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Untitled BÁO CÁO PHƢƠNG PHÁP NGHIÊN CỨU TRONG TÀI CHÍNH ng dGiảng viên hƣớ ẫn Dƣơng Đăng Khoa TOPIC CEO OVERPOWER, OWNERSHIP STRUCTURE AND STOCK PRICE CRASH RISH IN VIETNAM Group 6 Mai Bích Tuy B17012[.]
BÁO CÁO PHƢƠNG PHÁP NGHIÊN CỨU TRONG TÀI CHÍNH Giảng viên hƣớng dẫn: Dƣơng Đăng Khoa TOPIC: CEO OVERPOWER, OWNERSHIP STRUCTURE AND STOCK PRICE CRASH RISH IN VIETNAM Group 6: Mai Bích Tuyền - B1701298 Hà Vĩnh Nghi – B1701203 Lê Thị My Uyên – B1701304 Nguyễn Thị Thùy Dƣơng – B1800365 Hồ Chí Minh, Ngày 17 tháng 07 năm 2021 0 Tieu luan I Instruction The stock market is no stranger to us today, this market is growing every day, making many investors especially interested As usual, stock prices will have sudden ups and downs, they are difficult to control.The stock price crash, which refers to sudden and dramatic fall of stock prices, has become anincreasingly important topic in financial studies due to its effects on investment decisions, risk management, corporate governance and regulatory practices Crash risk, which is the risk of a stock price crash, is an attraction for investors, because unlike the risk derived from symmetric movements, it cannot be mitigated through strategies portfolio diversification (according to Sunder, 2010; Kim and Zhang, 2016) Previous studies have shown that crash risk builds on agency conflicts between managers and shareholders, and focuses primarily on managers' equity and attributes (Kim et al., 2019;Mamun et al., 2020) in addition to external monitoring forces (An and Zhang, 2013; Callen and Fang, 2017; Deng et al, 2020) on crash risk In the past, innovation in the context of CEO succession in family firms has been investigated as well as explored (Jan and Nora and Jan-Philipp and Michael, 2020) , that in many groups the distinctive familial leadership and access to knowledge resources of their predecessors, the transfer of resources and lovers of knowledge, provide a context that allows renew Meanwhile, the relationship between the power or dominance of the CEO and the ownership structure as well as the surrounding risks has not been clearly defined Therefore, in this study, we especially aim to clarify that relationship as accurately as possible, and specifically related to the risk of falling stock prices in the Vietnamese stock market Usually, something new is easy to attract attention and special interest The same thing happens in the economy as well as the stock market in Vietnam Vietnam is a developing country with potential but strong development potential such as real estate, currency, industry, stock market Moreover, the importance of emerging stock markets is gradually increasing gradually attracting more investors Rights always come with responsibilities, this is a basic principle in life as well as corporate governance For the board of directors, who receive the trust of shareholders, employees, customers, stakeholders and society at large, their responsibilities are therefore enormous.In Vietnam, the new chairman is the most powerful person in the company, while abroad, the chief executive officer (CEO) has almost full decision-making power; The CEO can be fired by the board of directors if he does not well, but the CEO is the one who executes, greatly affecting the 0 Tieu luan performance of a company In Vietnamese enterprises, the chairman often concurrently holds the title of CEO, or the board members are too young, holding a very large number of shares, without knowing the real role of this person nothing, or just "name" for enough numbers Many organizations pouring capital into businesses also receive the question of the management board as to what is the role of the investor in their business, so their search for an executive board just needs to be "friendly" to the public It was very difficult for investors alone In developed markets, the highest skill of a CEO is allocating capital and company resources effectively To this, a company's apparatus needs to be decentralized in decisionmaking and responsible for decisions