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 b:	@MANAGERIAL OVERCONFIDENCE AND COMPENSATION CONTRACTS: EVIDENCE FROM VIETNAM

                                                                                                                                                           
Abstract: 
This research investigates the impact of managerial overconfidence on compensation. The study is conducted on a sample of 225 companies listed on the Vietnamese stock market with a total of 1350 observations for a period of six years from 2012 to 2017. By using GMM (Generalized Method of Moments) to deal with endogeneity, the results add to the growing literature on Asian behavioral finance, which is still considered spars.
First, CEO (Chief Executive O�cer) overconfidence is found to positively affect on cash-based compensation. However, there is no evidence to prove that overconfident CEOs correlate with equity compensation in Vietnamese enterprises. 
Second, our findings serve as additional evidence on the influence of state-ownership on executive compensation structures. For SOEs (state-owned enterprises), overconfident CEOs have a positive impact on cash-based compensation. Meanwhile, we have not found evidence of the executive overconfidence affecting the equity compensation of SOEs. For non-SOEs (non-state enterprises), overconfident CEOs have a negative impact on cash-based compensation and equity compensation. The research results show that overconfident executives in non-SOEs have lower preference for incentive-intensive pay compared to SOEs.
Finally, we suggest that incentive-heavy compensation contracts are provided to overconfident CEOs to take advantage of their overly positive views on the company's prospects to add value to the company. In addition, the result indicates that the companies with overconfident CEOs, company performance has a positive impact on cash-based compensation. 


