Insider Trading, Regulation and the Components of the Bid- Ask Spread

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1 Insider Trading, Regulation and the Components of the Bid- Ask Spread Bart Frijns Nijmegen School of Management Radboud University Nijmegen P.O. Box 9108, 6500 HK Nijmegen, The Netherlands Aaron Gilbert Department of Finance Auckland University of Technology Private Bag 92006, 1020 Auckland, New Zealand Alireza Tourani-Rad Department of Finance Auckland University of Technology Private Bag 92006, 1020 Auckland, New Zealand 1

2 Insider Trading, Regulation, and the Components of the Bid-Ask Spread* Abstract In this paper, we investigate the relation between insider trading regulations and the bid-ask spread. We decompose the spread into its components before and after the enactment of strict new insider trading rules in New Zealand. We find that the enactment led to a significant decrease in the information asymmetry component of the spread, which is observed mainly in illiquid and high prechange information asymmetry companies. These findings are robust to model specification. In addition, we find a decrease in the contribution of information asymmetry to price volatility. Our results therefore imply that corporate insiders may represent a significant proportion of informed traders in poorly-regulated markets. JEL Codes : C22, D82, G18. Keywords: market microstructure, insider trading laws, bid-ask spread decomposition, regulatory change. *The authors would like to acknowledge the comments from the participants at the 10 th New Zealand Finance Colloquium, Dunedin, New Zealand (2006), the 19 th Australasian Banking and Finance Conference, Sydney, Australia (2006), the Financial Management Association Meetings Europe, Stockholm, Sweden (2006), and FMA/Asian Finance, Auckland, New Zealand (2006). Any remaining errors are ours. 2

3 I. Introduction If unregulated, corporate insider trading (i.e., trading by directors, executives, and large shareholders) may have an adverse effect on financial markets. For this reason, more than 80% of countries with financial markets have specific regulations governing insider trading (Bhattacharya and Daouk 2002). Such regulations are shown to increase liquidity, lead to wider share ownership and more accurate prices (Beny 2005), reduce the cost of capital (Bhattacharya and Daouk), and increase analyst following (Bushman et al. 2005). However, whether insider trading regulations reduce the degree of information asymmetry, and therefore the cost of trading, is a question that has remained largely unanswered. 1 This paper aims to address this question. Market microstructure theory suggests that information asymmetry, which may exist when superiorly-informed traders are present, causes the bid-ask spread to be wider in order to compensate the liquidity provider for potential losses made when trading with better-informed counterparties (e.g., Kyle 1985 amongst others). In poorly-regulated markets, corporate insiders may represent a substantial proportion of superiorly-informed traders. Their presence may increase the amount of information asymmetry, which may increase the bid-ask spread and, therefore, the cost of trading. 2 Effective insider trading regulations should reduce the degree of information asymmetry, which would result in a decreased bid-ask spread and lower cost of trading. These regulations may be even more important for illiquid stocks or illiquid markets, where the probability of informed trading is shown to be higher (Easley et al. 1996). In this paper, we examine whether a tightening in insider trading regulations reduces the degree of information asymmetry and, therefore, the cost of trading in a small, illiquid market, New Zealand. In 2002, New Zealand changed its regulations governing insider trading substantially in the so-called Securities Market Amendment 1 Recently, Eleswarapu et al. (2004) and Jiang and Kim (2005) investigated the impact of the U.S. Fair Disclosure (FD) Regulation, which was introduced in October The FD Regulation is directly aimed at reducing information asymmetry by requiring that any intentional disclosure of nonpublic information must be released to all parties (i.e., analysts, the general public, and other parties) simultaneously. Both studies find that trading costs were lower after the introduction of the FD Regulation and that, overall, the regulation has improved the information environment for U.S. stocks. 2 Chung and Charoenwong (1998), for example, find that firms with more prevalent insider trading have wider spreads. 3