within authority.There is no need for too many people to participate in the decisions, ensuring the leanest business operation of the company At this time, the new CEO has time to strategize capital allocation, because a CEO who only focuses on handling operational issues can not have optimal efficiency, simply put, CEOs Must have the mindset of an investor Thereby it can be seen that in Vietnam there are hardly too many powerful CEOs With an economy with too many bankrupt businesses, especially for our country, the main cause of bankruptcy of businesses is lack of management experience, not knowing how to use people The business is completely dependent on the CEO, when the CEO has no vision, makes the wrong strategy or doesn't know how to use people, the business will have to pay the price When the CEO lacks the necessary management skills to navigate and manage the business to a higher level, the business project he or she is in charge of easily fails The CEO must be able to effectively handle work related to employees, cash flow, business model This is the main cause, stemming from the reliance, coldness in not mentioning that the business leader does not possess management experience, does not understand the market, the inevitable consequence is that it is not sustainable in the current time management cycle, or deviating from business, rushing into businesses beyond their ability, which is certain to fail Even veteran, successful CEOs can get sad endings due to their own arrogance For example, the case of Lord Browne[1] at BP or Jack Welch[2] being stripped of unreasonable privileges by GE As the study of management, accounting, and finance evolves, scholars are increasingly turning their attention to understanding the uncanny effects of top executives' power on operations corporate finance (Clark, Murphy, & Singer, 2014) And before that, there have been many reports on Powerful CEOs and stock price crash risk (Powerful CEOs and stock price crash risk 2020; CEO power and stock price crash risk in China: Do female directors' critical mass and ownership structure 0 Tieu luan matter?; ) , but very few on the scope of family companies, as well as in the Vietnam market Therefore, in this paper, we examine the impact of CEO power on stock price crash risk (hereinafter referred to as crash risk) This study has both similarities with previous papers and points out its differences It is similar because there have been research models of CEO power, CEO and business owner or the risk of business collapse, but through that similarity, this study will examine the relationship between CEO and CEO combination of relationships and influences between these factors As CEO POWER, OWN STRUCTURE AND STOCK PRICE RISK IN VIETNAM Not only is the topic newer than the research reports that have been posted on reputable forums, the group's report is also different from previous articles in terms of getting research data, data is taken from two exchanges major securities market in Vietnam, the articles used by the group for reference are downloaded from foreign forums such as Sci Hub, ScienceDirect, The variables that the group chooses to use as data to run the model are also different from the articles Previously available in the country, some of the different variables here are NCSKEW, DUVOL, CEOOWN, STATEOWN, BOARD X CEOPRCH, BOARD X AGE, BOARD X CEOOWN, BOARD X SHAREHOLDER From the new data of the group, it will provide readers with a new report not only in the topic, but also with new sources of valuable data for reference Research reports can provide a new source of data for future research papers, as a reference From the new research results, it will create more premise for research on CEOs, businesses, risk of breakdown The method used in this report is to take data based on financial statements and annual reports of 116 enterprises on two Vietnamese stock exchanges, HOSE and HNX Run the model against original papers collected from mainstream sources such as ScienceDirect and Sci Hub to determine the correlation between the paper's