Signals of Market and Firm Characteristics and Asymmetric Information

Signals of Market and Firm Characteristics and Asymmetric Information Phan Bui Gia Thuy Nguyen Tat Thanh University, Vietnam Nguyen Tran Phuc Ngo Vi Trong Banking University HCMC, Vietnam Abstract This study aims to determine the effects of market and firm characteristics on asymmetric information This study finds that signals of the market and firm characteristics are likely to significantly explain to proxies for asymmetric information measured by two econometric model, trade-indicator model and serial covariance model Analyzing 174 firms listed on HOSE from 2010 to 2016 with 1102 observations, this study finds that liquidity of stock and debt financing inversely and significantly impact on adverse selection component Interestedly, unlike the extant literature, a negative relationship between volatility and adverse selection problem is found In addition, this study also confirms a high level of growth opportunity and policy on widening price limit range have the positive and statistically significant effects on private information Keywords: Asymmetric information, adverse selection component, liquidity, volatility, price limit JEL Classifications: D82; G18 Introduction Asymmetric information is a form of market failure, causing shocks to interest rates and banking crises (Mishkin, 1990) It is also the cause of the financial crisis in the US from 2007 to 2009, which has frozen the economy for years not only in the United States but also in other countries (Ashcraft and Schuermann, 2008) For the stock market, information asymmetry refers to informed investors have superior information related to firm business activities while uninformed investors not (Chae, 2005) Informed investors use this particular information to gain on the losses of uninformed investors These losses are adverse selection costs that uninformed investors have to burden The results of factors affecting information asymmetry have not reached a common consensus in some studies In particular, some studies find a positive relationship between growth opportunities and asymmetric information (Hegde and McDermott, 2004; Fosu et al., 2016), while Van Ness et al (2001) find no this relationship In addition, the debt ratio negatively effects on asymmetric information (Elbadry et al., 2015) while this effect is unclear (Cai et al., 2006) Furthermore, policy on widening the price limit range is likely to make the price move quickly to the equilibrium point (Kim and Rhee, 1997) and attract more investors joining in the stock market (Anshuman and Subrahmanyam, 1999), but increase information risk (Berkman and Lee, 2002) Moreover, price limit range is not an effective tool to limit asymmetric information (Chan et al., 2005; Kim and Yang, 2008) In Vietnam, the research mentioned above is highly rare Therefore, this study aims to identify determinants of asymmetric information between informed and uninformed investors when performing stock 359 trading This study is motivated by the empirical work of Van Ness et al (2001), Hegde and McDermott (2004), Kim and Yang (2008), Narayan et al (2015) and Fosu et al (2016) who explore the effects of asymmetric information among different investors on the specific stock market The results of this study show that there is a negative and statistically significant effect of liquidity of stock, volatility and debt financing on adverse selection component, while growth opportunity and policy on adjusting price limit range positively impact on information risk Our results are very useful for policymakers to consider whether to adjust policy on price limit range and for stakeholders to predict information risk of stock trading The remainder of the paper is organized as follows In Section 2, this study discusses the extant literature on asymmetric information and factors of market and firm characteristics related to information risk Section presents the data sample and discusses the methods used in our empirical estimator Section presents the empirical results, while Section discusses these results Section emphasizes our main findings and concludes the paper Literature review 2.1 Asymmetric information Asymmetric information reflects an object or group of objects that own superior firm-specific information related to future public disclosures not available to uninformed (Chae, 2005), and it arises from private information between informed and liquidity investors (Barakat et al., 2014) Informed traders make a profit from performing securities transactions on private information that uninformed ones not, which is attributed to adverse selection problem (Copeland and Galai, 1983; Glosten and Milgrom, 1985) When the stock market has a severe adverse selection problem between different investors, uninformed investors could leave the market According to signaling theory and market microstructure theory, specific signals of market and firm characteristics could predict the level of asymmetric information in several ways For example, frequent trading of stock and financial structure would negatively impact on adverse selection problem (Acker et al., 2002; Degryse and Jong, 2006), while volatile stock price and growth opportunity are likely to positively effect on adverse selection risk (Chung