4 Act (SMAA). These changes in the law provide an ideal setting to examine the impact of regulatory changes on the cost of trading. To determine whether the stricter new legislation has reduced the degree of information asymmetry, we use the spread decomposition model proposed by Madhavan, Richardson, and Roomans (1997). This model decomposes the spread into two components: (1) order-processing and inventory-holding costs 3 and (2) information asymmetry costs. We apply this decomposition model to a sample of 70 stocks listed on the New Zealand Exchange (NZX) before and after the introduction of the new regulations. The results show that the introduction of the new legislation resulted in a significant decrease in the spread. Conjointly, we find a significant decrease in the proportion of the spread attributable to information asymmetry. We examine the robustness of these results by using an alternative decomposition model proposed by Glosten and Harris (1988), and find that our main results remain valid. These results indicate that corporate insiders represented a significant proportion of informed traders before the enactment of the legislation. To examine whether illiquid stocks are more affected by corporate insiders, we create subsamples based on a measure for liquidity (i.e., number of trades) and prechange information asymmetry. We find that the decrease in the proportion of the spread attributable to information asymmetry is largest and most significant for the least-actively traded stocks and for those with the largest prechange information asymmetry. Finally, we use the Madhavan et al. (1997) decomposition model to examine the impact of information asymmetry on price volatility. We find that after the enactment of the new legislation there is a significant decline in the contribution of information asymmetry to price volatility. Overall, our results provide evidence that effective insider trading regulations are beneficial to the microstructure of financial markets by reducing the impact of information asymmetry on the cost of trading and price volatility. In addition these results imply that corporate insiders may represent a significant proportion of informed traders in markets that are poorly regulated. The purpose of the SMAA, which was enacted in December 2002, was to correct two major weaknesses in the previous legislation that resulted in a poor 3 These costs are often referred to as real frictions and relate to costs associated with exchange fees and so forth and compensation for having an inventory position that deviates from the desired inventory position. 4

5 enforcement record. The first weakness was the lack of a public regulator. As a consequence of this lack of a public regulator, enforcement was left to individual traders (who generally lack the resources to prosecute 4 ) and the issuing companies (who may have strong incentives not to prosecute). The SMAA empowered a public regulator (i.e., the Securities Commission) to prosecute corporate insiders if the issuing company refuses to take action. It is expected that the existence of a public regulator with the ability and will to prosecute should improve enforcement. The disclosure regime was the second major weakness of the previous legislation. Under the predecessor to the SMAA, only large blockholders were required to disclose details of their trades in a timely fashion (i.e., 5 working days). More conventional corporate insiders, such as directors, were not required to disclose until the annual report was published, and executives avoided disclosure entirely. The SMAA now requires all insiders to disclose within 5 working days, which should increase the timely flow of information and ultimately reduce the profitability of insider trades. The changes to the legal regime in New Zealand should, therefore, result in both an increase in the cost of engaging in insider trading (e.g., a greater probability of being prosecuted) and a concurrent decrease in the profitability of insider trading as a result of improved disclosure. 5 Overall, the new regulations should improve market quality. II. Components of the Bid-Ask Spread In the market microstructure literature the bid-ask spread (or more precisely half the bid-ask spread) is often used as a measure for the cost of trading as it represents a direct cost to a trader when buying or selling an asset. This cost of trading is generally assumed to compensate a liquidity provider for three cost components: (1) order processing costs, (2) inventory holding costs, and (3) information asymmetry costs. In this paper, we are most interested in the information asymmetry cost component, which compensates a liquidity provider for the potential loss she makes when trading with a better-informed counterparty. If corporate insiders represent a substantial proportion of informed traders and the new legislation has reduced insider trading, 4 New Zealand laws do not allow for class action or contingency fee arrangements, and prosecutors can face significant counterclaims for court costs if they lose. 5 Gilbert et al. (2005) find a reduction in insider profitability after the introduction of the new legislation. 5