variables The study will also provide additional data sources from the financial statements of 116 companies listed on the stock exchange for the following research papers: NCSKEW, DUVOL, CEOOWN, STATEOWN, SHAREHOLDER, BOARD, LEV, The research paper also provides a number of reputable sources for reference, which have been published in forums of scientific research articles Through the article, it can provide more reference data for investors, so that they can choose between CEOs who can create risks for businesses, but bring opportunities to turn prospects and secure CEOs safe but will make the business stereotype, make the business too safe, there is no new transformation Not only investors, the company's board of directors is also the target of this research report, they can have a new perspective on giving power to the CEO, the shares held 0 Tieu luan by the CEO can create risks to the business, as well as the risks and development benefits that each type of CEO can bring to the business II Literature review Measures of crash risk Crash risk, defined as the conditional skewness of return distribution, captures asymmetry in risk and is important for invesment decisions and risk management Yasir et al (2020),to examine the relationship between CEO power and stock price crash risk, we build the following regression model: = + +∑ + where, crash risk is the risk of stock price decline for company i's stock in year t; CEOPOWER is the sum of the value variable (dummy) CEOPRCH, the percentage of share ownership of CEO- CEOOWN; CEO age - CEOAGE To measure crash risk, we employed two proxies (NCSKEW, DUVOL) following previous studies (Kim et al., 2011; Xu et al., 2014; Zhang et al., 2016) as our main crash risk and CEOPRCH as the CEO's main power variable (Kim et al.2011) The literature of the research paper is referenced in a number of ways CEO power is associated with a higher risk of collapse The association between CEO power and higher risk of collapse keeps control of other drivers or mechanisms for hoarding bad news such as financial opacity (Hutton et al., 2009) Reduce the weight of the negative to positive earnings guidance and lower the negative to positive weighting in their financial statements Overall, to understand the determinants of corporate risk, it is important to look at the structure of a company's decision-making power The coefficients of CEO power are positive when turnover events result in increase in CEO power and are negative when turnover events result in decrease in CEO power Yasir et al.(2020) show: “Studies on CEO characteristics as determinants of crash risk elucidate that: Overconfident executives increase future crash risk (Kim) et al., 2016); incentives from holding CEO options have a weak positive association with collision risk (Kim et al., 2011); and (iii) younger CEOs tend to pose a collision risk (Andreou, Louca, & Petrou, 2016).Previous research (eg, Andreou et al., 2016; Chen et al., 2017a) shows that effective corporate governance can reduce the risk of failure Thereby seeing the impact of corporate governance on the relationship between the risk of collapse and CEO power and the negative impact of stock price risk on CEO power will be reduced by companies have a solid operating system.Through the 0 Tieu luan articles studied above, we can see that the CEO's power will lead to the risk of stock price decline The influence of CEO power on pre-collision risk is quite clear Thereby, our team hypothesized the following: CEO power as a positive relationship with on crash risk The relation between crash risk and future CEO power Hypothesize that crash risk is negatively related to future CEO power because CEO power increases crash risk (Mamun et al., 2019) Habib et al (2018) argue that rational firms should limit or eliminate certain factors that increase crash risk after stock price crashes Hence, expect that after price crashes, rational firms curtail CEO power.Through referenced original articles, we see accident risks after CEO changes CEOs with more power will have a higher risk of