et al., 2010; Fosu et al., 2016) However, many empirical studies have shown the mixed results, consistent with the literature and inconsistent, because these results depend on the characteristics of each country, the period of study and the research methodology 2.2 Determinants of asymmetric information Based on the signaling theory, market microstructure theory and empirical research involved, the factors of liquidity, volatility, growth opportunity, debt financing and price limit affecting asymmetric information are reviewed below 2.2.1 Liquidity of stock and asymmetric information Trading volume is likely to serve as a proxy for liquidity of a stock during a particular trading period Shares with large and frequent trading quantities are considered more liquid; otherwise, they are considered less liquid Chae (2005) has shown that there is an amount of significant asymmetric information between informed investors and uniformed before the period of earning disclosure Uninformed investors often choose to limit their trading activities except for the urgent need for liquidity Acker et al (2002) show that stocks with high trading volume and frequency would have lower adverse selection component than those with less liquid Draper and Paudyal (2008) also found that stock liquidity was negatively correlated with asymmetric information Therefore, the hypothesis is as follows: H1: Trading volume has a negative effect on asymmetric information 360 2.2.2 Volatility of stock price and asymmetric information Volatility of stock price usually increases during the upward or downward market trend and is considered a risk factor when trading The more private information reflects the stock price, the larger its volatility would become (Bhushan, 1989; Moyer et al., 1989) According to Wang (1993), there are different levels of stock price volatility because of private information held by investors, and the higher changing in stock price, the greater profit informed traders could make Therefore, the volatility of stock price positively relates to asymmetric information Supporting these perspectives, Chung et al (2010) and Barakat et al (2014) find that there is a positive impact of volatility on information risk On the other hand, the positive effect of volatility on asymmetric information is inconsistent with other empirical studies For instance, Li and Wu (2006) find no explanation of volatility leading to adverse selection component The authors suppose that volatility of stock price includes a noise signal that dissociates from asymmetric information Moreover, Chordia et al (2001) and Narayan et al (2015) find a negative effect of volatility on spread It can be seen that there are still mixed results suggesting that volatility could explain private information Many studies, however, have confirmed that price volatility positively effects on information problem This discussion leads to the following hypothesis: H2: There is a positive relationship between volatility of stock price and asymmetric information 2.2.3 Growth opportunity and asymmetric information Companies with high growth opportunities would have a high level of asymmetric information (Myers and Majluf, 1984) There are two approaches to explain this phenomenon, including the information approach and the behavioral finance Drawing from the information approach, in companies with high growth opportunities, inside managers have private information about new investment projects or cash flows from assets in place while outside investors could not afford to observe the behavior of the manager (Smith and Watts, 1992) In addition, drawing from the behavioral finance approach, shareholders who invest in companies with high growth opportunities are often overconfident and tend to overreact to vague or unverifiable information (Daniel and Titman, 2006) Consistent with these perspectives, Hegde and McDermott (2004), Fosu et al (2016) find that the companies with a high growth opportunity positively relate to the asymmetric information Therefore, according to the information approach, behavioral finance approach and related empirical research, the hypothesis of the relationship between growth opportunities and asymmetric information is as follows: H3: There is a positive relationship between growth opportunity and asymmetric information 2.2.4 Debt and asymmetric information Studying the relationship between debt ratio and asymmetric information opens up different perspectives According to Stulz (1990), many companies with poor performance often use debt financing to offset their operating cash flow Moreover, the excessive use of debt could incur financial costs for the company The debt ratio, hence, is likely to diminish company performance rather than asymmetric information For this argument, Hegde and McDermott (2004), Cai et al (2006) find no relationship between debt ratio and asymmetric information However, agency theory, signaling theory and pecking order theory underline a positive outlook of debt financing Debt financing conveys a positive signal to shareholders and creditors about the effectiveness of monitoring the behavior of management (Jensen and Meckling, 1976), improving transparent disclosure (Ross, 1977; Jensen, 1986) and declining the managerial discretion and private information (Degryse and Jong, 2006) It also positively signals to investors about a future value-for-money perspective (Myers and Majluf, 1984) In addition, debt financing is useful for firms to enhance their performance by exploiting tax shield effectively This discussion leads to the following hypothesis: 361 H4: There is a negative relationship between debt and asymmetric information 2.3.5 Price limit and asymmetric information Price limit set the maximum permitted price variation around a base price Changing the price limit range will have different impacts on the stock market Specifically, narrowing price limit range may reduce the volatility of stock price (Chen, 1993, Lee and Kim, 1995), assist the index of stock market not fall deeper during the crisis (Rhee and Chang, 1993) and restrict price manipulation in countries with high level of corruption and low-quality public enforcement (Kim et al., 2010) However, the disadvantage of the price limit is that the stock price movement slows to equilibrium (Kim and Rhee, 1997) Extending the price limit range is useful for attracting more investors, but its negative aspect causes adverse selection cost (Anshuman and Subrahmanyam, 1999) Lee and Chou (2004) study intraday price limit on the TSE and find that the firms whose stock price hits a ceiling of price limit would have a higher level of asymmetric information than the firms whose stock price fluctuates within the limit The price limits have been adjusted many times in Vietnam’s stock market In the period 2010-2016, the price limit range of the firms listed on HOSE was adjusted an increase from 5% to 7% since January 15, 2013 Widening the price limit range could increase the volatility of stock price and decline trading volume (Berkman and Lee, 2002) This discussion leads to the following hypothesis: H5: The firms after the enactment of the legislation increasing the price limit range would have the higher level of asymmetric information than those before this enactment Data and methodology 3.1 Data collection This study collected statistical data of trading prices and orders of companies listed on HOSE during the first quarter from January to March 31 in the period 2010-2016 to measure asymmetric information, stock liquidity, volatility and growth opportunity The first quarter is the time when the listed firms disclose information about annual reports and audited financial statements at the end of year related to firm performance, and there is a significant information risk between insider and outsider or between informed investors and uninformed investors In addition, the sample does not include banks, financial institutions, insurance companies and investment funds because of specific activities as well as specific legal regulations for these organizations The reason for choosing this period is that VNIndex has a negligible fluctuation Figure below reveals this index which is likely to present the market volatility Figure below illustrates VN-Index in the period 2007-2016 Obviously, VN-Index declined dramatically, from 943 points in December 2007 to 261 points in March 2009 At the beginning of April 2009, the Vietnam Prime Minister issued Decision No 443/QĐ-TTg on giving interest rate with an interest rate of 4%/year, so the VN-Index rose again from 263 points in March 2009 to 589 points in October 2009 However, after this period, October 2009 until March 2016, VN-Index had a tendency to move sideways within the resistance range of 400-600 points This evidence shows that although Vietnam has overcome the global financial crisis, the Vietnam’s stock market is still in a long period waiting for a signal of real prosperity 362 1000 800 600 400 200 Dec-16 Jun-16 Dec-15 Jun-15 Dec-14 Jun-14 Dec-13 Jun-13 Dec-12 Jun-12 Dec-11 Jun-11 Dec-10 Jun-10 Dec-09 Jun-09 Dec-08 Jun-08 Dec-07 VNIndex Fig.1 VNIndex during a period from December 2007 to December 2016 3.2 Measuring asymmetric information This study measures the adverse selection component for each stock as a proxy for asymmetric information To so, this study uses the model of George, Kaul and Nimalendran (1991) (hereafter GKN) and Kim and Ogden (1996) (hereafter KO) to accommodate transactions data These two models are discussed briefly below 3.2.1 George, Kaul and Nimalendran (1991) Trade-indicator GKN model assumes the transaction price and true price of the stock is determined by the following equation: Pit = Mit + πi (Sqi/2)Qit (1) Where Pt is the transaction price; Mt is the true price; Qt is the trade indicator variable; Sq is bid ask spread; π is the proportion of the order processing component in spead, and (1– π) is the proportion of the adverse selection component Take the differential Equation (1) given by the new equation as follows: ∆Pit = ∆Mit + πi (Sqi/2)∆Qit (2) Let RDTM,it = ∆Pit – ∆Mit denote the difference between the change in the transaction price and the change in the bid price, Equation (2) becomes: RDTM,it = πi (Sqi/2)[Qit – Qit–1] (3) Equation (3) can be written as a regression equation as follows: 2RDTM,it = a0 + a1 (Sqi)[Qit – Qit–1] + εit (4) The GKN model uses the regression Equation (4) to estimate the coefficient a1 = π as the order processing cost component Therefore, the average adverse selection component of the stocks is calculated as – a1 Next, let xit = (Sqi)[Qit – Qit–1] and yit = 2RDTM,it correspond to each stock i, the average adverse selection component of the stock i, ASCi,GKN is estimated according to the formula below: T ASCi ,GKN   a1,i    (x it t 1  x)( yit  y ) (5) T  ( xit  x)2 t 1 363 This study measures the variables in regression equation (4) as follows: RDTM,it = ∆Pit – ∆Mit is the difference between the change in the closing price at the end of the day (∆Pit) and the change in the mean of the bid price and ask price or change in the midpoint (∆Mit); Qit is a trading indicator variable determined by Lee and Ready (1991), Qit = +1 if the closing price is higher than the midpoint; otherwise, Qit = –1; Sqi is the difference between ceiling price and floor price 3.2.2 Kim and Ogden (1996) KO model adjusts and modifies the GKN model under serial covariance Accordingly, the regression equation estimated by KO model has the following form: ̅ + εi SKO = β0 + β1√𝑆𝑞𝑖 i Where SKO =2 i (6) Cov( RDTM ,it , RDTM ,it 1 ) is the spread in the KO model, with RDTM,it = ∆Pit – ∆Mit is the difference between the change in transaction price (∆Pit) and the change in the midpoint (∆Mit); S̅ qi = T  Sqit T t 1 is the mean of the sum of the squared spreads, where Sqit is the spread changing over time; β1 is the regression coefficient as a proxy for the order processing cost component, π The regression Equation (6) is used by the KO model to estimate the coefficient β1 = π that is the order processing cost component Therefore, the average adverse selection component of the stocks is calculated as – β Next, Kim and Ogden (1996) proposed a convenient formula for estimating asymptotic average adverse selection for each stock in the KO model Accordingly, ASCi,KO is estimated by the following formula: ASCi , KO   This study Cov( RDTM ,it , RDTM ,it 1 ) measure (7) T  Sqit T t 1 the variables in the regression Equation (7) as follows: SKO i = Cov( RDTM ,it , RDTM ,it 1 ) where RDTM,it = ∆Pit – ∆Mit is the difference between the change in the closing price at the end of the day (∆Pit) and the change in the midpoint (∆Mit); Sqi is the difference between ceiling price and floor price 3.3 Econometric model Based on the studies involving the factors affecting information asymmetry on the stock market according to theory (Bagehot, 1971; Copeland and Galai, 1983; Glosten and Milgrom, 1985) and according to empirical research (Van Ness et al., 2001; Acker et al., 2002; Hegde and McDermott, 2004; Draper and Paudyal, 2008; Fosu et al., 2016), this study formulates the following regression equation: ASCit     Liquidityit   Volatilityit   Growthit   Debtit   Policyit      ControlVarit   it   (8) Equation (8) describes explanatory variables that are likely to affect the asymmetric information corresponding to the expected mark and the control variables that improve the effectiveness of the regression model The left side of the Equation (8) is a dependent variable which serves as a proxy for the asymmetric information measured by ASCGKN and ASCKO The right hand side of the Equation consists of the following explanatory variables: Liquidity are the factors of the stock liquidity including frequent trading (Turover) and liquidity trading (Depth); Volatility is the volatile stock price; Growth are the factors of firm growth including growth opportunity (TobinQ) and level of growth opportunity (Opp); Debt are the factors of debt financing 364 including total of debt ratio (DebtRatio) and bank loan ratio (BankRatio); and Policy is the policy on adjusting price limit range from 5% to 7% and ControlVar are the control variables including the firm size (Asset) and the number of years since listing (ListYear) Measurement of research variables is detailed in Table below Table Definition and measurement of variables Variable ASC Definition Adverse selection component Turnover Frequent trading Depth Liquidity trading Volatility TobinQ Volatile stock price Growth opportunity Opp Level of growth opportunity DebtRatio BankRatio Total debt ratio Bank loan ratio Policy Government’s policy on adjusting price limit range from 5% to 7% Asset ListYear Firm size Number of years since listing Measurement ASCGKN and ASCKO estimated from trade-indicator GKN model and KO model The average of traded shares divided by outstanding shares The average of shares available at both the best bid and ask prices divided by number of outstanding shares Standard deviation of the bid and ask midpoint The sum of market value of stock and total debt divided by total assets Opp = if TobinQ > 1, high growth opportunity Opp = if TobinQ < 1, low growth opportunity Ratio of total debt to total assets The sum of short-term bank loan and long-term bank loan divided by total assets Policy = if years of study belong to the period 20132016 (the price limit range is 7%) Policy = if years of study belong to the period 20102012 (the price limit range is 5%) Natural logarithm of total assets Natural logarithm of number of years since listing Results 4.1 Characteristics of research sample Table presents the mean values of ASCGKN in Panel A and ASCKO in Panel B under < ASC < from 2010 to 2016 ASCGKN is in the range of (52.4%; 73.3%) while ASCKO is in the range of (50.1%, 68.7%) Generally, ASCGKN and ASCKO have the same trend Table The average adverse selection component of individual