Keywords: CEO compensation, overconfidence.
JEL classification: G34; J33; M52



Introduction 
Jensen and Meckling (1976) theorizes that owners or principals can possibly reduce the agency problem by offering agents incentives. In other words, compensation contracts are to be provided appropriately with agents� interest for reducing conflicts of interest. Optimal compensation contract is a tool in balancing shareholders� interest and agents� interest. Importantly, a compensation contracts must be provided in accordance with characteristics of the agents to increase benefits for the company. However, there is little research on developing compensation contracts that match executive behavioral characteristics. We fill this gap. 
Otto, C., (2014) find that optimistic CEOs receive smaller stock option grants and less total compensation than their peers. Overconfident CEOs may receive a compensation contract with weak incentives in the form of cash, options or restricted stock. Because of the overly optimistic view of future corporate value, a smaller cash and equity compensation contract may be enough to make overconfident CEOs to make the necessary efforts to reach decisions. Strong incentive contracts can exacerbate the company's risks. However, Gervais, Heaton and Odean (2011) believe that it is possible to optimize compensation contracts with high cash and equity intensity for overconfident CEOs that will maximize the value of the company. Hence, the purpose of a compensation contract with strong incentives is to take advantage of the CEO�s miscalibration rather than to provide incentives. From a rational perspective, this will reduce the total compensation paid to overconfident managers. Humphery et. al (2016) refer to this as the strong-incentive hypothesis, they develop a simple extension of Gervais, Heaton and Odean�s model to show that the need to provide incentives can also lead to overconfident managers being offered compensation contracts with greater incentives. Humphery et. al (2016) also find evidence that overconfident CEOs feature even greater option (and stock) intensity in innovative and risky firms. This view is probably consistent with the development of companies in Vietnam. Most companies in Vietnam are young, with fast and strong growth, so they need factors such as overconfident CEOs with their high-risk tolerance. Therefore, a compensation contract with strong incentives for executives is probably more appropriate in the Vietnamese market. Vietnam�now is one of the most dynamic�emerging countries�in East Asia.�However, corporate governance�mechanisms remain weak�and ineffective in Vietnam. Compared to the rich US empirical studies, researchs on executive compensation in Vietnam have not been developed widely due to limited research data. Thus, this paper attempts to fill the gap by addressing how overconfident executives in a collective culture, like Vietnam, are paid. To the best of our knowledge, our paper is the first Vietnamese paper of investigating the relation between overconfident CEOs and compensation. This paper can provide the impetus for post-development studies in this area. 
First, the paper explores the relationship of executive compensation (in cash and equity intensity) and executive overconfidence. Second, Vietnamese enterprises still have a large proportion of State-owned enterprises (SOEs). The management of these enterprises still have many differences compared to the private enterprises (non-SOEs) and executive compensation mechanism is no exception. Therefore, the understanding of the difference between the relationship of CEOs' overconfidence and compensation of SOEs and non-SOEs is also an interesting topic. Third, does the CEO's overconfidence help increase the value of businesses in Vietnam? This article will answer this question. To test�the robustness�of�our results, we use firm-year fixed effects regressions, Tobit and GMM regressions. The study is conducted on a sample of 225 firms listed on the Vietnamese stock market with a total of 1350 observations for a six-year period from 2012 to 2017 (excluding financial companies due to difference in capital structure or insufficient data collection). 
2. Literature review
2.1 Overconfidence and CEO compensation
The�term��overconfidence��has been widely used�in�psychology�starting�from the 1960s. The researchers have expanded the meaning of overconfidence to explain for psychology phenomena that standard theory cannot explain (Skala, 2008). Overconfidence refers to people�s tendency to overestimate their knowledge, ability, and accuracy of information, or to be overly optimistic about themselves in dealing with their own situations (Glaser & Weber, 2003). According to Ackert and Deaves (2010), overconfidence manifests itself in these facets: �miscalibration�� implies the tendency to overestimate the accuracy of one�s knowledge; �better-than-average effect�- implies the tendency to believe that their own capabilities are better than average; �illusion of control��  implies the�tendency�for human beings to believe�they can control�or at least influence outcomes that�they�demonstrably�have no�influence�over; �excessive optimism�� the�tendency� to overestimate the probability of positive events and underestimate the probability of negative ones. 
In a�well-known� survey by Svenson (1981), 90 percent of drivers believe�they are�more skilled�and safer behind the wheel�than�their peers. Patty Bick (2015) shows that, CEOs have to overcome many obstacles to achieve their top positions that others who are not confident can not do. This success might reinforce  CEOs� conception of their own ability. For this reason, CEOs are often overconfident. 
Proxy variable for managerial overconfidence, mainly�based on the study of Barro Lucas and Silveira (2008). According to Barros Lucas and Silveira (2008), managers who are the company's owners (called entrepreneurs) tend to be more optimistic and confident than rational people. Similarly, a study by Evans & Leighton (1989) using survey data of nearly 4,000 men in North America shows that entrepreneurs have a strong belief that the company's business performance is largely dependent on their action. Or Pinfold (2001) suggests that entrepreneurs in New Zealand often overestimate the chances of a successful project. In Vietnam, many family businesses with entrepreneurs are very popular. Accordingly, overconfident CEO (OVER1) is a dummy variable�equals�1 if the CEO of the company is the owner or the founder or the heir of the company, otherwise it equals 0. This metric is suitable for the situation in Vietnam and can easily be collected.
In order to enhance the measurement of overconfidence, we measure the variable OVER2 based on Barros Lucas and Silveira (2008). Accordingly, overconfident CEO equals 1 if the CEO owns more than 50% of the company�s shares, and 0 otherwise. In addition to persistent optimism for their firms� future prospects, overconfident CEOs desire to own more shares than they really need to gain control their company (Barros Lucas & Silveira, 2008). This is also a common situation in family businesses in Vietnam. The managers of family companies often own a high percentage of shares to control the business of their family. 
The third measurement of the CEO's overconfidence is based on the tendency of overconfident CEOs to buy more company stocks despite the high risk (Malmendier and Tate, 2005). 
Malmendier and Tate (2005) show that while rational CEOs tend to minimize the holding of their company�s stock in order to divest themselves of company-specific risk, overconfident CEOs are likely to increase their control by purchasing new shares of their company�s stock. Malmendier and Tate (2005), Jarboui et al. (2014) identify overconfident CEOs are CEOs�to have been in�their position�for at�least 5 years in�their�sample. So, Malmendier and Tate eliminate the five years from the regressions. Due to the limited time of the data sample, we could not calculate the variable (Over3) after subtracting the first 5-year period such as Malmendier and Tate (2005). Instead, to measure the overconfident CEO (OVER3), I consider the increase (decrease) in the CEO's shares each year as the net amount the CEO has bought (sold). CEOs are classified as Net Buyers (Net Sellers) if the difference between the number of shares held at the end of the current financial year and the number of shares held at the end of the previous financial year is positive (negative). We use the dummy variable (Net Buyer) that equals 1 if the CEO is the net buyer of the company's stock during the sampling period and otherwise it equals 0.
Because, Overconfident CEOs are always optimistic about the company's overall outlook, they tend�to�overestimate investment returns (Dittrich et al, 2005; Malmendier & Tate, 2005, 2008; Kolasinski & Li, 2013). Similarly, Patty Bick (2015) and David et.al (2007) argue that overconfident CEOs underestimate the actual risks of their projects, so they overinvest in the hope to get more profits. Hirshleifer et al. (2012) note that CEO overconfidence can benefit shareholders by increasing investment in risky projects. Campbell et al. (2011) suggest that overconfident CEOs create an optimal level of investment for companies. Palomino and Sadrieh (2011) show that owners benefit from CEO overconfidence once they know how to take advantage of CEOs� behavioral characteristics. Adjusting incentives relating to CEO benefits can motivate them to act in the best interests of shareholders. Therefore, if the owners discover the overconfidence of the CEOs, a compensation contract should be formed taking into account of this advantage (Patty Bick, 2015). 
Adjusting incentives in the interests of the CEO can motivate them to act in the best interests of shareholders. Therefore, if the owner discovers the overconfidence of the CEOs, a compensation contract should be formed taking this into account (Patty Bick, 2015).
Otto et.al (2014) provide evidence that overconfident CEOs have lower�option�intensity�than their rational counterparts. However, many researches indicate that overconfident CEOs tend to perform better in companies that require higher levels of creativity and risk (Galasso & Simcoe, 2011; Hirshleifer et al., 2012). Hence, CEO overconfidence makes them less conservative in undertaking risky projects and saves certain cost for the companies (Gervais, 2011). Similarly, Humphery et.al (2016) find evidence to indicate that overconfident CEOs receive strong incentives when working in creative, risky, and high growth companies. In conclusions, prior theoretical literature suggests that incentive-heavy compensation contracts are offered to overconfident CEOs to take advantage of their overly positive view of company prospects. 
McKinsey Global Institute (2018) shows that Vietnam is one of 18 economies to be rated as outperformer as evidenced by rapid growth rate of 5% per year in a short term (20 years) from 1996 through 2016. The�revenue-to-GDP�ratio�almost tripled in�just 20 years, from�22%�in 1995�to�64%�in 2016�� far higher than the ratio�in�other emerging�economies, and approaching high-income�country levels. Further, the size of Vietnam stock market has doubled during 2010�2015. Vietnam has moved rapidly from being a socialist-market economy to becoming a deregulated capitalist economy with more and more private enterprises and foreign investment. Vietnam has undergone a period of high growth companies, hence, compensation contracts for overconfident CEOs are maybe in accordance with the strong incentives. Based on the actual situation in Vietnam and earlier research by Gervais et.al (2011) and Humphery et.al (2016), the following is proposed:
H1a: Overconfident CEOs have a positive impact on cash-based compensation.
The inconsistency between the risk appetite of owners and executives has caused executives to act against the goal of maximizing the value of shareholders' interests. In particular, equity compensation has received extensive attention from researchers as an important method for shareholders to motivate executives to take risks and minimize conflicts of interest (Jensen and Meckling, 1976). Therefore, there have been many important studies recently devoted to investigating the implications of the equity executive compensation plan. 
Equity executive compensation has been argued as an effective means to align the interests of managers and shareholders (Devers, Wiseman et al., 2007; Jensen & Meckling, 1976; Wiseman & Gomez-Mejia, 1998). However, a number of other studies show that a noticeable disadvantage is that equity executive compensation can exacerbate the risk for businesses becase CEOs invest heavily in high risk projects to gain more profit (Devers et al., 2008; Devers, Wiseman, et al., 2007; Rajgopal & Shevlin, 2002; Sanders & Hambrick, 2007; Seo & Sharma, 2013; Wiseman & Gomez-Mejia, 1998).
Moreover, Humphery et.al (2016) suggest that overconfident CEOs receive more stock options than their rational counterparts. Gervais et.al (2011) further suggest that high growth potential companies will benefit from overconfident CEOs. Such equity-based compensation contracts with weak incentives maybe have negative impact on company value. In Vietnam, as with its exponential and explosive growth, companies are supposed to consider equity-based compensation contracts with strong incentives in accordance with CEOs� overconfidence. Based on the actual situation in Vietnam and earlier research by Gervais et.al (2011) and Humphery et.al (2016), the following is proposed:
H1b: Overconfident CEOs have a positive impact on equity-based compensation.
2.2 Overconfidence and ownership structure