collapse, and vice versa, CEOs with less power will reduce the risk of change We compare CEOs of companies, these CEOs have similar characteristics but not have absolute power Companies with powerful CEOs are more at risk than similar companies but CEOs don't The CEO is the person responsible for operating and making decisions for the direction of the business, so the CEO's power not only affects the risk of business collapse, but ownership is also an important factor that directly affects the company's business the CEO's decisionmaking process From the above impact, our group boldly hypothesized: Ownership will reduce the negative impact of CEO power on the risk of collapse Measures of DUVOL DUVOL is Down to up volatility (DUVOL) calculated from the equation used to measure the risk of falling stock prices.The DUVOL variable is measured as follows: ( ) = log ( ( ) ) Our measure of stock price crash risk is down-to-up volatility (DUVOL) This measure was developed by Chen et al (2001) and followed by Huttonet al (2009) and Kim et al (2011a, 2011b) To calculate DUVOL, we separate specific weekly returns into down and up weeks Specifically, down (up) weeks refer to those weeks during which firm-specific weekly returns are below (above) the annual average weekly return We calculate DUVOL as the log of the ratio of the standard deviation of firmspecific down weekly returns to the standard deviation of up weekly returns during the fiscal year Similar to Kim et al (2011b) and Kim and Zhang (2016), we estimate our crash risk measures over a 12-month period starting three months after the fiscal year-end 0 Tieu luan Measures of NSCKEW NCSKEW is the negative coefficient of skewness (NCSKEW) calculated from the equation used to measure the risk of falling stock prices The main measure of crash risk is the “negative coefficient of skewness” (NCSKEW), calculated by taking the negative of the third moment of firm-specific weekly returns for each sample year and dividing it by the standard deviation of firmspecific weekly returns raised to the third power (Kim et al., 2011; Xu et al., 2014) Particularly, in the second step, we calculate the NCSKEW (crash risk) for each firm „i‟ in year „t‟ as: = -[n(n-1)3/2∑W3i,t]/[(n-1)(n-2)(∑W2i,t)3/2] Chen et al.(2001), scaling the raw third moment by the standard deviation cubed allows for comparisons across stocks with different variances; this is the usual normalization for skewness statistics (Greene, 1993) By putting a minus sign in front of the third moment, we are adopting the convention that an increase in NCSKEW corresponds to a stock being more „„crash prone‟‟ Other determinants Similar to previous studies on crash risk (Callen & Fang, 2013; Gul et al., 2010; Kim et al., 2011; Xu et al., 2014) and CEO power ( Adams et al., 2005; Bebchuk et al., 2011; Liu & Jiraporn, 2010), the study indicated a number of different related control variables, which can influence the models To measure the CEO power variable, we use the following variables: CEO age (CEOAGE) - we take the years in the period 2005-2020 minus the CEO's age according to each current CEO of the company companies in the above period, because there are a number of companies with different CEO changes, follow younger CEOs tend to pose a collision risk (Andreou, Louca, & Petrou, 2016); with CEOPRCH - this variable is a binary variable if the person is both chief executive officer and chairman, the value will be and vice versa, if the person holds the position of chief executive officer only, it will be If a CEO is not the chair of the board, the CEO will have less power, since the chair has a greater influence on most strategic business decisions (Adams et al., 2005) Similarly, a CEO with president role can ensure that board has limited choice in ensuring an in-training successor to tap if disagreement with the CEO ensues (Morse et al., 2011) CEOOWN - the percentage of shares the CEO owns Finkelstein (1992) also acknowledges that structural power is perhaps the most commonly cited type of 0 Tieu luan power In terms of ownership power, Finkelstein (1992) argues that founder executives gain power through their long-term interaction with the board In addition, this paper uses control variables to measure ownership structure of companies such as LEV - Total long-term debt is calculated by total assets year over year for each company ROA - Income before extraordinary items divided by lagged total assets.BOARD -owned by the company's organization, which can be a domestic or foreign organization SHAREHOLDER - percentage of shareholders with a percentage ownership greater than 5% of the shares in the company, possibly including percentage ownership of the CEO STATEOWN - state ownership, this is the percentage of state ownership that contributes to the company's charter capital TOTAS - total assets of the company over the years III Research design Sample selection and descriptive statistic We obtain a total of 116 firm with 986 firm-year observations for 2005 – 2020 from tow the most stock screen at market Vietnam To develop measures of CEO power, we use year-over-year observations of 116 selected and family-owned companies with the aim of bringing the observation closer to CEO power We not use finance companies here, nor companies that have been in business for less than two years Correlation matrix and univariate test We further performed a univariate test to highlight the possible association between crash risk and CEO power Overall, our results in this empirical section provide an initial positive/negative relationship between CEO power and stock price crash risk In this univariate regression table, we use NSCKEW and DUVOL for year t as the dependent variable The main independent variable is CEOpower, using different measures CEOown, CEOPRCH 0 Tieu luan IV Empirical results Descriptive statistic DUVOL Mean Median Maximum Minimum Std Dev Skewness Kurtosis DUVOL -0.12549 -0.13322 1.174062 -1.36612 0.242627 0.130123 4.719333 CEOPRCHAGE X7_CEOOWN_BOARD 0.362069 49.70863 0.085546 0.246611 50 0.0268 0.16355 77 0.7 0.98 23 0 0.480843 9.35085 0.125591 0.250629 0.573997 -0.03815 2.092791 0.752439 1.329472 2.970093 7.563808 2.311323 Jarque-Be 124.2291 Probability 168.793 0.275654 0.871249 SHAREHOSTATEOW LEV ROA BOARD_X_BOARD_X_BOARD_X BOARD_X TOTAL_AS 0.404534 0.054366 0.500187 5.75579 0.014348 12.58725 0.075067 0.105904 3661872 0.40095 0.52 4.54 0.001369 7.24 0.040734 937136 0.966 0.7919 0.998 72.19 0.331513 57.82 0.98 0.717968 1.32E+08 0 0.005719 -33.2 0 0 36844 0.232609 0.137428 0.203592 6.830939 0.030754 13.53655 0.172475 0.149115 10740220 0.133381 2.898481 -0.11793 1.23858 3.90182 0.963982 2.572039 1.87874 6.655941 2.346247 11.28937 2.307727 13.82426 24.96681 2.881431 8.930724 6.341598 57.62317 1561.06 112.5245 20.48231 4203.582 21.95187 5060.476 22122.44 153.1306 2532.173 1038.789 129333.4 0 0.000036 0.000017 0 0 0 Sum -123.728 357 48963 83.57814 243.1585 398.8702 53.6053 492.6842 5669.453 14.01762 12398.44 74.01646 104.4212 3.60E+09 Sum Sq D 57.98505 227.7414 86039.38 15.39453 61.87253 53.29541 18.60308 40.78649 45915.14 0.923136 180306.4 29.30149 21.90176 1.13E+17 Observatio 986 986 985 977 986 986 986 985 985 977 985 986 986 982 NCSKEW Mean Median Maximum Minimum Std Dev Skewness Kurtosis NCSKEW -0.30296 -0.32927 3.599262 -3.48413 0.735078 0.244349 5.047296 Jarque-Be 182.0093 Probability CEOPRCHAGE X7_ CEOOWN_BOARD 0.362069 49.70863 0.085546 0.246611 50 0.0268 0.16355 77 0.7 0.98 23 0 0.480843 9.35085 0.125591 0.250629 0.573997 -0.03815 2.092791 0.752439 1.329472 2.970093 7.563808 2.311323 168.793 0.275654 0.871249 SHAREHOSTATEOW LEV ROA BOARD_X_BOARD_X_BOARD_X_BOARD_X_TOTAL_AS 0.404534 0.054366 0.500187 5.75579 12.58725 0.014348 0.075067 0.105904 3661872 0.40095 0.52 4.54 7.24 0.001369 0.040734 937136 0.966 0.7919 0.998 72.19 57.82 0.331513 0.98 0.717968 1.32E+08 0 0.005719 -33.2 0 0 36844 0.232609 0.137428 0.203592 6.830939 13.53655 0.030754 0.172475 0.149115 10740220 0.133381 2.898481 -0.11793 1.23858 0.963982 3.90182 2.572039 1.87874 6.655941 2.346247 11.28937 2.307727 13.82426 2.881431 24.96681 8.930724 6.341598 57.62317 1561.06 112.5245 20.48231 4203.582 