firm over the years Year 2016 2015 2014 2013 2012 2011 2010 Panel A ASC using the model of George, Kaul and Nimalendran (1991) ASCGKN Number of firms 69.5% 73.3% 69.8% 63.5% 52.4% 60.9% 56.4% 163 168 169 170 163 152 117 Panel B ASC using the model of Kim and Ogden (1996) ASCKO Number of firms 63.7% 68.7% 64.4% 60.1% 50.1% 57.2% 55.4% 163 168 169 170 164 154 117 Note: ASCGKN and ASCKO are referred to trade-indicator GKN model and KO model To fit sample size between two variables of ASC and variables of market and firm characteristics and arm to estimate the regression equation, the final research sample consists of 174 firms with a total of 1102 observations for the period 2010-2016 Table below presents the statistics of the study variables 365 Table Descriptive statistics of variables Variables ASCGKN ASCKO Turnover Depth Volatility TobinQ DebtRatio BankRatio ListYear Asset Mean 64.1% 60.2% 0.3% 0.1% 1.96 1.18 46.5% 23.3% 3,063,766 Std Dev 12.7% 13.5% 0.5% 0.1% 2.22 0.53 20.9% 19.2% 8,426,724 Min 25.4% 15.5% 0.0% 0.0% 0.12 0.37 0.3% 0.0% 117,170 Max 92.3% 91.7% 5.5% 1.2% 23.72 5.85 97.1% 81.7% 16 145,000,000 ASCGKN and ASCKO is estimated from trade-indicator GKN model and KO model; Turnover: the average of traded shares divided by outstanding shares; Depth: the average of shares available at both the best bid and ask prices divided by number of outstanding shares; Volatility: standard deviation of the midpoint; TobinQ: the sum of market value of stock and total debt divided by total assets; DebtRatio: ratio of total debt to total assets; BankRatio: the sum of short-term bank loan and long-term bank loan divided by total assets; ListYear: number of years since listing; Asset: total assets (mil vnd) Next, the mean value of ASCGKN and ASCKO for different levels of growth opportunity from 2010 to 2016 illustrates in Figure below ASCGKN ASCKO 2016 2016 2015 2015 2014 2014 2013 2013 2012 2012 2011 2011 2010 2010 0.0% 20.0% 40.0% GKN (Opp = 1) 60.0% 0.0% 80.0% GKN (Opp = 0) 20.0% KO (Opp = 1) 40.0% 60.0% 80.0% KO (Opp = 0) Fig Adverse selection component at high and low growth opportunity during 2010-2016 Figure shows that ASCGKN and ASCKO at a high level of growth opportunity (Opp = 1) are almost greater than those at a low level of growth opportunity (Opp = 0) This statistical data shows that the higher level of growth opportunity that company has the more severe asymmetric information is Another important factor which is likely to affect asymmetric information is price limit Widening price limit range from 5% to 7% according to Regulation No 01/2013/QĐ-SGDHCM in Vietnam may affect the private information Table below presents the mean values of ASCGKN, ASCKO, Turnover and Depth between the period 2013-2016 with price limit range of 7% (Policy = 1) and the period 2010-2012 with price limit range of 5% (Policy = 0) 366 Table Factors of study under different price limit range Variables Policy = (n = 432) Policy = (n = 670) Satterthwaite-Welch t-test Probability ASCGKN ASCKO Turnover Depth 56.47% 69.01% -18.87 0.000 54.04% 64.23% -13.40 0.000 0.30% 0.27% 1.15 0.251 0.07% 0.04% 4.72 0.000 *** *** *** Note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level ASCGKN and ASCKO is estimated from trade-indicator GKN model and KO model; Turnover: the average of traded shares divided by outstanding shares; Depth: the average of shares available at both the best bid and ask prices divided by number of outstanding shares; Policy: policy on adjusting price limit range, Policy = if years of study belong to the period 2013-2016 (price limit range of 7%), and Policy = if years of study belong to the period 2010-2012 (price limit range of 5%) The Satterthwaite-Welch t-test statistic in Table shows that the mean values of ASCGKN and ASCKO under Policy = are smaller than those under Policy = In addition, the mean value of Depth under Policy = is greater than that under Policy = (t = 4.72, p = 0.00) while no significant changing in the mean value of Turnover between two periods is found (t = 1.15, p = 0.25) These results show that the policy adjusting the price fluctuation range from 5% in the period 2010-2012 to 7% in the period 2013-2016 is likely to cause no changing in frequency trading noticeably, reducing the volume of stocks at the best bid and ask prices and increasing adverse selection component significantly In other words, this adjustment policy could not only reduce the liquidity of stock but also increase asymmetric information 4.2 Regression results Before estimating Equation (8), the study estimates the pairwise correlation coefficients between the determinants and asymmetric information The matrix of correlations between the studied variables is shown in Table below Table Correlation matrix Variables (1) ASCGKN (2) ASCKO (3) Turnover (4) Volatility (5) TobinQ (6) DebtRatio (7) Asset (8) ListYear (1) 0.83 -0.07 -0.08 0.19 -0.12 0.18 0.29 (2) *** ** *** *** *** *** *** -0.01 -0.02 0.20 -0.08 0.18 0.20 (3) *** ** *** *** 0.07 -0.08 0.07 -0.05 -0.05 (4) ** ** ** * 0.44 -0.09 0.14 -0.06 (5) *** *** *** * -0.23 0.20 -0.07 (6) *** *** ** 0.12 -0.08 (7) *** *** 0.09 (8) *** VIF 1.03 1.27 1.38 1.11 1.10 1.03 Note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level ASCGKN and ASCKO is estimated from trade-indicator GKN model and KO model; Turnover: the average of traded shares divided by outstanding shares; Volatility: standard deviation of the midpoint; TobinQ: the sum of market value of stock and total debt divided by total assets; DebtRatio: ratio of total debt to total assets; Asset: natural logarithm of total assets; ListYear: natural logarithm of number of years since listing Table illustrates that there is a significantly strong correlation coefficient between ASCGKN and ASCKO (r = 0.83, p < 1%) Considering a correlation between the explanatory variable and dependent one, TobinQ reveals a signal of positive and statistically significant correlation with ASCGKN as well as ASCKO while DebtRatio shows the opposite In addition, a negative and statistically significant correlation is found between Turnover and 367 Volatility and ASCGKN Moreover, the maximum VIF in Table is 1.38, showing that the problem of multicollinearity is not severe in a regression model Table below shows the results of regression from Equation (8) The results of F test, Breusch-Pagan test and Hausman test from Column [1] to [3] in Table recommend FEM method for performing regression equation Although the Hausman-test result in Column [4] recommends REM method (χ2 = 12.59; p = 0.08 > 0.05), the regression results using the FEM method are not significantly different compared to those using the REM method Therefore, the FEM method is used to estimate regression equation Table Determinants of ASC estimated from GKN model Variables Constant Turnover Depth Volatility TobinQ Opp DebtRatio BankRatio Policy Asset ListYear Observation R2 adj Durbin-Watson F Test Breusch-Pagan Test Hausman Test ASCGKN [1] -0.2418 -2.8761 -0.0086 0.0587 -0.1881 0.1149 0.0621 -0.0065 1102 48.8% 2.11 2.74 863.88 25.95 *** *** *** *** *** *** *** *** *** [2] -0.0651 -3.0536 -0.0080 0.0424 -0.1487 0.1184 0.0486 -0.0048 1102 47.9% 2.11 2.92 789.35 14.48 *** *** *** *** *** *** *** *** ** [3] -0.1957 -20.0468 -0.0086 0.0572 -0.1730 0.1118 0.0587 -0.0061 1102 49.0% 2.13 2.74 863.73 22.45 *** *** *** *** *** *** *** *** *** [4] -0.0278 -20.5509 -0.0080 0.0396 -0.1374 0.1152 0.0461 -0.0048 1102 48.0% 2.12 2.88 928.00 12.59 *** *** *** *** *** *** *** *** * Note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level The t-statistics are based on robust standard errors ASCGKN is estimated from trade-indicator GKN model; Turnover: the average of traded shares divided by outstanding shares; Depth: the average of shares available at both the best bid and ask prices divided by number of outstanding shares; Volatility: standard deviation of the midpoint; TobinQ: the sum of market value of stock and total debt divided by total assets; Opp: growth opportunity, Opp = if TobinQ > high growth opportunity; otherwise, Opp = 0; DebtRatio: ratio of total debt to total assets; BankRatio: the sum of shortterm bank loan and long-term bank loan divided by total assets; Policy: policy on adjusting price limit range, Policy = if years of study belong to the period 2013-2016 (price limit range of 7%), and Policy = if years of study belong to the period 2010-2012 (price limit range of 5%); Asset: natural logarithm of total assets; ListYear: natural logarithm of number of years since listing Table reveals the regression results in Columns Columns [1] and [2] present the relationship between Turnover and ASCGKN while Columns [3] and [4] consider the effect of Depth on ASCGKN In addition, the regression coefficients on TobinQ and DebtRatio are presented in Columns [1] and [3] while Opp and BankRatio are shown in Columns [2] and [4] Finally, the regression results of the Volatility, Policy and control variables including Asset and ListYear affecting ASCGKN were presented in all Columns It can be seen that the model has a relatively high degree of relevance by virtue of the adjusted R2 from Column [1] to [4] with 47.5%, 46.6%, 47.6% and 46.7% respectively The results show that two liquidity factors have a negative and significant effect on asymmetric information Specifically, the coefficients on Turnover (Columns [1] and [2]) and Depth (Columns [3] and [4]) are negative and statistically significant at 1% significance level, accepting the hypothesis H Unlike the initial expectation, the coefficient on Volatility is negative and statistically significant at 1% significance level in all Columns, rejecting the hypothesis H2 In addition, the coefficients on TobinQ (Column [1] and [3]) and Opp (Column [2] and [4]) are positive and statistically significant at 1% significance level The hypothesis H 3, therefore, is accepted Considering two 368 debt categories, DebtRatio (Columns [1] and [3]) and BankRatio (Columns [2] and [4]), the regression results show that two these factors have the negative and statistically significant effect on ASCGKN at 1% significance level These results accept the hypothesis H4 Also from Table 6, the regression coefficient on Policy is positive and statistically significant at the 1% level in all Columns, showing that ASCGKN in the period 2013-2016 with a price limit range of 7% are higher than those in the period 2010-2012 with a price limit range of 5% Hence, the hypothesis H is supported Next, the results of the regression of determinants of ASCKO will be presented in Table below and are also shown in columns, similar to Table Table