The actual situation in Vietnam shows that there is a big difference in the compensation structure of state-owned enterprises (SOEs) and non-state enterprises (non-SOEs). In general, executives of SOEs have weaker incentives than executives of non-SOEs. During the "planned economy" period, managers of SOEs were nominated and dismissed by government agencies, and their achievements were assessed by their performance to meet the government' plans rather than the financial efficiency. Therefore, the compensation paid to managers is mainly based on their qualifications and does not relate to business performance. The result is that managers are motivated by political interests and lack of financial incentives to improve business performance. 
By the time SOEs were promoted by the market mechanism and gradually ending the central planning function of the government. In this process, the direct control of the state over SOEs was supplemented by economic incentives and SOE executives were freed up. The separation of state ownership and management of state enterprises became crucial during this period. In particular, executive compensation also underwent a reform in the process of SOE reform. Specifically, the government has issued a number of related policies to encourage compensation based on performance, deciding that compensation will be related to the economic efficiency of SOEs as measured by profits or an index of economic profits. 
However, the current compensation mechanism for representatives at SOEs is still a controversial issue at present. State Capital Investment Corporation (SCIC) is exercising the right to represent capital in 378 enterprises, including 76 listed enterprises. The salary and bonus regime of the leaders of SOEs is still not much different from the previous civil servants' salaries because it is less reliant on the results of production and business activities. So, it is quite low, inequitable and scientific. The current compensation is still in accordance with the State framework and still not competing with non-SOES. SOEs pay compensation to the Management Board in accordance with Circular No. 28/2016/TT-BLDTBXH dated September 1, 2016, guiding the implementation of regulations on labor, compensation and bonus for SOEs. According to this Circular, SOEs provide compensation to the Board of Directors according to the determination based on the number of company managers and the average planned salary (in months) of managers (including: full-time members of the Members' Council or full-time members of the Managing Boards, the general directors, directors, deputy general directors, deputy directors, chief accountants, Head of Control Board and full-time Controllers). If the company fulfills its production, business and profit plan to exceed the plan, every 1% of the realized profit exceeds the planned profit, the average realized salary shall be added a bonus by a maximum of 2%, but not more than 20% of the planned average salary. Conversely, if the company has a lower realized profit than the planned profit, then 1% of the realized profit decreases compared to the planned profit, the average realized salary shall be deducted 1% of the planned average salary. However, the planned average salary of SOEs is much lower than that of non-SOEs. Therefore, according to the regulation, SOEs with the compensation regime are not yet competitive with the market. Instead, CEOs often receive other benefits in the form of job promotion and political power rather than their salary. In addition, SOEs executives have to obey political goals, so they often give lower priority to the company's long-term stocks or high risk projects. So, for an overconfident executive of SOEs, it is likely that they also receive a less incentive package than non-SOEs. Therefore, we propose the following hypothesis:
H2: Overconfident executives in SOEs have lower preference for incentive-intensive pay compared to their non-SOEs.
2.3 Overconfidence and firm performance
The impact of the CEOs on company performance is a topic of great interest to researchers because the CEOs play important role in determining the strategy and performance of the company. Previous studies have provided mixed results on overconfident CEO and corporate performance. Kyle and Wang (1997), Gervais et al. (2010) find that managerial overconfidence will lead to higher company value. Humphery et.al (2016) show that compensation contracts offer to overconfident executives with strong incentives that can add value to the company. Similarly, Vitanova et.al (2014) claim that company value and company performance are significantly higher for firms with CEOs who are overconfident than those with rational CEOs. On the other hand, overconfident executives are found to have a negative impact on company performance due to overinvestment (Heaton et.al, 2002). Roll et.al (1986) show that overconfident CEOs can spur multiple business mergers. Most studies�find that overconfident CEOs lead�to significantly�higher�leveraged firms.�Hackbarth�(2008) for example shows that�overconfident CEOs�underestimate financial costs of distress and thus tend to issue more�debt. The above arguments show that the relationship between overconfident CEOs and company performance is still a controversial issue today.
In this paper, we use Tobin�s Q as a market measure of firm performance. Tobin's Q is widely used in previous studies of overconfident CEOs (such as Malmendier and Tate, 2005; Hirshleifer et al., 2010; Vitanova, 2014). In this study, we calculate Tobin's Q by dividing the market value of assets by the book value of assets. As well as research by Elango et al, 2008; Shim, 2011; Huang et al., 2013, I also use measures of accounting profitability such as return on assets (ROA).
Palomino and Sadrieh (2011) suggest that shareholders can benefit from the overconfident CEOs if they learn about this feature and take advantage of this. According to research by Hirshleifer and Suh (1992), compensation contracts must aim to adjust management's incentives and ensure that managers will make the necessary efforts to make decisions on investment opportunities. And this will be easier for overconfident CEOs than for other rational CEOs. Because overconfident CEOs make the necessary effort to learn about risky projects. And so, hiring overconfident CEOs will become a top priority for businesses. Gervais et.al (2011) argue that overconfident managers are more likely than rational managers in young, risky, growth firms. According to the research of Gervais et.al (2011), executives use their information to consider the prospect of a risky project, they must decide whether to implement the project or replace a safe project. Their research results show that overconfident CEOs create value for the company. First, the overconfident CEOs will follow the investment policy with the optimal risk level. Second, the overconfident CEOs will cause them to make every effort to collect information in order to improve the company's success rate and investment value.
In addition, Humphery et.al (2016) show that overconfident CEOs will increase the company's performance sensitivity (ROA and stock volatility). As such, Humphery et.al (2016) show that strong incentive contracts are provided to overconfident executives to take advantage of their overly positive views on company prospects. We think that the Humphery (2016) study is relevant to the situation in Vietnam, in which compensation contracts can increase a strong motivation with cash compensation to compensate for the overconfident CEOs. In other words, overconfident CEOs tend to receive more sensitive compensation contracts with the company's performance. According to Humphery's research results, ROA has no significant effect on compensation in the reported models. However, when replacing ROA with the overconfidence variable x ROA, the authors find that ROA increases executive compensation. Based on the research results of Humphery (2016) and Gervais (2011), I hypothesize:
Hypothesis H3a: Overconfident CEOs positively impact firm performance.
Hypothesis H3b: For companies with overconfident CEOs, firm performance has a positive impact on executive compensation.
3. Methods
This study utilizes quantitative approach typified by regression models. The data were collected from financial statements, prospectuses, annual and management reports, etc. available for companies listed on Vietnam Stock Exchange. 
3.1 Regression models
3.1.1 To test the impact of overconfidence on cash-based CEO compensation, we run the following regression:
LOGCASH= �0 + �1 OVER it + �2 LOGTOBINQ it + �3 CEO CHAR it + �4 ADMIN it + �5 FIRM CHAR it + � it (1)
3.1.2 To test the impact of overconfidence on equity-based CEO compensation, we run the following regression:
EQUITY = �0 + �1 OVER it + �2 CEO CHAR it + �3 ADMIN it + �4 FIRM CHAR it + � it (2)
3.1.3 To test the impact of overconfidence on firm performance, we run the following regression:
LOGTOBINQ = �0 + �1 OVER it + �2 LOGCASH it + �3 CEO CHAR it + �4 ADMIN it + �5 FIRM CHAR it + � it (3)
To test the impact of Pay-to-performance sensitivity and cash-based CEO compensation, we run the following regression:
LOGCASH= �0 + �1 OVER it * LOGTOBINQ it + �2 CEO CHAR it + �3 ADMIN it + �4 FIRM CHAR it + � it (4)
�: estimation coefficient; i: ith observation; t: year t; �: residuals
We regress Eq. (1), Eq. (2), Eq. (3), Eq. (4) using the techniques of the ordinary least squares model (pooled OLS), tobit model, fixed effects model (FEM), random effects model (REM), and generalized least squares (GLS). Then, we tackle endogeneity by employing GMM.
We control for a variety of factors that the compensation literature suggests are potentially important. At the CEO level, we control for gender, ownership, education, EXPERIENCE and age. At the firm level, we control for age, assets, leverage, stock price volatility and the degree of industry competition the firm faces. At the Business administration characteristics, we control for Level of independent board members, Size of board of directors, State ownership, Foreign ownership, Ownership of the largest shareholder. Appendix describes the control variables in detail along with all other variables we use in the paper.
4. Results
4.1. Descriptive Statistics
     Table 1. Descriptive Statistics
    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      EQUITY |      1350    8667.109      151866          0    4498046
    LOGCASH  |      1350    2.690989    .3507132   1.255273   3.844834
        OVER1|      1350    .1918519    .3939031          0          1
        OVER2|      1350    .0088889    .0938957          0          1
-------------+--------------------------------------------------------
        OVER3|      1142    .9711033    .1675895          0          1
   LOGTOBINQ |      1350    2.551345    .6784819  -5.331707   9.829304
      GENDER |      1350    .9451852    .2277028          0          1
         AGE |      1350    49.62889    7.625553         24         72
  EXPERIENCE |      1350    4.442963    4.181493          0         24
-------------+--------------------------------------------------------
   EDUCATION |      1350    .2525926    .4346602          0          1
   OWNERSHIP |      1350    4.059242    8.281558          0      64.74
     DUALITY |      1350     .282963    .4506055          0          1
       STATE |      1350    .6007407    .4899277          0          1
      FOWNER |      1350    9.898681    15.67511          0        100
-------------+--------------------------------------------------------
CONCENTRATION|      1350    34.48613    20.31649          0      94.99
       BSIZE |      1350    5.374074    1.022393          0         11
 INDEPENDENT |      1350    1.061481    1.237509          0          5
       FSIZE |      1350    5.775368    .6313271  -.3187588   7.790771
     LEVERAGE|      1350    .5041696    .3165174   .0000362   7.661143
-------------+--------------------------------------------------------
        FAGE |      1350       22.96    13.84704          4         72
  VOLATILITY |      1350    4.17e-17    1.888271  -10.58167   22.42833
The summary statistics are reported in Table 1. According to a sample of CEO compensation in cash, the average is 679.33 million VND/year. In particular, the CEOs receiving equity compensation accounted for only about 2.2% of the sample. This proves that the CEOs in Vietnam receive a limited equity compensation. Overconfident CEOs (Over1) is about 19.6% of the sample. Thus, the proportion of CEOs who are the owners/founders/inheritors accounts for a high proportion in Vietnamese businesses. However, Overconfident CEOs (Over2) only accounts for 0.96%, proving that CEOs have a modest ownership rate of shares over 50% in listed companies in Vietnam. With Over3, accounting for 70% of the sample. CEO age ranges between 24 and 72 along with the longest EXPERIENCE of 49 years. The CEO has the highest share ownership ratio of 64.74%, the lowest is 0%, the average is 4.05%. The CEO has the most experience with 24 years, the average is 4.4 years. The highest number of directors on the board is 11, in addition to the maximum level of independence of 5. The highest ownership ratio of the largest shareholder is 94.99%, the lowest is 0%, the average is 34.48%. As such, Vietnamese businesses have a relatively high ownership concentration. In the sample, up to 60% are State-owned enterprises, a relatively high percentage.
Correlation coefficients among the variables are all lower than 0.5, which is not considered significant.
4.2 Overconfidence and compensation
4.2.1 Overconfidence and cash compensation
Table 2. Overconfidence and cash-based compensation