21.95187 5060.476 153.1306 22122.44 2532.173 1038.789 129333.4 0 0.000036 0.000017 0 0 0 Sum -298.721 357 48963 83.57814 243.1585 398.8702 53.6053 492.6842 5669.453 12398.44 14.01762 74.01646 104.4212 3.60E+09 Sum Sq D 532.2339 227.7414 86039.38 15.39453 61.87253 53.29541 18.60308 40.78649 45915.14 180306.4 0.923136 29.30149 21.90176 1.13E+17 Observatio 986 986 985 977 986 986 986 Tieu luan 985 985 985 977 986 986 982 In Table 1, descriptive statistics on CEO power and control variables, stock price collapse risk variables in the period 2005-2020 are presented We find that the mean of NSCKEW is – 0.302 and that of DUVOL is -0.125 ; for variables of CEO power such as CEO percentage ownership of CEO (CEOOWN) 8.5% and CEOPRCH- ceo holds the position of chairman is 36.2%; control variables such as BOARD is 24.6%; Shareholder is 40.4%; LEV is 50%; Stateown is 5.43% These summary statistics are comparable to those reported in the literature on the determinants of failure risk stock price Crash risk and CEO power relation atistic NCSKEW OPRCH X2_ OOWN X8 NCSKEW AGE X7_ _ 1.000000 - EOPRCH X2_ -0.016485 -0.513755 1.000000 - AGE X7_ 0.015629 0.487077 0.262491 8.476686 1.000000 - -0.023178 -0.722428 0.437240 15.14968 0.132488 4.165145 -0.041472 -1.293423 -0.115204 -3.613912 HAREHOLDER -0.009064 -0.282438 STATEOWN EOOWN X8_ BOARD BOARD AREHOLDE RSTATEOWN LEV OARD_X_A OARD_X_CE ARD_X_COARD_X_SH ROA GE OOWN EOPRCH REHOLDER TOTAL_ASSET 1.000000 - 0.136649 -0.212699 4.298415 -6.783096 1.000000 - 0.015353 0.478468 0.057413 1.791998 0.162478 5.131125 0.102420 3.208374 1.000000 - -0.021918 -0.683144 0.086558 2.707396 0.049358 -0.030371 1.539916 -0.946811 0.078891 2.465993 -0.091165 -2.852656 1.000000 - LEV -0.027774 -0.865809 0.027305 0.851157 0.005111 0.159258 0.065388 2.041924 -0.058207 -1.816874 0.136298 4.287164 0.115972 3.638353 ROA 0.001697 0.049356 0.018763 0.020280 0.176740 1.000000 - -0.039256 -0.018860 -0.296947 0 Tieu luan 1.000000 OARD_X_AGE ARD_X_CEOOW N ARD_X_CEOPRC H ARD_X_SHAREH OLDER OTAL_ASSET 0.052892 1.539848 -0.040531 -1.264022 -0.064884 -2.026111 -0.030445 -0.949142 0.584790 0.632080 5.595452 -1.224201 -0.587793 -9.690221 - 0.285500 -0.204083 9.282784 -6.496118 0.975596 138.4527 0.110279 3.457478 0.077331 -0.057602 2.416930 -1.797912 0.169435 1.000000 5.357190 - 0.226938 7.261048 0.046807 1.460143 0.511985 18.57276 0.291243 9.486640 0.044656 1.392904 0.062840 1.962023 0.063601 1.985896 0.115076 0.281374 3.609853 9.136998 1.000000 - -0.022034 -0.686770 0.577383 22.03595 0.237368 7.614213 0.096850 3.032176 0.395391 13.41379 0.092483 2.894249 0.052407 -0.004890 1.635306 -0.152363 0.139042 0.456852 4.375183 16.00364 0.404445 1.000000 13.78021 - -0.048369 -1.508997 -0.056630 -1.767491 0.128892 -0.154742 4.050161 -4.880681 0.786697 39.70938 0.527413 19.34380 0.042820 1.335534 0.038786 1.209526 0.086924 0.782726 2.718913 39.18977 0.151363 0.341952 4.771568 11.33909 1.000000 - -0.021772 -0.678599 -0.024783 -0.772494 0.015691 -0.082229 0.489020 -2.571022 0.111735 3.503690 0.073875 -0.099888 2.308306 -3.128251 0.097246 3.044686 0.013790 0.099686 0.429761 3.121840 -0.013913 0.127987 -0.433581 4.021274 0.129669 4.075017 1.000000 - Correlation t-Statistic DUVOL CEOPRCH X 2_ AGE X7_ CEOOWN X8 _ BOARD CEOPRCH AGE X7 CEOOWN_ X2_ _ _X8_ DUVOL 1.000000 - -0.004416 -0.137601 SHAREHO STATEOW LDER N BOARD 1.000000 - 0.018035 0.562064 0.262491 1.000000 8.476686 - -0.030817 -0.960755 0.437240 0.132488 15.14968 4.165145 1.000000 - -0.037697 -0.115204 0.136649 -0.212699 1.000000 0 Tieu luan LEV BOARD_X_ BOARD_X_ BOARD_X_C BOARD_X_ SHAREHO TOTAL_A AGE EOOWN CEOPRCH SSET LDER ROA -1.175492 -3.613912 4.298415 -6.783096 - 0.006934 0.216073 0.015353 0.057413 0.478468 1.791998 0.162478 5.131125 0.102420 3.208374 1.000000 - STATEOWN -0.034663 -1.080770 0.086558 0.049358 2.707396 1.539916 -0.030371 -0.946811 0.078891 2.465993 -0.091165 -2.852656 1.000000 - LEV -0.039331 -1.226544 0.027305 0.005111 0.851157 0.159258 0.065388 2.041924 -0.058207 -1.816874 0.136298 4.287164 0.115972 3.638353 1.000000 - ROA -0.012972 -0.404241 0.049356 0.018763 1.539848 