6 then we expect the information asymmetry cost component, and therefore also the total spread, to decrease. To determine whether the new legislation has been effective we first consider the impact of the new legislation on the total bid-ask spread. We look at both the quoted dollar spread and the quoted percentage spread, and compare them in the period before and after the introduction of the new legislation. 6 This analysis will indicate whether spreads have decreased significantly after the enactment of the new legislation. However, in order to establish whether the decrease in the spread was actually caused by a decrease in the information asymmetry cost component, we need to decompose the spread and examine the impact of the new legislation on the on the information asymmetry cost component. The Spread Decomposition Model To decompose the spread, several models have been proposed, which can be broadly classified into two categories: (1) models that rely on the serial covariances of the time series of trades and quotes (e.g., George, Kaul, and Nimalendran 1991; Roll 1984; Stoll 1989); and (2) trade indicator models that examine the impact of buys and sells on prices (e.g., Glosten and Harris 1988; Huang and Stoll 1997; Madhavan et al. 1997). In line with previous research, we use a trade indicator model (Ahn et al. 2002). Of these models, the Glosten and Harris and Madhavan et al. models are most suitable to decompose the spread in a limit order book market, because they decompose spreads into two components: (1) permanent price impacts, which cover information asymmetry; and (2) transitory price effects, which cover order processing and, if existing, inventory holding costs. 7 Both Glosten and Harris (1988) and Madhavan et al. (1997) assume that expectations about the fundamental value of a risky security change with the change in public beliefs. 8 We refer to this (post-trade) expected value as the efficient price. Changes in public beliefs are assumed to be driven by two sources of information: (1) information that arrives through new public announcements and (2) information 6 A matched pairs t-test to determine the significance of any observed changes. 7 An alternative trade indicator model, the Huang and Stoll (1997) model was rejected as it explicitly decomposes spreads into all three components, making it inappropriate for use in limit order markets where inventory costs play little or no role. This model was also shown by Van Ness et al. (2001) to produce a high proportion of implausible estimates. 8 The fundamental value of a security can be thought of as the value of a security in the distant future. 6

7 market participants infer from order flow. When information arrives through a new public announcement, all market participants update their beliefs simultaneously. This simultaneous update results in an instant change in the efficient price, without any trade occurring. The amount by which the efficient price changes as a result of new public information is measured by ε t, which is assumed to be a zero mean, i.i.d. random variable. New information can also be inferred from order flow when privately informed traders are present. When these traders are present, their trading activity reveals information about the fundamental value to other market participants, and this information will affect public beliefs. This results in a change in the efficient price. However, not all trades are based on private information, meaning order flow provides a noisy signal about future asset values. Furthermore, as argued by Madhavan et al. (1997), it is the innovation or surprise in the order flow that reveals new information about a stock s fundamental value. 9 By relating the surprise in order flow to the change in the efficient price, we can infer the amount of privately informed trading. To formalize the discussion above, let x t be a variable that equals 1 if a trade at time t is buyer initiated and -1 if a trade is seller initiated. For trades that cross (i.e., trades that are initiated by both buyer and seller), x t = 0. Let λ be the unconditional probability of a trade that crosses (i.e., λ = Pr(x t = 0)). Then, assuming that the unconditional expectation of x t is zero and buys and sells are equally likely, the variance of the trade indicator variable is given as Var(x t ) = (1-λ). The surprise in order flow can be measured by considering the difference between the realized trade indicator and its ex-ante conditional expectation. We define the surprise in order flow as x t - E[x t x t-1 ], where E[x t x t-1 ] is the expected value of the trade direction at time t conditional on the previous trade direction. Madhavan et al. (1997) suggest using the first-order autocorrelation of the order flow (ρ) to determine the conditional expectation (i.e., E[x t x t-1 ] = ρx t-1 ). The surprise in order flow can, therefore, be stated as x t -ρx t-1. The impact of the surprise in order flow on the efficient price provides a measure for the amount of information asymmetry. Given these two sources of information, we can define the process for the efficient price. Let µ t be the post-trade expected value of the asset (i.e., the efficient 9 Order flow might be predictable as a result of, for example, order splitting. Glosten and Harris (1988) differ from Madhavan et al. (1997) because they consider the total impact of the arrival of a new order and not the impact of the surprise of a new order. 7