---------------------------------------------------------------------------------------------------
Variables          POOL          FEM             REM            GLS           GMM          Tobit
                 LOGCASH        LOGCASH        LOGCASH        LOGCASH       LOGCASH       LOGCASH
                 (1)         (2)           (3)          (4)         (5)   	(6)   
---------------------------------------------------------------------------------------------------
OVER1            0.00653       0.0570          0.0227        -0.00302        -0.00723     0.00643     
                  (0.28)        (1.39)         (0.70)         (-0.29)         (-0.15)      (0.27)   

OVER2            0.307**      -0.00985         0.0439         0.329***        0.307**     0.307**
                  (2.48)        (-0.09)         (0.42)        (4.68)          (2.23)       (2.51)   

OVER3            -0.0367      -0.0654         -0.0564         -0.0169         0.00336     -0.0368   
                  (-0.67)       (-1.44)         (-1.28)        (-1.15)          (0.09)     (-0.67)   

LOGTOBINQ        0.0974***     0.00533         0.0470***       0.118***       0.117**      0.0974***
                  (6.27)        (0.29)          (2.98)         (11.12)          (2.52)     (6.32)                   

GENDER           -0.0176       0.0809          0.0583         -0.0292        -0.00518      -0.0177   
                  (-0.42)       (1.27)          (1.09)         (-1.50)         (-0.12)     (-0.42)   

AGE              0.000837      0.000941        0.000988        0.000155       0.0000216    0.000854   
                  (0.60)        (0.54)          (0.65)          (0.27)          (0.01)     (0.62)   

EXPERIENCE       -0.00103      0.00254         0.00515**      0.000515         0.00139     -0.00104   
                  (-0.42)       (0.81)          (1.98)          (0.45)          (0.33)     (-0.43)   
EDUCATION        0.0549**      0.127***        0.0996***       0.0408***       0.0495       0.0546** 
                  (2.49)        (4.12)          (3.79)          (4.84)          (1.42)      (2.50)   

OWNERSHIP        -0.00163     -0.00120        -0.00105        -0.00179**      -0.00279     -0.00165   
                  (-0.99)       (-0.64)         (-0.62)         (-2.35)         (-1.14)     (-1.01)   

DUALITY           0.00769     -0.0299         -0.0321          0.0110         -0.00471     0.00754   
                  (0.33)        (-1.26)         (-1.47)          (1.13)         (-0.15)     (0.33)   

STATE            0.00161      -0.0200         -0.0370          0.00873        -0.0252      0.00137   
                  (0.07)        (-0.68)         (-1.47)          (1.02)         (-0.51)     (0.06)   
FOWNER           0.00521***    0.00172*        0.00335***      0.00380***      0.00537***  0.00522***
                  (7.25)        (1.87)          (4.23)          (10.03)         (4.64)      (7.32)   

CONCENTRATION    0.000570      0.000508        0.000361        0.0000934        0.000960   0.000565   
                  (1.02)        (0.63)          (0.55)          (0.46)          (0.76)      (1.02)   

BSIZE            0.0436***    -0.00171          0.0160         0.0344***        0.0355**   0.0441***
                  (4.46)        (-0.14)          (1.49)          (6.21)          (2.35)     (4.54)   

INDEPENDENT      0.0126       -0.00736        -0.00394         0.00773**        0.00979    0.0125   
                  (1.64)        (-0.84)         (-0.50)          (2.45)          (0.67)     (1.64)   

FSIZE            0.155***      0.162***        0.197***        0.157***         0.152***   0.155***
                  (8.03)        (3.59)          (7.01)         (18.51)           (4.05)     (8.10)   

LEVERAGE         -0.0134       -0.0166         -0.0205        -0.00670          -0.0249    -0.0133   
                  (-0.43)       (-0.76)         (-0.96)         (-0.73)          (-0.77)    (-0.43)   

FAGE             -0.000410      0.0162***      0.00168        -0.000197        -0.000207   -0.000411   
                  (-0.59)        (4.31)          (1.28)         (-0.69)         (-0.13)     (-0.59)   

VOLATILITY        0.00560       0.00583**       0.00479*       0.00289          0.00462*    0.00565   
                  (1.19)         (2.35)          (1.94)         (1.36)          (1.73)      (1.21)   

_cons             1.245***      1.268***        1.207***       1.280***         1.561***    1.242***
                   (8.88)        (4.95)          (6.61)         (20.25)          (6.55)     (8.93)   
--------------------------------------------------------------------------------------------------------
N                   1142           1142           1142           1142            1142        1142
--------------------------------------------------------------------------------------------------------
Adj -squared       0.2351                                                                    0.3862

Log likelihood                                                                              -258.93192

