Signals of market and firm characteristics and asymmetric information

2016 who explorethe 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 sig

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

risk (Berkman and Lee, 2002) Moreover, price limit range is not an effective tool to limitasymmetric information (Chan et al., 2005; Kim and Yang, 2008).

In Vietnam, the research mentioned above is highly rare Therefore, this study aims to identifydeterminants of asymmetric information between informed and uninformed investors whenperforming stock

trading This study is motivated by the empirical work of Van Ness et al (2001), Hegde andMcDermott (2004), Kim and Yang (2008), Narayan et al (2015) and Fosu et al (2016) who explorethe 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 ofliquidity of stock, volatility and debt financing on adverse selection component, while growthopportunity and policy on adjusting price limit range positively impact on information risk Ourresults are very useful for policymakers to consider whether to adjust policy on price limitrange 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 theextant literature on asymmetric information and factors of market and firm characteristicsrelated to information risk Section 3 presents the data sample and discusses the methodsused in our empirical estimator Section 4 presents the empirical results, while Section 5discusses these results Section 6 emphasizes our main findings and concludes the paper

2 Literature review

2.1 Asymmetric information

Asymmetric information reflects an object or group of objects that own superior firm-specificinformation related to future public disclosures not available to uninformed (Chae, 2005), and itarises from private information between informed and liquidity investors (Barakat et al., 2014).Informed traders make a profit from performing securities transactions on private informationthat uninformed ones do not, which is attributed to adverse selection problem (Copeland andGalai, 1983; Glosten and Milgrom, 1985) When the stock market has a severe adverseselection problem between different investors, uninformed investors could leave the market.According to signaling theory and market microstructure theory, specific signals of marketand firm characteristics could predict the level of asymmetric information in several ways Forexample, frequent trading of stock and financial structure would negatively impact on adverseselection problem (Acker et al., 2002; Degryse and Jong, 2006), while volatile stock price andgrowth 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, consistentwith the literature and inconsistent, because these results depend on the characteristics ofeach 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 researchinvolved, the factors of liquidity, volatility, growth opportunity, debt financing and price limitaffecting 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 tradingperiod 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 significantasymmetric information between informed investors and uniformed before the period ofearning disclosure Uninformed investors often choose to limit their trading activities except forthe urgent need for liquidity

Acker et al (2002) show that stocks with high trading volume and frequency would havelower adverse selection component than those with less liquid Draper and Paudyal (2008) alsofound that stock liquidity was negatively correlated with asymmetric information Therefore,the hypothesis is as follows:

H 1 : Trading volume has a negative effect on asymmetric information.

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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 byinvestors, and the higher changing in stock price, the greater profit informed traders couldmake 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 apositive impact of volatility on information risk

On the other hand, the positive effect of volatility on asymmetric information is inconsistentwith other empirical studies For instance, Li and Wu (2006) do find no explanation of volatilityleading to adverse selection component The authors suppose that volatility of stock priceincludes 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 explainprivate information Many studies, however, have confirmed that price volatility positivelyeffects on information problem This discussion leads to the following hypothesis:

H 2 : 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 asymmetricinformation (Myers and Majluf, 1984) There are two approaches to explain this phenomenon,including the information approach and the behavioral finance Drawing from the informationapproach, in companies with high growth opportunities, inside managers have privateinformation about new investment projects or cash flows from assets in place while outsideinvestors could not afford to observe the behavior of the manager (Smith and Watts, 1992) Inaddition, drawing from the behavioral finance approach, shareholders who invest in companieswith high growth opportunities are often overconfident and tend to overreact to vague orunverifiable information (Daniel and Titman, 2006)

Consistent with these perspectives, Hegde and McDermott (2004), Fosu et al (2016) findthat the companies with a high growth opportunity positively relate to the asymmetricinformation Therefore, according to the information approach, behavioral finance approachand related empirical research, the hypothesis of the relationship between growthopportunities and asymmetric information is as follows:

H 3 : 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 differentperspectives According to Stulz (1990), many companies with poor performance often usedebt financing to offset their operating cash flow Moreover, the excessive use of debt couldincur financial costs for the company The debt ratio, hence, is likely to diminish companyperformance 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 positiveoutlook of debt financing Debt financing conveys a positive signal to shareholders andcreditors about the effectiveness of monitoring the behavior of management (Jensen andMeckling, 1976), improving transparent disclosure (Ross, 1977; Jensen, 1986) and declining themanagerial discretion and private information (Degryse and Jong, 2006) It also positivelysignals to investors about a future value-for-money perspective (Myers and Majluf, 1984) Inaddition, debt financing is useful for firms to enhance their performance by exploiting taxshield effectively This discussion leads to the following hypothesis:

H 4 : 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 theprice limit range will have different impacts on the stock market Specifically, narrowing pricelimit range may reduce the volatility of stock price (Chen, 1993, Lee and Kim, 1995), assist theindex of stock market not fall deeper during the crisis (Rhee and Chang, 1993) and restrictprice 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 pricemovement slows to equilibrium (Kim and Rhee, 1997) Extending the price limit range is usefulfor attracting more investors, but its negative aspect causes adverse selection cost (Anshumanand Subrahmanyam, 1999) Lee and Chou (2004) study intraday price limit on the TSE and findthat the firms whose stock price hits a ceiling of price limit would have a higher level ofasymmetric 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 period2010-2016, 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 ofstock price and decline trading volume (Berkman and Lee, 2002) This discussion leads to thefollowing hypothesis:

H 5 : 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.

3 Data and methodology

3.1 Data collection

This study collected statistical data of trading prices and orders of companies listed on HOSEduring the first quarter from January 1 to March 31 in the period 2010-2016 to measureasymmetric information, stock liquidity, volatility and growth opportunity The first quarter isthe time when the listed firms disclose information about annual reports and audited financialstatements at the end of year related to firm performance, and there is a significantinformation risk between insider and outsider or between informed investors and uninformedinvestors In addition, the sample does not include banks, financial institutions, insurancecompanies and investment funds because of specific activities as well as specific legalregulations for these organizations The reason for choosing this period is that VNIndex has anegligible fluctuation Figure 1 below reveals this index which is likely to present the marketvolatility

Figure 1 below illustrates VN-Index in the period 2007-2016 Obviously, VN-Index declineddramatically, 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 interestrate 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, Index had a tendency to move sideways within the resistance range of 400-600 points Thisevidence shows that although Vietnam has overcome the global financial crisis, the Vietnam’sstock market is still in a long period waiting for a signal of real prosperity

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 do 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

Where Pt is the transaction price; Mt is the true price; Qt is the trade indicator variable; Sq is bid

ask spread;

πis 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]

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 1 – a1

Next, let x it = (S qi )[Q it – Q it–1 ] and y it = 2RD TM,it correspond to each stock i, the average

adverse selection component of the stock i, ASC i,GKN is estimated according to the formula

below:

 ( x it  x )( y it  y) T

ASC

t 1

This study measures the variables in regression equation (4) as follows: RD TM,it = ∆P it – ∆M it is

the difference between the change in the closing price at the end of the day (∆P it) and the

change in the mean of the bid price and ask price or change in the midpoint (∆M it ); Q it is a

trading indicator variable determined by Lee and Ready (1991), Q it = +1 if the closing price is

higher than the midpoint; otherwise, Q it = –1; S qi 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:

is the mean of the sum of the squared spreads, where S qit 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

1 – β1

Next, Kim and Ogden (1996) proposed a convenient formula for estimating asymptotic

average adverse selection for each stock in the KO model Accordingly, ASC i,KO is estimated by

the following formula:

where RD TM,it = ∆P it – ∆M it is the difference between the change in the closing

(∆P it ) and the change in the midpoint (∆M it ); S qi 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:

  ControlVarit it

Equation (8) describes explanatory variables that are likely to affect the asymmetric informationcorresponding to the expected mark and the control variables that improve the effectiveness of theregression model The left side of the Equation (8) is a dependent variable which serves as a proxy

for the asymmetric information measured by ASC GKN and ASC KO 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

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 1 below

Table 1 Definition and measurement of variables

price limit range from 5% to 7%

4 Results

4.1 Characteristics of research sample

Table 2 presents the mean values of ASC GKN in Panel A and ASC KO in Panel B under 0 < ASC <

1 from 2010 to 2016 ASC GKN is in the range of (52.4%; 73.3%) while ASC KO is in the range of

(50.1%, 68.7%) Generally, ASC GKN and ASC KO have the same trend.

Table 2 The average adverse selection component of individual firm over the years

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 3 below presents

the statistics of the study variables

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Table 3 Descriptive statistics of variables

number of years since listing; Asset: total assets (mil vnd)

Next, the mean value of ASC GKN and ASC KO for different levels of growth opportunity from

2010 to 2016 illustrates in Figure 2 below

2016 2015 2014 2013 2012 2011 2010

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