8 price). This efficient price is driven by public announcements and by private information that can be inferred from order flow: µ + t = µ t 1 + θ ( xt ρ xt 1) ε t, (1) where θ is a measure for the amount of information asymmetry. Although it is impossible to make any inferences from equation (1) because µ t is unobserved, we can make inferences from the observed trade and quote processes. Quotes, for example, are set to reflect the information in the efficient price. In addition, when setting quotes, liquidity providers want to be compensated for making a market. They will, therefore, set different prices at which they want to buy or sell. First, liquidity providers want to be compensated for the possibility of trading with better-informed counterparties. Second, they require compensation for costs associated with order processing and inventory imbalances (this compensation is captured by φ). The quotes that arise on the market reflect both compensations, where the ask quote ( and the bid quote ( a p t ) is the price of the asset conditional on a buy order arriving (x t = 1), b p t ) is the price conditional on a sell order arriving (x t = -1): p p a t b t = µ = µ t 1 t 1 + θ (1 ρx θ (1 + ρx t 1 t 1 ) + φ + ε, ) φ + ε. t t (2) Since trades occur at quoted prices, equation (2) also defines the process for transaction prices (p t ): p t = µ ( ) t 1 + θ xt ρxt 1 + φxt + εt + ξt, (3) where ξ t captures the effects of price discreteness. Alternatively, we can express equation (3) in first differences, which removes the unobserved efficient price and yields the covariance stationary process: p = θ x ρx ) + φ( x x ) + u, (4) t ( t t 1 t t 1 t 8

9 where u t = ε t +ξ t - ξ t-1. To estimate equation (4), we follow Madhavan et al. (1997) and employ the Generalized Methods of Moments (GMM) using the following orthogonality conditions: xt (1 λ) 2 xt xt 1 xt ρ E u t α = 0, ( ut α) xt ( ) ut α xt 1 (5) where α captures the drift in returns. The orthogonality conditions applied are essentially the OLS conditions with two additional conditions to identify λ and ρ. The advantage of using GMM is that it imposes no distributional assumptions, which results in more robust standard errors. Since the error term in equation (4) is autocorrelated, we compute standard errors using a Newey-West (1987) correction. When estimating the model, all returns data are multiplied by 100. III. Data and Sample The NZX is one of the smallest and least-liquid developed markets (Bhattacharya and Daouk 2002). Over the course of our sample period (i.e., January 2001 to August 2004), the exchange had between 149 and 164 domestic issuers of equity, with a market capitalization of NZ$37 billion in August Trades averaged between 40,000 and 60,000 a month at a market value of approximately NZ$2 billion. The NZX runs an electronic limit order book with no designated market makers. There are, however, a number of market participants who provide liquidity to the market, although they have no regulatory obligation to do so. The exchange runs a preopening session between 9 and 10 am, during which buys and sells can cross. The opening price is set at the end of this session as the price that clears the market. There is also a post-close period between 5 and 5:30 pm, during which there are post-trading adjustments of orders. We exclude all trades outside the normal trading hours because prices in these periods are set under different mechanisms. 9

10 We obtain intraday transaction data, including the bid and ask quotes at the time of the trade, from the NZX. The data contain the transaction price, traded volume, time of the trade (rounded to the nearest second), and the best bid and ask quote at the time of the trade for all listed companies. To ensure enough data are available to estimate our models, we restrict the sample to the 70 most-actively traded stocks. These selected stocks trade, on average, more than 5 times a day over the entire sample period. Because the data do not contain any information about the direction of the trade (buyer- or seller-initiated), we use the quotes at the time of the trade to determine the trade direction, which is accomplished by comparing the transaction price with the prevailing bid and ask prices. A trade that occurs above the midquote (i.e., the average of the bid and ask prices) is classified as buyer initiated, while a trade below the midquote is classified as seller initiated. Trades at the midquote are left undetermined. Lee and Ready (1991) suggest matching trades with lagged quotes to correct for any delays in the reporting of transactions. We only have quotes at the time of the trade; therefore, we cannot match trades with lagged quotes. However, Peterson and Sirri (2003) show that the success rate of matching trades and quotes is highest when quotes are not lagged. In addition, because the NZX operates an electronic limit order book, it is questionable whether there are any reporting delays. To explore the impact of SMAA on trading costs, we consider two equal time periods: 18 months before and after the enactment date of 1 December The prechange period runs from June 2001 to November 2002, while the postchange period covers March 2003 to August We consider the postchange period from March 2003 to control for some delays in the implementation of the new legislation by the NZX. While there was no specific phase-in of the provisions, full effective implementation did not occur until early Delaying the start of the postchange period ensures that the new legislation is fully implemented. INSERT TABLE 1 HERE 10 As a robustness check, we consider different pre- and postchange periods with equal periods of 12 months (a prechange period from November 2001 to November 2002 and a postchange period from March 2003 to March 2004). We also consider a prechange period that ended in June 2002 (again using 18- and 12-month periods before and after the enactment) to check whether market participants adjusted to the new legislation before it was enacted. All results are consistent with those presented in this article and are, therefore, not reported. 10