Sigma cons                                                                                   0.303***
---------------------------------------------------------------------------------------------------------
Modified Waled                    0.000	       0.000
Prob>chi2              
---------------------------------------------------------------------------------------------------------
Wooldridge                        0.000          0.000
Prob>F  
---------------------------------------------------------------------------------------------------------
Hausman                           0.000          0.000
Prob>chi2
---------------------------------------------------------------------------------------------------------
Hansen test of overid. Restrictions                                                0.110
AR (2)                                                                             0.179
---------------------------------------------------------------------------------------------------------

                Notes: t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01                 
                                                        
Table 2 reports regression results testing the first hypotheses relating CEO overconfidence to cash compensation (Hypotheses H1a). Columns (1), (2), (3), (4) report the results of OLS regressions, while Columns (5) to (6) report the results of GMM and Tobit regressions. We ensure the results are robust by using an Arellano and Bond (1991) model. The dependent variable in all regressions is LOGCASH from year, where all regressors date from year. Year and industry fixed effects are also controlled for each model and standard errors are clustered at the firm level. Overconfidence and other variables are measured as in Eq. (1). 
The regression result of OVER1, OVER3 is not statistically significant, whereas that of overconfident variable 2 (OVER2) suggests the positive impact in all the models of Pooled OLS, GLS, Tobit and GMM. Accordingly, the more overconfident the CEO, the larger the compensation packages they receive. Overconfidence variable ranges from 0 to 1, it leads to about 30.7 % increase in the value of CEO�s incentive compensation. The research results are consistent with the Hypothesis H1a. This result is in line with the hypothesis of Gervais et.al (2011). It also agrees with the current landscape of Vietnam enterprises� advancement processes where such an immature market demanding exponential growth is absolutely opportunity for CEO overconfidence. 
The regression results of most control variables are consistent with previous findings. Company performance (LOGTOBINQ) has a positive impact on CEO cash compensation. This result is highly consistent with earlier findings by, for example, Meeks and Wittington (1975) and Murphy (1985, 1999). For business administration characteristics, our results are consistent with both realities and earlier findings by Murphy (1985), Core et al. (1999), Conyon and He (2012). Specifically, the company�s size, the board of directors� size, and the foreign ownership have positive effects on CEO cash compensation. Large companies with potential finance usually offer higher compensation levels to attract and keep their best managers. The result shows that, companies with foreign ownership rates have very competitive compensation policies for talented CEOs compared to other companies to run their companies in the best way. Those other variables, no evidence is found to the relationship with CEO cash compensation.
4.2.2 Overconfidence and equity compensation
Do the same according to equation (1), however, there is no evidence to prove that overconfident CEOs correlate with equity compensation in Vietnamese companies. This issue may lead to the fact that equity incentives are not yet common in Vietnam. And, Vietnamese companies have not yet taken advantage of the CEOs' behavioral characteristics and equity compensation to increase company value.
4.3 Overconfidence and State-Ownership
Table 3. Overconfidence and cash-based compensation (SOEs)
--------------------------------------------------------------------------------------------              
 Variables      POOL            FEM             REM             GLS              GMM                          
               LOGCASH         LOGCASH        LOGCASH          LOGCASH         LOGCASH   
                 (1)              (2)             (3)             (4)            (5)   
--------------------------------------------------------------------------------------------       

OVER3           0.0394         -0.0800         -0.0412         0.0315          0.0913** 
                 (0.61)         (-1.33)         (-0.72)         (1.37)          (1.99)   

GENDER          0.00822         0.0950          0.0838         0.0221          0.0258   
                 (0.15)         (1.39)          (1.40)          (1.05)          (0.35)   

AGE             0.00108         0.00246         0.00223        0.000315       -0.00127   
                 (0.60)         (0.92)          (1.02)          (0.47)          (-0.35)   

EXPERIENCE      0.00252         0.00240         0.00478*       0.00177         0.00861   
                 (0.90)         (0.71)          (1.66)          (1.34)          (1.48)   

EDUCATION       0.0915***       0.180***        0.142***       0.0825***       0.0263   
                 (3.68)         (4.74)          (4.53)          (7.27)          (0.54)   

OWNERSHIP      -0.00257         0.00437         0.00245       -0.00552***      0.0000517   
                 (-1.01)        (1.40)          (0.89)          (-3.98)         (0.01)   

DUALITY         0.00413         -0.0636**      -0.0601**       0.0113         -0.0342   
                 (0.15)          (-2.13)        (-2.26)         (0.97)         (-0.89)   

FOWNER          0.00437***      0.000424        0.00275**      0.00327***      0.00634***
                 (4.21)          (0.30)         (2.38)          (6.53)          (3.53)   

CONCENTRATION   0.00248***      0.00110         0.000816       0.00101***      0.000568   
                 (3.47)          (0.90)         (0.89)          (2.83)          (0.31)   

BSIZE           0.0465***      -0.0104          0.0104         0.0432***       0.0287   
                 (4.19)          (-0.70)         (0.83)         (6.94)          (1.59)   

INDEPENDENT     0.00655        -0.00131         0.00243        0.0000750      -0.0175   
                 (0.72)          (-0.11)         (0.24)         (0.02)          (-1.03)   

FSIZE           0.100***        0.162**         0.161***       0.103***        0.126***
                 (4.46)          (2.49)          (4.65)         (10.40)          (2.80)   

LEVERAGE        -0.0352        -0.0126         -0.0159        -0.0211         -0.0351   
                 (-1.06)         (-0.56)         (-0.73)        (-1.43)          (-0.65)   

FAGE            -0.00311***     0.0114***     -0.00101        -0.00220***     -0.000627   
                 (-4.10)         (2.71)          (-0.69)         (-5.89)         (-0.35)   

_cons            1.338***       1.207***       1.315***        1.382***        1.715***
                 (8.08)         (3.33)           (5.75)         (18.07)          (5.53)   
--------------------------------------------------------------------------------------------
N                 684            684              684            684             684   
--------------------------------------------------------------------------------------------
Adj -squared      0.2892	

--------------------------------------------------------------------------------------------
Modified Waled                    0.000	        0.000	
Prob>chi2              
--------------------------------------------------------------------------------------------
Wooldridge                        0.00           0.000	
Prob>F  
--------------------------------------------------------------------------------------------
Hausman                           0.0003         0.0003
Prob>chi2
--------------------------------------------------------------------------------------------
Hansen test of overid. Restrictions                                               0.021
AR (2)                                                                            0.014
--------------------------------------------------------------------------------------------
                    
               Notes: t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01   

Table 4. Cash compensation and Overconfidence (non-SOEs)
--------------------------------------------------------------------------------------------
Variables         POOL          FEM             REM              GLS           GMM                          
                 LOGCASH       LOGCASH        LOGCASH          LOGCASH        LOGCASH   
                  (1)           (2)             (3)             (4)            (5)   
 -------------------------------------------------------------------------------------------
OVER1           0.0883**       0.170           0.102          0.0758***       0.0295   
                 (2.32)        (1.52)          (1.60)          (4.44)         (0.40)   

OVER2           0.284*         0.0748          0.165          0.358***        0.177   
                 (1.80)        (0.45)          (1.10)          (4.45)         (0.90)   

OVER3          -0.0626        -0.0289         -0.0392        -0.0913***      -0.0640*  
                 (-0.68)       (-0.37)         (-0.53)         (-3.67)        (-1.65)   
GENDER         -0.0186         0.0479         -0.00547        -0.0326          0.0376   
                 (-0.27)       (0.35)          (-0.06)         (-1.03)        (0.47)   

