DTURNOVER 0.03 (9.92) 2.37*** (1.07) RET 4.58*** (3.93) 16.77*** (3.32) 12.36*** (1.75) 28.15** (2.07) 4.84*** (1.56) RET 65.31*** (11.28) 10.96* (3.48) M/B 146*** (1.37) 0.85 (13.05) 0.13* (7.16) 0.59 (3.19) 0.24*** (6.54) MB 0.03*** (3.28) 6.40 (1.12) SIZE 2.68*** (12.09) 0.39 (4.15) 0.22*** (16.38) 0.67*** (9.41) 0.39** (5.44) SIZE 0.32** (4.55) 0.50*** (1.84) LEV 8.97*** (6.69) 0.78*** (1.91) 0.99*** (12.19) 1.43 (10.7) 0.30 (3.06) LEV 0.42*** (5.12) 0.16* (2.55) Constant 6.54*** (4.77) 9.79*** (8.86) 7.09*** (15.31) 45.98*** (6.15) 5.66*** (18.73) Constant 4.58*** (4.61) 0.01** (7.89) N 2,360 2,360 2,360 2,360 2,360 N 2,360 2,360 R 2 0.19 0.17 0.31 0.19 0.24 R 2 0.87 0.77 Year effects Yes Yes Yes Yes Yes Year effects Yes Yes Industry effects Yes Yes Yes Yes Yes Industry effects Yes Yes Note(s): C_COMP 5CEO compensation; C_DUAL 5CEO duality; C_STATUS 5CEO status; C_TENURE 5CEO tenure; C_DIRECTOR 5CEO directorship; AVG_ C_COMP 5average measure for CEO compensation of companies in the same industry; AVG_C_DUAL 5average measure for CEO duality for companies in the same industry; AVG_C_STATUS 5average measure for CEO status for companies in the same industry; AVG_C_TENURE 5average measure for CEO tenure for companies in the same industry; AVG_C_DIRECTOR 5average measure for CEO directorship for companies in the same industry; PREDICTED_C_COMP 5predicted value of CEO compensation as estimated in the first stage of IV-2SLS; PREDICTED_C_DUAL 5predicted value of CEO duality as estimated in the first stage of IV-2SLS; PREDICTED_C_STATUS 5predicted value of CEO status as estimated in the first stage of IV-2SLS; PREDICTED_C_TENURE 5predicted value of CEO tenure as estimated in the first stage of IV-2SLS; PREDICTED_C_DIRECTOR 5predicted value of CEO directorship as estimated in the first stage of IV-2SLS; NCSKEW tþ1 5negative conditional skewness measured at tþ1;DUVOL tþ1 5down-to-up-volatility measured at tþ1; DTURNOVER 5de-trended turnover; RET 5company-specific weekly returns; M/B 5market to book value of equity; SIZE 5size of company measured by taking the natural log of market capitalisation; LEV 5leverage; AVG_C_COMP,AVG_C_DUAL,AVG_C_STATUS,AVG_C_TENURE, and AVG_C_DIRECTOR, are used as instrumental variables in the first stage of IV-2SLS model. ***, ** * indicates the level of significance at 1, 5 and 10%, respectively. t-statistics reported in the parentheses are based on robust standard errors Source(s): Author’s calculations based on using Stata 14 Table 8. Addressing endogeneity-IV-2SLS regression results CEO power and stock price crash risk in India 187 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 turnover risk vis- a-vis less powerful CEOs (Finkelstein et al., 2009), alternatively, it is unlikely that CEO turnover directly affects stock price crash risk, therefore, reverse causality is not a concern here. For the application of DiD technique, this study categorises sample firms into treatment and control groups. The companies categorised under treatment group are the ones where CEO turnover takes place during the study period, while control groups are similar companies based on firm size of treatment groups where no CEO turnover takes place. The changes in CEO power and stock price crash risk have been evaluated for both the treatment (companies with CEO turnover) and control (matched companies) groups. Panel A of Table 9 Before After DiD Panel A: univariate analysis Difference in mean NCSKEW tþ1 between treatment and control companies 0.23** (0.09) 0.16* (0.01) 2.36*** (0.43) Difference in mean DUVOL tþ1 between treatment and control companies 0.12** (0.00) 0.04** (0.00) 1.42* (0.06) Difference in mean C_COMP between treatment and control companies 0.43 (1.23) 0.36* (0.04) 1.05 (0.00) Difference in mean C_DUAL between treatment and control companies 0.21* (0.63) 0.03* (0.02) 3.48** (0.01) Difference in mean C_STATUS between treatment and control companies 0.18*** (0.07) 0.06** (0.00) 1.13* (0.00) Difference in mean C_TENURE between treatment and control companies 0.35* (0.00) 0.27** (0.13) 2.49* (0.00) Difference in mean C_DIRECTORSHIP between treatment and control companies 0.22** (0.06) 0.11* (0.18) 1.77* (0.00) Variable NCSKEW tþ1 DUVOL tþ1 Panel B: regression analysis TREAT 0.01 (1.09) 0.00 (0.00) POST 0.46*** (0.02) 0.08*** (0.01) TREAT*POST 0.46*** (0.03) 0.08*** (0.01) DTURNOVER 0.00 (0.00) 0.00 (0.00) RET 