11 Table 1 presents summary statistics for the full sample period and the pre- and postchange subsamples. For the full sample, the average number of trades is a day, with a median value of This is substantially lower than the mean and median values reported by Madhavan et al. (1997) for NYSE-listed stocks (mean and median values are 95 and 66, respectively) and Ahn et al. (2002) for stocks listed on the Tokyo Stock Exchange (lowest reported mean and median values are 296 and 249, respectively). The average dollar spread is relatively low at 2.52 cents a share. However, given the low average share price of NZ$3.15, we observe a large percentage spread, which averages 1.18%. These percentage spreads are much larger than spreads observed in other studies. For example, Madhavan et al. report a percentage of around 0.6%. Similar results are found for other markets with limit order books: for example, Ahn et al. (2002) find between 0.31% and 0.56% on the Tokyo Stock Exchange, and Brockman and Chung (1999) find approximately 0.62% on the Hong Kong Stock Exchange. Average daily volatility is 2.17%; however, the median volatility is substantially lower at 1.61%. This indicates that the distribution of daily volatility across the sample is right-skewed. When the sample is split into pre- and postchange periods, we observe a significant drop in both dollar and percentage spreads. Average dollar spread decreases by nearly a quarter (i.e., from 2.91 cents to 2.22 cents). The average percentage spread decreases from 1.28% to 1.14%. There is, however, no significant change in the number of trades and the average price, implying that market conditions have remained similar. Finally, a significant drop in the median daily volatility is observed, although the decrease in average volatility is insignificant. This implies that the majority of stocks are less volatile after the enactment of the new legislation. IV. Results The Impact of the SMAA on the Information Asymmetry Cost of Trading To test whether the new legislation had an effect on the cost of trading and, most important, on the information asymmetry component of the cost of trading, equation (4) is estimated for the full sample period (i.e., June 2001 August 2004) and the prechange (i.e., June 2001 November 2002) and postchange (i.e., March

12 August 2004) subperiods. The results of this model for the whole sample are reported in Table INSERT TABLE 2 HERE The results for the parameter θ, which measures the degree of information asymmetry, are presented in Table 2. The new legislation is directly targeted at reducing insider trading, and insiders may represent a considerable subset of privately-informed traders; therefore, we expect the information asymmetry parameter to decrease in the period after the enactment of the legislation. The results in Table 2 support this hypothesis and show a weakly significant (at the 10% level) decline of about 12% in the mean value of θ between the pre- and postchange periods (i.e., from to ). A similar decrease is observed in the median value, which decreases from to To determine whether the degree of information asymmetry decreased significantly on a stock-by-stock basis, we perform a Wald test on the parameter estimates for each stock in the pre- and postchange subperiods. We report the number of stocks for which θ has decreased or increased at the 5% and 1% significance levels, respectively, in the last two columns of Table 2. Nearly 45% of the stocks show a significant decrease in the costs associated with information asymmetry at the 5% level. At the 1% level, approximately 30% of the stocks show a decrease. In contrast, approximately 7% of the stocks show a significant increase in θ. We find a decrease in order processing and inventory costs (i.e., parameter φ) similar to the decrease in information asymmetry costs. On average, φ decreases by more than 14% (i.e., from to ). However, in contrast to the information asymmetry parameter, the decrease is not significant. In addition, a slight increase in the median value of φ is seen, which indicates that the decrease in the mean value is driven by a few stocks with large values of φ in the prechange period. The lack of a significant decrease in mean values is further highlighted by the Wald tests conducted on a stock-by-stock basis. At the 5% level, only 17 stocks show a significant decrease 11 All estimated parameters have low standard errors. This suggests that the parameters are estimated with great accuracy. Therefore, the low liquidity of the stocks in the sample is not an issue. We also find implausible results for parameter estimates in less than 6% of cases. This contrasts with the findings of Van Ness et al. (2001), who report implausible results for the Madhavan et al. (1997) decomposition model in approximately 18% of cases. 12