AGE             0.00147        0.000592        0.000890        0.00279***     -0.00201   
                 (0.67)        (0.20)          (0.36)          (2.92)         (-0.53)   

EXPERIENCE     -0.00977**     -0.000538       -0.000603       -0.00977***      0.00147   
                 (-2.27)       (-0.06)         (-0.11)         (-4.02)        (0.22)   

EDUCATION       0.0691*        0.0587          0.0408          0.0389**        0.0375   
                 (1.69)        (0.91)          (0.82)          (2.34)         (0.61)   

OWNERSHIP      -0.00223       -0.00582*       -0.00509**      -0.00312**      -0.00272   
                 (-0.91)       (-1.89)         (-1.97)         (-2.36)         (-0.81)   

DUALITY         0.0820**       0.0152          0.0312          0.0593***       0.00750   
                 (2.09)         (0.34)          (0.79)          (2.92)          (0.14)   

FOWNER          0.00597***     0.00277*        0.00420***      0.00560***      0.00502***
                 (5.72)         (1.94)          (3.57)          (9.37)          (3.53)   

CONCENTRATION  -0.00163*       0.000949        0.000499       -0.000640        0.000810   
                 (-1.70)        (0.67)          (0.44)          (-1.32)          (0.38)   

BSIZE           0.0520***     -0.00384         0.0179          0.0267***       0.0128   
                 (2.91)         (-0.16)         (0.91)          (2.77)          (0.38)   

INDEPENDENT     0.0172        -0.0134         -0.00912         0.00409         0.0369   
                 (1.38)         (-0.92)         (-0.71)         (0.64)          (1.58)   

FSIZE           0.251***       0.145*          0.248***        0.214***        0.258***
                 (7.18)         (1.90)          (5.23)         (10.72)          (4.22)   

LEVERAGE        0.0350        -0.0745         -0.0166          0.0301**        0.00903   
                 (0.59)         (-0.60)         (-0.24)          (2.32)         (0.10)   

FAGE            0.00534***     0.0230***       0.00686***      0.00271***      0.00448*  
                 (3.77)         (2.66)          (2.73)          (3.90)          (1.66)   

VOLATILITY      0.0103         0.00777*        0.00675         0.00455         0.00216   
                 (1.30)         (1.70)          (1.51)          (1.50)          (0.41)   

_cons           0.763***       1.535***        1.037***        1.180***        1.069***
                 (3.05)         (3.40)          (3.35)          (8.16)          (2.73)   
--------------------------------------------------------------------------------------------
N                 458            458             458             458              458   
--------------------------------------------------------------------------------------------
Adj -squared      0.3022	

---------------------------------------------------------------------------------------------
Modified Waled                   0.000	
Prob>chi2              
---------------------------------------------------------------------------------------------
Wooldridge                       0.000
Prob>F  
---------------------------------------------------------------------------------------------
Hausman                          0.2805         0.2805
Prob>chi2
----------------------------------------------------------------------------------------------
Hansen test of overid. Restrictions                                               0.175
AR (2)                                                                            0.735
----------------------------------------------------------------------------------------------
                             
                 Notes: t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01   

Table 5. Equity compensation and Overconfidence (non-SOEs)
-------------------------------------------------------------------------------------------
Variables          POOL          FEM             REM             GLS              GMM                          
                  EQUITY        EQUITY          EQUITY          EQUITY           EQUITY   
                   (1)           (2)             (3)             (4)              (5)   
--------------------------------------------------------------------------------------------
OVER1             -67554.0**      -2105.4        -68873.0**       -946.7        -36693.6*  
                  (-2.29)         (-0.01)         (-2.26)         (-0.19)         (-1.73)   

OVER2             245895.5**      -50920.7        244100.4**      3581.8         91130.8   
                   (2.02)         (-0.22)          (1.96)         (0.05)          (0.46)   

OVER3              26219.1         556.0          25634.7        -14324.8         3058.7   
                   (0.37)         (0.01)          (0.36)          (-0.94)         (0.21)   

_cons             -329778.7*      -843723.2      -336441.3*       12593.1        -266338.4   
                   (-1.71)         (-1.33)         (-1.70)          (0.40)        (-1.26)   
--------------------------------------------------------------------------------------------
N                  458             458             458             458             458   
--------------------------------------------------------------------------------------------
Adj -squared       0.06    	

--------------------------------------------------------------------------------------------
Modified Waled                    0.000	          0.000
Prob>chi2              
--------------------------------------------------------------------------------------------
Wooldridge                        0.9045	        0.9045

Prob>F  
---------------------------------------------------------------------------------------------
Hausman                           0.8464            0.8464
Prob>chi2
---------------------------------------------------------------------------------------------
Hansen test of overid. Restrictions                                                    0.742
AR (2)                                                                                 0.244
---------------------------------------------------------------------------------------------

                   Notes: t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01                 
Table 3, Table 4, Table 5 report regression results testing the hypotheses relating CEO overconfidence to State-Ownership (Hypotheses H2). Columns (1), (2), (3), (4) report the results of OLS regressions, while Columns (5) reports the results of GMM regression. We ensure the results are robust by using an Arellano and Bond (1991) model. The dependent variable in all regressions is LOGCASH and EQUITY from year, where all regressors date from year. Year and industry fixed effects are also controlled for in each model and standard errors are clustered at the firm level. Overconfidence and other variables are measured as in Eq. (1). We split the samples into SOEs and non-SOEs. For SOEs, the research results show that the Over3 variable has a positive impact on cash compensation. Meanwhile, the research results have not found the evidence of the executive overconfidence affecting the equity compensation of SOEs. For non-SOEs, Overconfidence variable has a negative impact on cash compensation (Over3 in GLS and GMM models) and equity compensation (variable Over1 in Pool, REM, GMM models). Thus, contrary to our prediction, the research results are not consistent with the hypothesis of the study. The research results show that Overconfident executives in non-SOEs have lower preference for incentive-intensive pay compared to their non-SOEs. Hence, the development of corporate governance has been more and more effective in SOEs, they know how to take advantage of the executive's behavioral characteristics to provide appropriate compensation for companies to grow strongly.
4.4 CEO Overconfidence and company Performance
Table 6. CEO Overconfidence and company Performance
--------------------------------------------------------------------------------------------
 Variables       POOL             FEM             REM             GLS            GMM   
                LOGTOBINQ       LOGTOBINQ       LOGTOBINQ       LOGTOBINQ      LOGTOBINQ   
	        (1)               (2)             (3)             (4)           (5)   
--------------------------------------------------------------------------------------------
OVER1            0.0642         -0.0897       -0.0405          0.0427**        0.0641   
                 (1.43)         (-1.20)        (-0.66)          (2.57)          (0.75)   

OVER2            0.718***       0.0871         0.176           0.570***        0.709** 
                  (3.08)         (0.44)         (0.90)          (5.76)          (2.23)   

OVER3            0.279***      -0.0613        -0.00164         0.175***        0.156*  
                  (2.69)         (-0.74)        (-0.02)          (4.13)         (1.68)   

GENDER          -0.178**       -0.0702        -0.0698         -0.0589*        -0.170*  
                  (-2.23)        (-0.60)        (-0.69)          (-1.93)        (-1.75)   