0.584790 0.020280 0.632080 0.176740 5.595452 -0.039256 -0.018860 -1.224201 -0.587793 -0.296947 -9.690221 1.000000 - -0.033869 -1.055980 -0.064884 0.285500 -2.026111 9.282784 -0.204083 -6.496118 0.975596 138.4527 0.110279 3.457478 0.077331 2.416930 -0.057602 -1.797912 0.169435 5.357190 1.000000 - -0.029345 -0.914800 0.226938 0.046807 7.261048 1.460143 0.511985 18.57276 0.291243 9.486640 0.044656 1.392904 0.062840 1.962023 0.063601 1.985896 0.115076 3.609853 0.281374 9.136998 1.000000 - -0.011079 -0.345241 0.577383 0.237368 22.03595 7.614213 0.096850 3.032176 0.395391 13.41379 0.092483 2.894249 0.052407 1.635306 -0.004890 -0.152363 0.139042 4.375183 0.456852 16.00364 0.404445 13.78021 1.000000 - -0.033803 -0.056630 0.128892 -1.053940 -1.767491 4.050161 -0.154742 -4.880681 0.786697 39.70938 0.527413 19.34380 0.042820 1.335534 0.038786 1.209526 0.086924 2.718913 0.782726 39.18977 0.151363 4.771568 0.341952 11.33909 1.000000 - -0.027244 -0.024783 0.015691 -0.849248 -0.772494 0.489020 -0.082229 -2.571022 0.111735 3.503690 0.073875 -0.099888 2.308306 -3.128251 0.097246 3.044686 0.013790 0.429761 0.099686 3.121840 -0.013913 -0.433581 0.127987 4.021274 0.129669 1.000000 4.075017 - SHAREHOLDE R BOARD_X_AG E BOARD_X_CE OOWN BOARD_X_CE OPRCH BOARD_X_SH AREHOLDER TOTAL_ASSE T This table presents the results on the impact of CEO power on stock price crashes, present our main regression results using NCSKEW and DUVOL, respectively, as our crash variables 0 Tieu luan NCSKEW Dependent Variable: NCSKEW Method: Panel Least Squares Date: 07/17/21 Time: 13:12 Sample: 2005 2020 Periods included: 16 Cross-sections included: 116 Total panel (unbalanced) observations: 973 Variable Coefficient Std Error t-Statistic Prob C CEOPRCH X2_ AGE X7_ CEOOWN X8_ BOARD SHAREHOLDER STATEOWN LEV ROA BOARD_X_AGE BOARD_X_CEOOWN BOARD_X_CEOPRCH BOARD_X_SHAREHOLDER TOTAL_ASSET -0.679288 -0.031430 0.006974 -0.386095 0.693602 -0.024434 -0.121973 0.218185 -0.000951 -0.015311 0.002200 0.364271 -0.147687 -7.26E-10 0.340304 0.121094 0.006658 0.415193 0.950852 0.250015 0.347943 0.233222 0.004594 0.018720 1.475127 0.394506 0.629256 4.23E-09 -1.996121 -0.259553 1.047383 -0.929917 0.729453 -0.097730 -0.350556 0.935528 -0.207078 -0.817890 0.001492 0.923359 -0.234702 -0.171596 0.0462 0.7953 0.2952 0.3527 0.4659 0.9222 0.7260 0.3498 0.8360 0.4137 0.9988 0.3561 0.8145 0.8638 Effects Specification Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.133348 0.001913 0.736244 457.4942 -1013.505 1.014552 0.444030 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -0.301818 0.736949 2.348416 2.995456 2.594663 2.073241 Panel 1: Cross-section fixed model of NSCKEW Dependent Variable: DUVOL Method: Panel Least Squares Date: 07/17/21 Time: 13:17 Sample: 2005 2020 Periods included: 16 Cross-sections included: 116 Total panel (unbalanced) observations: 973 Variable Coefficient Std Error t-Statistic Prob CEOPRCH X2_ AGE X7_ CEOOWN X8_ BOARD SHAREHOLDER 0.007409 0.001403 -0.137630 0.098538 0.025616 0.039540 0.002174 0.135571 0.310477 0.081636 0.187367 0.645289 -1.015188 0.317376 0.313785 0.8514 0.5189 0.3103 0.7510 0.7538 0 Tieu luan STATEOWN LEV ROA BOARD_X_AGE BOARD_X_CEOOWN BOARD_X_CEOPRCH BOARD_X_SHAREHOLDER TOTAL_ASSET C -0.012973 0.001431 -0.001897 -0.002664 0.107787 0.080081 -0.057476 -4.78E-10 -0.176343 0.113612 0.076153 0.001500 0.006113 0.481666 0.128816 0.205468 1.38E-09 0.111118 -0.114190 0.018798 -1.264291 -0.435762 0.223780 0.621665 -0.279731 -0.345764 -1.586987 0.9091 0.9850 0.2065 0.6631 0.8230 0.5343 0.7798 0.7296 0.1129 Effects Specification Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.149012 0.019953 0.240402 48.77749 75.52333 1.154601 0.130769 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat -0.125651 0.242837 0.109921 0.756961 0.356168 2.110357 Panel 2: Cross-section fixed model of DUVOL After testing and selection, we use random effects regression model to consider the control of unobserved factors Considering the regression table, we have an R-squared index of 0.007, which means that the independent variables affect the dependent variable 0.7%, the