78.50*** (2.45) 0.93*** (0.36) M/B 0.00 (0.00) 0.00 (0.00) SIZE 0.05*** (0.00) 0.01*** (0.01) LEV 0.29*** (0.05) 0.02** (0.01) Constant 0.56*** (0.13) 0.08*** (0.02) N 205 205 R 2 0.73 0.65 Year effects Yes Yes Industry effects Yes Yes Note(s): TREAT 5binary variable that takes the value of 1 if the company is categorised under the treatment group i.e. the company experienced CEO turnover and 0 if the company is categorised under the control group i.e. the company did not experience any CEO turnover; POST 55binary variable that takes the value of 1 post the CEO turnover period and 0 otherwise; TREAT*POST 5an interaction variable between the treatment group and post CEO turnover period; NCSKEW tþ1 5negative conditional skewness measured at tþ1; DUVOL tþ1 5down-to-up-volatility measured at tþ1; DTURNOVER 5de-trended turnover; RET 5company-specific weekly returns;M/B5market to book value of equity; SIZE 5size of company measured by taking the natural log of market capitalisation;LEV5leverage. *** and ** indicates the level of significance at 1 and 5% respectively. t-statistics reported in the parentheses are based on robuststandard errors. Difference-in-Difference panel data regression methodology has been applied to obtain the said test results Source(s): Author’s calculations based on using Stata 14 Table 9. Addressing endogeneitydifference-in-difference analysis AJEB 8,2 188 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 presents the test results from the univariate analysis. The results indicate that the differences in the mean values of all the proxies of CEO power between treatment and control companies prior to CEO turnover were statistically significant. Further, after CEO turnover, the mean values of CEO power proxies of treatment companies significantly lowered compared to control companies. The univariate analysis also revealed that the stock price crash risk increased more treatment companies vis- a-vis control group, indicating that CEO turnover lowers CEO power which further increases the risk of stock price crash. After examining the univariate results, the following multivariate DiD regression model was run: Crash Riski;tþ1¼ α þβ1POSTi;tþβ2TREATi;tþβ3POST*TREATi;tþβ4Controls þ ε i;t (8) where Crash_Risk i,tþ1 is alternatively represented by NCSKEW tþ1 and DUVOL tþ1 measured one year ahead. POST i,t refers to the binary variable that takes the value of one post the CEO turnover period, and zero otherwise, TREAT i,t takes the value of one if a company has experienced CEO turnover and zero otherwise, POST*TREAT i,t is an interaction term, and Controls include SIZE, LEV, RET, DTURNOVER, M/B, IND and YEAR. Panel B of Table 9 ascertains a significantly positive impact of POST*TREAT i,t on crash risk indicating that treatment companies experience a higher stock price crash risk following the event of CEO turnover, highlighting that CEO power weakens in companies post such an event. Therefore, these results further affirm that CEO power lowers the incidence of future stock price crash risk. 4.4 Assessing moderating effects of insider trades The moderating effect of insider trades on the association between CEO power and future stock price crash risk has been analysed with the application of pooled OLS hierarchical regression methodology. The regression models for the same has been specified as below: Crash Riski;tþ1¼ α þβ1CEOPoweri;tþβ2InsiderTradesi;tþβ4Controlsi;tþ ε i;t(9) Crash Riski;tþ1¼ α þβ1CEOPoweri;tþβ2InsiderTradesi;t þβ3CEOPoweri;t* InsiderTradesi;tþβ4Controlsi;tþ ε i;t(10) where Crash_Risk i,tþ1 represents the variable future stock price crash risk (NCSKEW tþ1 and DUVOL tþ1 ) measured one year ahead. CEOPower i,t , indicates the various proxies of power dimensions of a CEO (C_COMP, C_DUAL, C_STATUS, C_TENURE and C_DIRECTOR). InsiderTrades i,t is the moderating variable when the interactive variable “CEOPower i,t * InsiderTrades i,t ”is significant (Baron and Kenny, 1986). The list of controls remains the same as specified in Model 4 and Table 1 entails the detailed definitions of all the variables. The moderating variable strengthens the relationship between two variables (Baron and Kenny, 1986). Data analysis in the hierarchical regression model follows certain steps. Firstly, the moderating variable, viz., insider trades is entered in the regression Model 9. Secondly, the interactive variable of five