AGE              0.00382       -0.00573*      -0.00249         0.00302***      0.00389   
                  (1.46)         (-1.80)        (-0.86)          (3.13)         (1.32)   

EXPERIENCE      -0.00153       -0.0114**      -0.00147         0.00505***      0.00195   
                  (-0.33)        (-2.00)        (-0.30)          (3.01)         (0.25)   

EDUCATION        0.0228         0.0258         0.0170         -0.0129          0.0449   
                  (0.55)         (0.45)         (0.34)          (-0.93)         (0.92)   

OWNERSHIP       -0.0164***     -0.00774**     -0.00965***      -0.0111***     -0.0172***
                  (-5.34)        (-2.26)        (-3.04)          (-9.29)        (-3.10)   

DUALITY         -0.00626        0.0429        -0.0131          0.00251        -0.0300   
                  (-0.14)        (0.99)         (-0.32)          (0.17)         (-0.56)   

STATE           -0.172***       -0.113**        -0.208***       -0.114***     -0.190***
                  (-4.03)         (-2.10)         (-4.42)         (-7.48)       (-3.62)   

FOWNER           0.00440***     -0.00583***     -0.00108         0.00448***    0.00665** 
                   (3.18)         (-3.48)         (-0.72)         (8.19)        (2.43)   

CONCENTRATION    0.00270**       0.00135         0.00193         0.00213***     0.00319** 
                   (2.56)          (0.93)          (1.55)         (5.90)         (2.29)   

BSIZE           -0.00812         0.0445**        0.0489**        0.00324        0.00589   
                  (-0.44)          (1.98)          (2.42)         (0.50)          (0.19)   

INDEPENDENT     -0.0467***       0.0158          0.00238        -0.0280***     -0.0475   
                  (-3.23)          (0.99)          (0.16)         (-6.02)         (-1.37)   

FSIZE           -0.137***       -0.464***       -0.170***       -0.115***      -0.0934   
                  (-3.66)         (-5.71)         (-3.15)         (-8.22)         (-1.41)   

LEVERAGE        -0.429***       -0.0530          -0.132***      -0.554***      -0.348*  
                  (-7.56)         (-1.32)         (-3.30)        (-18.89)         (-1.80)   

FAGE             0.00166         0.0642***      0.00875***       0.00136***     0.00197   
                  (1.26)          (9.76)          (3.56)          (3.45)          (1.18)   

VOLATILITY       0.0153*         0.0239***       0.0191***       0.0168***      0.0125** 
                  (1.72)          (5.32)          (4.10)          (6.03)          (2.12)   

_cons            2.390***        4.041***        2.984***        2.361***       3.020***
                  (9.03)          (8.87)          (8.81)          (22.48)         (9.27)   
--------------------------------------------------------------------------------------------
N                 1142            1142            1142            1142            1142   
--------------------------------------------------------------------------------------------
Adj -squared      0.1601	

--------------------------------------------------------------------------------------------
Modified Waled                    0.000	        0.000
Prob>chi2              
--------------------------------------------------------------------------------------------
Wooldridge                        0.000          0.000
Prob>F  
--------------------------------------------------------------------------------------------
Hausman                           0.000          0.000
Prob>chi2
--------------------------------------------------------------------------------------------
Hansen test of overid. Restrictions                                                 0.367
AR (2)                                                                              0.202
--------------------------------------------------------------------------------------------

            Notes: t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01   
Table 6 reports regression results testing the hypotheses relating CEO overconfidence to company performance (Hypotheses H3a). Columns (1), (2), (3), (4) report the results of OLS regressions, while Columns (5) reports the results of GMM regression. Year and industry fixed effects are also controlled for in each model and standard errors are clustered at the firm level. We ensure the results are robust to using an Arellano and Bond (1991) model. The dependent variable in all regressions is LOGTOBINQ from year, where all regressors date from year. Overconfidence and other variables are measured as in Eq. (1).
The results show that variables Over 2, Over 3 have significantly positive with firm performance in the POOL, GLS and GMM regressions. The results are consistent with the research hypothesis H3a and consistent with previous studies such as: Kyle and Wang (1997), Gervais et al. (2010), Humphery (2016). As such, Vietnamese businesses with overconfident CEOs can increase their business performance. Other variables such as foreign ownership and the largest percentage of ownership also have a positive effect on firm performance. The research results show the effective impact of large shareholders in the company and foreign shareholders on Vietnamese businesses. While other variables such as gender, state ownership, CEO ownership, leverage have a negative impact on firm performance. The results reinforce the poor performance of businesses with state ownership, CEO ownership and leverage. This is a actual  situation needs to be improved in Vietnam.

Table 7: Pay-to-performance sensitivity 
--------------------------------------------------------------------------------------------
Variables            POOL             FEM             REM             GLS            GMM                    
                    LOGCASH          LOGCASH         LOGCASH        LOGCASH        LOGCASH   
                      (1)             (2)             (3)             (4)            (5)   
--------------------------------------------------------------------------------------------
OVER1* LOGTOBINQ    0.00653         0.0570          0.0227        -0.00302        -0.00262   
                    (0.28)          (1.39)          (0.70)         (-0.29)         (-0.06)   
OVER2* LOGTOBINQ    0.307**        -0.00985          0.0439        0.329***        0.229*  
                    (2.48)          (-0.09)          (0.42)        (4.68)          (1.76)   
OVER3 * LOGTOBINQ  -0.0367         -0.0654         -0.0564        -0.0169         -0.0155   
                    (-0.67)         (-1.44)         (-1.28)        (-1.15)         (-0.44)   
GENDER             -0.0176          0.0809          0.0583        -0.0292         -0.0115   
                    (-0.42)         (1.27)          (1.09)         (-1.50)         (-0.27)   

AGE                 0.000837        0.000941        0.000988       0.000155        0.000187   
                    (0.60)          (0.54)          (0.65)          (0.27)         (0.06)   

EXPERIENCE         -0.00103         0.00254         0.00515**      0.000515       -0.000159   
                    (-0.42)         (0.81)          (1.98)          (0.45)         (-0.04)   

EDUCATION           0.0549**        0.127***       0.0996***       0.0408***       0.0501   
                    (2.49)          (4.12)          (3.79)          (4.84)         (1.42)   

OWNERSHIP          -0.00163        -0.00120        -0.00105       -0.00179**      -0.00102   
                    (-0.99)         (-0.64)         (-0.62)         (-2.35)        (-0.43)   

DUALITY             0.00769        -0.0299         -0.0321         0.0110         -0.00134   
                    (0.33)          (-1.26)         (-1.47)         (1.13)         (-0.04)   

STATE               0.00161        -0.0200         -0.0370         0.00873         -0.0127   
                    (0.07)          (-0.68)         (-1.47)         (1.02)         (-0.26)   

FOWNER              0.00521***      0.00172*        0.00335***     0.00380***      0.00482***
                    (7.25)          (1.87)          (4.23)          (10.03)         (4.15)   

CONCENTRATION       0.000570        0.000508        0.000361       0.0000934       0.000818   
                    (1.02)          (0.63)          (0.55)          (0.46)          (0.64)   

BSIZE               0.0436***      -0.00171         0.0160         0.0344***       0.0301*  
                    (4.46)          (-0.14)          (1.49)         (6.21)          (1.92)   