Determinants of ASC estimated from KO model Variables Constant Turnover Depth Volatility TobinQ Opp DebtRatio BankRatio Policy Asset ListYear Observation R2 adj Durbin-Watson F Test Breusch-Pagan Test Hausman Test ASCKO [1] 0.0514 -2.0631 -0.0043 0.0600 -0.0841 0.1079 0.0352 -0.0151 1102 29.2% 2.24 1.70 230.47 24.69 ** ** *** * *** ** *** *** *** [2] 0.1715 -2.2216 -0.0031 0.0342 -0.0849 0.1124 0.0289 -0.0176 1102 28.0% 2.23 1.82 231.59 15.65 ** * *** ** *** * *** *** ** [3] 0.0654 -5.5912 -0.0046 0.0591 -0.0754 0.1065 0.0337 -0.0138 1102 29.0% 2.26 1.67 230.76 18.28 *** *** * *** ** *** *** ** [4] 0.1786 -6.0063 -0.0034 0.0320 -0.0790 0.1110 0.0282 -0.0166 1102 27.7% 2.24 1.79 230.30 11.71 *** ** *** * *** * *** *** Note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level The t-statistics are based on robust standard errors ASCKO is estimated from KO model; Turnover: the average of traded shares divided by outstanding shares; Depth: the average of shares available at both the best bid and ask prices divided by number of outstanding shares; Volatility: standard deviation of the midpoint; TobinQ: the sum of market value of stock and total debt divided by total assets; Opp: growth opportunity, Opp = if TobinQ > high growth opportunity; otherwise, Opp = 0; DebtRatio: ratio of total debt to total assets; BankRatio: the sum of short-term bank loan and long-term bank loan divided by total assets; Policy: policy on adjusting price limit range, Policy = if years of study belong to the period 2013-2016 (price limit range of 7%), and Policy = if years of study belong to the period 20102012 (price limit range of 5%); Asset: natural logarithm of total assets; ListYear: natural logarithm of number of years since listing In Table 7, the necessary tests from Column [1] to [3] recommend FEM method for performing regression equation while these tests in Column [4] recommend REM However, the regression results are not statistically and significantly different by using FEM or REM in Column [4] The FEM method, therefore, is used to estimate the regression equation from Column [1] to [4] In addition, comparing the adjusted R2 in Table (29.2%, 28.0 %, 29.0%, and 27.7% respectively from Columns [1] to [4]) to the adjusted R2 in Table (48.8%, 47.9%, 49.0% and 48.0% respectively from Column [1] to [4]), the regression model of factors affecting ASCKO is not more relatively effective than that affecting ASCGKN The regression results in Table show that, Turnover (Columns [1] and [2]) has a negative and statistically significant effect on ASCKO at 5% significance level, while no relationship between Depth and ASCKO is found 369 (Columns [3] and [4]) In addition, similar to the regression results in Table 6, the regression coefficients on Volatility (all Columns), DebtRatio (Columns [1] and [3]) and BankRatio (Columns [2] and [4]) are negative and statistically significant Moreover, those on TobinQ (Columns [1] and [3]), Opp (Columns [2] and [4]) and Policy (all Columns) have the positive and statistically significant impacts on ASCKO at 1% significance level Discussion This study aims to estimate determinants of asymmetric information between informed investors and uninformed Our empirical results are both the same and somewhat different from the literature Specifically, the effects of stock liquidity, firm growth, debt financing, and price limit on adverse selection component support the hypothesis supposed, while impact of volatility on adverse selection risk does not Like the extant literature, stock liquidity is one of the important factors that determine asymmetric information Not many investors would accept trading stock without precise and transparent information disclosed This study shows that Turnover and Depth, proxies for stock liquidity, have the opposite relation to asymmetric information, which supports the empirical works of Draper and Paudyal (2008), Goh et al (2016) and Abad et al (2017) In addition, this study confirms a positive effect of TobinQ and Opp which serve as proxies for growth opportunities on asymmetric information Our result supports the hypothesis suggesting that firms with great growth opportunities are likely to increase information risk This result is similar to that of Smith and Watts (1992), Daniel and Titman (2006) Our results also present that debt financing measured by DebtRatio and BankRatio is a significantly effective mean to mitigate adverse selection component Like the extant literature, debt financing as a monitoring mechanism is useful for shareholders to supervise management behaviors that could abuse free cash flow and distort earning disclosure (Jensen and Meckling, 1976; Jensen, 1986; Degryse and Jong, 2006) Statistical facts and regression results in this study show that the policy on widening price limit range from 5% in the period 2010-2012 to 7% in the period 2013-2016 could not only undermine the liquidity of stock but also increase a variety of proxies for asymmetric information This result implies that if the solutions of policymaker to ensure the stability of stock market are not consistent, expanding the price limit range should be considered Empirical studies designed according to cross-sectional models have suggested a positive and statistically significant effect of volatility on information risk (see Kim and Ogden, 1996; Van Ness et al., 2001) However, studies applying panel data model have indicated different results For example, Chordia et al (2001) and Narayan et al (2015) find a negative relationship between volatility and spread Our results are consistent with the negative effect of volatility on information problem This phenomenon can be explained in two ways Firstly, volatility could contain an amount of noise signal that separate spread Secondly, the period 2010-2016 in Vietnam is the sideways period in which firms with less changing in their stock price seem to be unattractive to investors, therefore increases private information Conclusions The aim of this study is to analyze signals of the market and firm characteristics as determinants of asymmetric information Based on the signaling theory, market microstructure theory and empirical research involved, this study hypothesizes liquidity of stock, volatility, growth opportunity, debt financing and policy on adjusting price limit have the potential effects on asymmetric information The methodologies of George, Kaul and Nimalendran (1991) and Kim and Ogden (1996) are used to estimate the adverse selection component for the HOSE traded firms during the first quarter from January to March 31 in the period 2010-2016 370 Analyzing 174 firms listed on HOSE from 2010 to 2016 with 1102 observations, this study finds the notable results which support the extant literature and not Like the extant literature, there is a negative and statistically significant effect of liquidity of stock and debt financing on adverse selection component and a positive impacts of growth opportunity and policy on adjusting price limit on adverse selection cost Unlike the extant literature, a negative and statistically relationship between volatility and private information is found This result notably implies that if the solutions of policymaker to ensure the stability of stock market are not consistent, widening the price limit range should be considered In addition, volatility is likely to contain an amount of noise signal that is unrelated to information risk Finally, to mitigate adverse selection problem, firms should focus on enhancing their liquidity of stock by disclosing information transparency, improving the monitoring of management by effectively using debt financing, and limiting problem of firms with high growth opportunity by encouraging managers to share more information References Abad, D., Lucas-Pérez, M.E., Minguez-Vera, A & Yague, J (2017) Does Gender Diversity on Corporate Board Reduce Information Asymmetry in Equity Markets? 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Financial Management, 30(3), 77-98 Wang, J (1993) A Model of Intertemporal Asset Prices Under Asymmetric Information Review of Economic Studies, 60(2), 249-282 Appendixes Apendix Regression results using trade-indicator GKN model 2RDTM,it = a0 + a1 (Sqit)[Qit – Qit–1] + εit Coefficient a0 a1 ASCGKN1 Observation R2 adj DW F Test Breusch-Pagan Test Hausman Test 2016 0.020 0.287 71.3% 9,570 51.13% 2.99 0.01 187.93 0.10 2015 0.024 0.283 71.7% 9,349 45.75% 2.99 0.02 82.70 1.10 *** *** *** *** 2014 0.020 0.245 75.5% 9,405 49.28% 2.96 0.02 310.45 0.82 *** *** 2013 0.017 0.301 69.9% 9,804 51.48% 2.89 0.03 313.66 1.46 *** *** 2012 0.020 0.435 56.5% 9,778 58.40% 2.88 0.03 1,115 1.76 * *** *** 2011 0.024 0.389 61.8% 8,960 54.48% 2.83 0.02 1,222 0.90 *** *** 2010 0.005 0.413 58.7% 6,954 60.66% 2.86 0.01 6,308 0.04 *** *** Note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level The t-statistics are based on robust standard errors Apendix Regression results using serial covariance KO model ̅𝟐 𝑺𝑲𝑶 𝒊 = β0 + β1√𝑺𝒒𝒊 + εi Coefficient β0 2016 0.018 β1 0.345 ASCKO Observation R2 adj White Test 65.5% 165 73.05% 42.22 2015 -0.275 0.376 *** 62.4% 170 61.41% 145.24 *** 2014 0.303 *** *** 0.250 75.0% 171 64.16% 66.35 *** *** *** 2013 0.253 0.300 70.0% 172 85.27% 51.65 *** *** *** 2012 0.062 0.461 53.9% 169 90.54% 44.86 * *** *** 2011 0.166 * 0.369 *** 63.1% 160 70.02% 53.44 *** 2010 0.331 0.345 65.5% 122 77.76% 18.92 Note: *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level The t-statistics are based on robust standard errors Apendix Statistics of the mean of adverse selection component for each stock Year 2010-2016 2016 2015 2014 ASC ASC unsorted ASC sorted (0 < ASC < 1) Mean Min Max n Mean Min Max n ASCGKN 63.9% -29.0% 100.0% 1106 64.1% 25.4% 92.3% 1102 ASCKO 60.1% -21.3% 91.7% 1106 60.2% 15.5% 91.7% 1105 ASCGKN 69.5% 36.2% 90.0% 163 69.5% 36.2% 90.0% 163 ASCKO 63.7% 27.4% 88.4% 163 63.7% 27.4% 88.4% 163 ASCGKN 73.3% 31.6% 92.0% 168 73.3% 31.6% 92.0% 168 ASCKO 68.7% 17.4% 90.3% 168 68.7% 17.4% 90.3% 168 ASCGKN 69.8% 36.7% 92.3% 169 69.8% 36.7% 92.3% 169 372 *** *** *** 2013 2012 2011 2010 ASCKO 64.4% 21.0% 91.7% 169 64.4% 21.0% 91.7% 169 ASCGKN 63.5% 29.0% 85.2% 170 63.5% 29.0% 85.2% 170 ASCKO 60.1% 34.1% 83.7% 170 60.1% 34.1% 83.7% 170 ASCGKN 52.7% 25.4% 100.0% 164 52.4% 25.4% 79.9% 163 ASCKO 50.1% 24.1% 90.4% 164 50.1% 24.1% 90.4% 164 ASCGKN 59.5% -29.0% 79.1% 155 60.9% 32.2% 79.1% 152 ASCKO 56.7% -21.3% 79.5% 155 57.2% 18.4% 79.5% 154 ASCGKN 56.4% 35.7% 78.2% 117 56.4% 35.7% 78.2% 117 ASCKO 55.4% 15.5% 81.1% 117 55.4% 15.5% 81.1% 117 Note: ASCGKN and ASCKO are referred to trade-indicator GKN model and KO model 373