dimensions of CEO power and insider trades are included in the Model 10. The test results of this section have been reported in Table 10. The findings indicate that the moderating variable insider trades poses a significantly positive impact on diverse measures of crash risk {NCSKEW tþ1 (β50.06, p<0.10, DUVOL tþ1 (β50.07, p<0.01)}. Further, the test results confirmed the moderation effects of insider trades on the association of select dimensions of CEO power viz., C_DUAL, C_STATUS and C_DIRECTOR and stock price crash risk. Thus, H6 is not fully supported. CEO power and stock price crash risk in India 189 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 5. Conclusion This study explores the impact of CEO power on future stock price crash risk within the context of companies listed on the S&P BSE 500 Index in India during the period from 2013 to 2022. The findings reveal that, except for CEO tenure, all other measures of CEO power examined show a negative association with one-year ahead or future stock price crash risk. Particularly, CEO duality, status and directorship exhibit significant negative effects on stock price crash risk, while CEO compensation does not demonstrate similar significance. These results maintain their robustness when applying alternative methodological approaches including a fixed-effects panel data regression model and Bayesian regression based on MCMC simulation. Further, the results hold even after incorporating an alternate measure of stock price crash risk and some additional control variables such as financial constraints and analyst coverage. Furthermore, the study addresses concerns of endogeneity Column A Column B Variable NCSKEW tþ1 NCSKEW tþ1 DUVOL tþ1 DUVOL tþ1 C_COMP 4.81** (1.82) 1.34 (1.90) 1.16** (3.87) 3.26 (3.84) C_DUAL 0.03* (0.05) 0.09** (0.00) 0.01* (0.01) 0.05* (0.00) C_STATUS 0.02** (0.06) 0.05* (0.04) 0.07* (0.01) 0.01* (0.01) C_TENURE 0.04 (0.00) 0.00 (0.00) 0.00 (0.01) 0.00 (0.00) C_DIRECTOR 0.06* (0.10) 0.24*** (0.00) 0.04* (0.02) 0.17* (0.00) INSIDER_TRADES 0.06* (0.03) 0.10* (0.09) 0.07*** (0.00) 0.09* (0.02) C_COMP* INSIDER_TRADES –8.28 (1.33) –1.45 (3.89) C_DUAL* INSIDER_TRADES –0.13* (0.12) –0.01* (0.00) C_STATUS* INSIDER_TRADES –0.06* (0.01) –0.11* (0.00) C_TENURE* INSIDER_TRADES –0.00* (0.08) –0.00 (0.02) C_DIRECTOR* INSIDER_ TRADES –0.06* (0.02) –0.03** (0.00) DTURNOVER 0.00* (0.00) 0.00 (0.00) 2.04 (0.00) 6.19 (0.00) RET 42.04*** (2.39) 16.75* (14.03) 4.38*** (0.51) 4.26* (4.63) M/B 0.00** (0.00) 0.00 (0.00) 0.00* (0.00) 0.00 (0.00) SIZE 0.11*** (0.02) 0.04 (0.02) 0.09*** (0.00) 0.03 (0.01) LEV 0.17* (0.15) 0.86* (0.21) 0.12* (0.04) 0.05 (0.03) Constant 3.19*** (0.47) 1.55 (0.34) 0.91*** (0.10) 0.86 (0.15) N 2,360 2,360 2,360 2,360 R 2 0.43 0.67 0.31 0.46 Year effects Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Note(s): C_COMP 5CEO compensation; C_DUAL 5CEO duality; C_STATUS 5CEO status; C_TENURE 5CEO tenure; C_DIRECTOR 5CEO directorship; INSIDER_TRADES 5insider trading; C_COMP*INSIDER_TRADES 5interaction variable between CEO compensation and insider trading; C_DUAL*INSIDER_TRADES 5interaction variable between CEO duality and insider trading; C_STATUS*INSIDER_TRADES 5interaction variable between CEO status and insider trading; C_TENURE*INSIDER_TRADES 5interaction variable between CEO tenure and insider trading; C_DIRECTOR*INSIDER_TRADES 5interaction variable between CEO directorship and insider trading; NCSKEW tþ1 5negative conditional skewness measured at tþ1;DUVOL tþ1 5down-to-up-volatility measured at tþ1; DTURNOVER 5de-trended turnover; RET 5company-specific weekly returns;M/ B5market to book value of equity; SIZE 5size of company measured by taking the natural log of market capitalisation; LEV 5leverage. *** and ** indicates the level of significance at 1 and 5%, respectively. t-statistics reported in the parentheses are based on robust standard errors. Pooled OLS hierarchical regression methodology has been applied to obtain the said test results. Column A relates to the testing of Model 9 where only the moderating variable is entered, while Column B represents the results pertaining to Model 10 where both the moderating and interaction variables are entered Source(s): Author’s calculations based on using Stata 14 Table 10. Assessing moderating effects of insider trades on association between CEO power and stock price crash risk AJEB 8,2 190 