INDEPENDENT         0.0126         -0.00736        -0.00394        0.00773**        0.0110   
                    (1.64)          (-0.84)         (-0.50)         (2.45)          (0.76)   

FSIZE               0.155***        0.162***        0.197***       0.157***         0.163***
                    (8.03)          (3.59)          (7.01)         (18.51)          (4.38)   

LEVERAGE           -0.0134         -0.0166         -0.0205        -0.00670         -0.00303   
                   (-0.43)          (-0.76)         (-0.96)        (-0.73)          (-0.11)   

FAGE               -0.000410        0.0162***      0.00168        -0.000197        -0.000601   
                   (-0.59)          (4.31)          (1.28)         (-0.69)          (-0.38)   

_cons              1.245***         1.268***       1.207***        1.280***         1.281***
                   (8.88)           (4.95)          (6.61)         (20.25)          (4.72)   
--------------------------------------------------------------------------------------------
N                  1142            1142            1142            1142           1142   
--------------------------------------------------------------------------------------------
Adj -squared       0.2351    	

--------------------------------------------------------------------------------------------
Hansen test of overid. Restrictions                                                 0.036
AR (2)                                                                              0.157
--------------------------------------------------------------------------------------------
             Notes: t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01   

                
Table 7 reports regression results testing the hypotheses relating pay-to-performance sensitivity to cash compensation (Hypotheses H3b). Columns (1), (2), (3), (4) report the results of OLS regressions, while Columns (5) reports the results of GMM regressions. Year and industry fixed effects are also controlled for each model and standard errors are clustered at the firm level. We ensure the results are robust to using an Arellano and Bond (1991) model. The dependent variable in all regressions is LOGCASH from year, where all regressors date from year. Overconfidence and other variables are measured as in Eq. (1).
To test our hypothesis (H3b), we include the interaction term of Overconfidence and LOGTOBIN�S Q in the regression model. The coefficient of OVER2 * LOGTOBIN�S Q is significantly positive in the POOL, GLS and GMM regressions (at the 1% level). The result shows that the companies with overconfident CEOs, company performance has a positive impact on cash compensation. The research results are consistent with the Hypothesis H3b. This proves that Vietnamese businesses have a strong incentive policy for executives who are overconfident when they achieve company performance. 
5. Additional robustness tests
In this section, we conduct a series of robustness checks of our main findings and perform an additional test to examine the source of better company performance with overconfident CEOs.  
The results of multicollinearity check using variance inflation factor (VIF) suggest its value smaller than 10 and thus, there is no existing multicollinearity�in the data. We apply modified Wald test and Wooldridge test for testing the problem of heteroskedasticity and autocorrelation. And then we employ generalized least squares (GLS) technique to address the issue. Buck, Liu and Skovoroda (2008)�find that the company's performance and executive compensation interacted with each other so there was the possibility of an endogenous.� So, we have to deal with GMM, we implement the xtabond2 command with robust twostep option to handle these problems. 
In addition, CEOs may have inside information about future stock prices, thereby they usually increa their equity holdings. To address this concern, Malmendier and Tate (2005) classify CEOs as overconfident based on their first five years in the sample. But they eliminate the five years from the regressions because any insider information to affect corporate decisions. Due to the limited time of the data sample, we could not calculate the variable (Over3) after subtracting the first 5-year period such as Malmendier and Tate (2005). Instead, to check the quality of the sample, we recalculated the overconfidence variable (Over3) with the following sample after subtracting the first 1-year period of the CEO's current position. After running regressions without the first year, we find that our main results are still unchanged (see Table 8, Table 9 in the appendix), implying that our results are not driven by insider information. Specifically, the overconfident CEOs have a positive impact on cash-based compensation in SOEs (in GMM model) and the overconfident CEOs have a positive impact on company performance (GLS and GMM models). 
Finally, an alternative way to investigate whether overconfident CEOs receive strong incentives, as predicted by our theoretical analysis, is to explore the pay-to-performance sensitivity. Thus, we assess how CEO overconfidence impacts the relation between the current level of compensation and the previous year�s performance as measured by ROA. In Table 10, we find that CEO overconfidence (Over2) has a positive impact on performance (ROA) in the GLS and GMM regressions. This is consistent with our prediction and supports the general idea that overconfident CEOs tend to receive compensation contracts that are more sensitive to company performance. 
6. Conclusion
This study contributes a novel approach to formulating efficient executive compensation schemes in Vietnam. Thus, our paper adds to the growing literature on Asian behavioral finance, which is still considered spars. 
First, CEO overconfidence is found to positively affect on cash-based compensation. Our findings suggest that incentive-heavy compensation contracts are offered to overconfident CEOs to take advantage of their overly positive view of company prospects. Accordingly, overconfidence ranges from 0 to 1, it leads to about 30.7 % increase in the value of CEO�s cash compensation. Thus, besides a combination of CEO characteristics as well as firm and business administration characteristics, compensation schemes should also underline overconfidence as a major component to exploit CEO advantages for optimal firm value. It also agrees with the current landscape of Vietnam enterprises� advancement processes where such an immature market demanding exponential growth is absolutely opportunity for CEO overconfidence. However, there is no evidence to prove that overconfident CEOs correlate with equity compensation in Vietnamese enterprises. This issue may lead to the fact that equity incentives are not yet common in Vietnam. And, Vietnamese companies have not yet taken advantage of the CEOs' behavioral characteristics and equity compensation to increase company value. So, this study sheds some light on policy in Vietnam companies that they should improve their compensation contracts with equity incentives for overconfident CEOs.
Second, our findings serve as additional evidence on the influence of state-ownership on executive compensation structures. For SOEs, we show that overconfident CEOs have a positive impact on cash-based compensation. Meanwhile, we have not found evidence of the executive overconfidence affecting the equity compensation of SOEs. For non-SOEs, overconfident CEOs have a negative impact on cash-based compensation and equity compensation. So, it can be said that overconfident executives in non-SOEs have lower preference for incentive-intensive pay compared to SOEs. Hence, the development of corporate governance has been more and more effective in SOEs, they know how to take advantage of the executive's behavioral characteristics to provide appropriate compensation for companies to grow strongly.
Finally, the results show that overconfident CEOs have significantly positive with firm performance. As such, Vietnamese businesses with overconfident CEOs can increase their business performance. In addition, our findings suggest that the companies with overconfident CEOs, company performance has a positive impact on cash-based compensation. This proves that Vietnamese businesses have a strong incentive policy for overconfident executives when they achieve firm performance. Our results suggest that the companies in Vietnam should provide compensation contracts that match personal behavior traits such as overconfidence to achieve desirable business performance.
Acknowledgments:
This research is funded by University of Economics Ho Chi Minh City, Vietnam.
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h�C0Jjh�C0JUh�
6jh�
6Uh.(�h�N�OJPJQJ\�Uh.(�h!bOJPJQJ\�jh.(�h!bOJQJUh.(�h!bOJQJjh.(�h!bUh.(�h!b3#FuG�G:I�IoJ�K�L�MJ����k����G�H�J�K�M�N�P�Q�S�T�]������������������������������&`#$gd^$�x�xa$gd�=�$�x�xa$gd!bation and risk taking. Journal of Accounting and Economics, 33,  145-171.
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