other variables are 99.3% Considering the above regression table, we see that the variable CEOPRCH and the variable CEOAGE are positively correlated with crash risk The fact that a CEO holds the chairmanship will increase the executive's power, while also increasing the risk of crash risk Andreou et al (2016), younger CEOs tend to pose a higher crash risk We identify CEO ownership during our sample period, we have 986 observations, recorded 973 samples with changes in CEO power The t-values of DUVOL and NCSKEW are -1.475 and -0.916 respectively,as CEO ownership increases, crash risk is reduced Crash risk and ownership structure In panel and 2, it is found that the variables representing the ownership structure of firms are positively and negatively related to crash risk Variables BOARD,SHAREHOLDER, BOARDxCEOOWN, BOARDxCEOPRCH are all positively correlated with DUVOL and NCSKEW V Conclusion We investigate the role of management power in the downside risk of a company's stock Through the study, we provide evidence that CEO power is positively related to 0 Tieu luan the risk of stock devaluation of each company Our results are valid when controlling for previously recognized determinants such as total assets, CEO power, ROA We use several approaches such as random effects regression, correlation matrix Appendix: Variable definitions CEOPRCH: A binary variable equal to if the CEO is both the president and the chair CEOOWN: The percentage of shares the CEO owns CEOAGE: The age of the firm's CEO DUVOL: The log of the ratio of the standard deviation of firm-specific down weekly returns to the standard deviation of up weekly returns during the fiscal year We follow Kim et al (2011a) to calculate DUVOL NCSKEW: The negative skewness of firm-specific weekly returns over the fiscal year We follow Kim et al (2011a, 2011b) to calculate NCSKEW ROA: Income before extraordinary items divided by lagged total assets SHAREHODLER: percentage of shareholders with a percentage ownership greater than 5% of the shares in the company STATEOWN: state ownership, this is the percentage of state ownership that contributes to the company's charter capital BOARD: owned by the company's organization, which can be a domestic or foreign organization References Powerful CEOs and stock price crash risk Md Al Mamuna , Balasingham Balachandrana, Huu Nhan Duong.2020 CEO power and stock price crash risk in China: Do female directors' critical mass and ownership structure matter? Yasir Shahaba,⁎ , Collins G Ntimb , Farid Ullaha , Chen Yugangc , Zhiwei Yed.2020 Stock price crash risk and CEO power: Firm-level analysis Joel Harpera , Grace Johnsonb , Li Sun.2019 The economic determinants of CEO stock option compensation Lamia Chourou a,∗, Ezzeddine Abaoub b, Samir Saadi.2007 Hutton, A.P., Marcus, A.J., Tehranian, H., 2009 Opaque financial reports, R2, and crash risk J Financ Econ 0 Tieu luan Kim, J.B., Zhang, L., 2016 Accounting conservatism and stock price crash risk: firmlevel evidence Contemp Account Res Kim, J.B., Li, Y., Zhang, L., 2011a Corporate tax avoidance and stock price crash risk: firm-level analysis J Financ Econ Kim, J.B., Li, Y., Zhang, L., 2011b CFOs versus CEOs: equity incentives and crashes J Financ Econ Chen, J., Hong, H., Stein, J.C., 2001 Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices J Financ Econ Adams, R.B., Almeida, H., Ferreira, D., 2005 Powerful CEOs and their impact on corporate performance Rev Financ Stud 18 (4) 0 Tieu luan 0 Tieu luan ... 2014) And before that, there have been many reports on Powerful CEOs and stock price crash risk (Powerful CEOs and stock price crash risk 2020; CEO power and stock price crash risk in China: Do... CEOs and stock price crash risk Md Al Mamuna , Balasingham Balachandrana, Huu Nhan Duong.2020 CEO power and stock price crash risk in China: Do female directors'' critical mass and ownership structure. .. changes in CEO power The t-values of DUVOL and NCSKEW are -1.475 and -0.916 respectively,as CEO ownership increases, crash risk is reduced Crash risk and ownership structure In panel and 2, it