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/2/169/9598592/ajeb-10-2023-0095.pdf by ZBW German National Library of Economics user on 16 December 2025 by initially employing the IV-2SLS estimation, using the average CEO power measures of other firms within the same industry as instruments. Secondly, DiD technique also addressed the endogeneity concerns by categorising the sample companies into treatment and control groups based on exogenous shock of CEO turnover. The study also examined the moderating effects of insider trades on the association between CEO power and stock price crash risk. The results found that the presence of insider trades increases the risk of a future crash risk and it also moderates the relationship between select dimensions of CEO power viz., duality, status and directorship and stock price crash risk. These findings hold significant implications for emerging economies like India, where stock price crashes can pose a profound impact on the investment community. Firstly, despite the growing pertinence of understanding the nuances of stock price crash risk, limited heed has been paid to this area within the Indian stock market. There are studies that have examined the effect of outside block ownership (Chauhan et al., 2015), stock liquidity (Chauhan et al., 2017) and corporate governance including corporate social responsibility (Hunjra et al., 2020) on stock price crash risk in India. However, the theme explored in this study remains unique. Secondly, this study’s results which pronounced the significant negative impact of CEO power gauged through CEO duality, status and directorship on the potential risk of future stock price crash risk signify a notable departure from the conventional reliance on agency theory. Instead, this research prominently highlights the application of stakeholder theory within the unique landscape of India’s corporate environment. Thirdly, the findings bear substantial implications for retail investors, who, in contrast to institutional investors possess constrained resources to counter losses on account of stock price crashes. Through a comprehensive examination, this study unveils how specific indicators of CEO power can help mitigate the risk of stock price crashes, serving as a vital roadmap for retail investors seeking to reduce their exposure to risk in the ever-changing landscape of capital markets. Fourthly, in 2020, the Securities and Exchange Board of India (SEBI) imposed a mandatory requirement for the top 500 listed companies based on market capitalisation to separate the roles of chairperson and CEO. However, this mandate was subsequently revised to become voluntarily. In contrast to this regulatory shift, the results of this study offer empirical evidence supporting a significant negative effect of CEO duality on stock price crash risk. This underscored the importance of regulators reconsidering their stance on CEO duality and its implications for corporate governance. Fifthly, the revelation that increased CEO power is associated with a reduced risk of future stock price crashes underscores the significance of family business structures within the Indian corporate landscape. Consequently, future research endeavours can delve deeper into this dimension by stratifying the study sample into family and non-family companies, thereby providing additional valuable insights. Sixthly, the study’s findings, which link the presence of insider trades with the association between CEO power and future stock price crash risk, underscore the detrimental effects of such practices. Despite SEBI mandating the disclosure of insider trades, several illegal transactions evade detection by government and corporate authorities, posing threats to stock price declines. Addressing this issue further could be beneficial. Finally, the study was distinctive in its approach by incorporating Bayesian regression analysis, a non-frequentist methodology, alongside traditional classical methods. This novel inclusion of Bayesian techniques in management research represents a relatively unexplored avenue, adding depth and diversity to the analytical framework employed. In conclusion, the findings from this study offer essential insights for companies and retail investors seeking to manage and mitigate concerns related to stock price crash risk. 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