13 in the postchange period, while 12 stocks show a significant increase. For more than half the sample, no significant change in any direction is observed. Table 2 also shows the results for the parameter λ (i.e., the probability of a trade occurring at the midquote). The results indicate a very low probability of midquote trades (less than 1% for the full sample period). There is no a priori expectation of λ changing after the enactment of the new legislation. However, the results show that the crossing probability has increased significantly after the enactment (i.e., more trades are being executed at the midpoint). The significance of this finding is confirmed by the number of increases and decreases. More than 60% of the stocks experience a significant increase in the crossing probability, while only 20% experience a decrease. The next parameter, ρ, captures the first-order autocorrelation in the trade direction variable (i.e., the order flow). This first-order autocorrelation is expected to be positive because continuations in trade direction are more likely as a result of factors such as order splitting. For the whole sample, the average first-order autocorrelation is approximately 0.41 (see Table 2), with no substantial difference between the pre- and postchange periods for mean, median, or on a stock-by-stock basis. The value of ρ is also similar to Madhavan et al. s (1997) results, who find values between 0.37 and 0.41, but higher than values observed by Ahn et al. (2002), who find values between 0.23 and Finally, Table 2 presents the summary statistics for the proportion of the spread attributable to information asymmetry costs. Given the specification of the bid and ask prices in equation (2), the expected value of the spread is a E [( p t p )] = 2( θ + φ). b t The proportion of the spread that is attributable to information asymmetry costs (π) is defined as θ π =. ( θ + φ) 13

14 Full-sample estimates show an average proportion of information asymmetry costs of approximately 58%, with a slightly higher median value of 61%. This value is substantially higher than those found in most other studies, where the proportion of information asymmetry costs is typically less than 50%, regardless of the decomposition model used and market studied. For the United States, for example, studies find values for π between 35% and 50% (Affleck-Graves et al [43%]; Kim and Ogden 1996 [50%]; Lin et al [39.2%]; Madhavan et al [35% to 51%]; and Stoll 1989 [43%]). Similar results are found for the London Stock Exchange (Menyah and Paudyal 2000 find 47%), the Tokyo Stock Exchange (Ahn et al find between 44% and 57%), and the Hong Kong Stock Exchange (Brockman and Chung 1999 find 33%). The higher proportion of information asymmetry costs observed in New Zealand may be attributed to the relatively low liquidity of the stocks traded on the NZX as illiquid stocks are shown to be more affected by informed trading (Easley et al. 1996). When we examine the pre- and postchange subperiods, we detect a significant decrease in the proportion of the spread attributable to information asymmetry costs in both mean and median values. Mean (median) values decrease from approximately 59% (62.5%) to about 55% (57.5%). This provides strong evidence that the new legislation has been effective in reducing insider trading. In addition to the significant decrease in mean and median, the proportion of information asymmetry costs decreases for approximately 57% of the stocks. Given recent research, it is unlikely that insider trading affects all companies equally. Easley et al. (1996), for example, show that the probability of informed trading is higher for less-actively traded stocks. In fact, Gregory, Matatko and Tonks (1997) and Friederich, Gregory, Matatko and Tonks (2002) show that insider trading is particularly heavy in illiquid stocks. Therefore, the impact of the new legislation on the cost of trading should be greatest for those companies where insiders are most prevalent. We examine this by splitting the sample into two subsamples based on trading activity (proxied by the average daily number of trades) and creating portfolios for the 30 most-actively traded stocks and the 30 least-actively traded stocks. The proportion of the spread attributable to information asymmetry costs in each subsample is reported in Table 3, Panel A. The reduction in the cost of trading as a result of a reduction in information asymmetry mainly manifests itself in the least- 14

15 actively traded stocks, decreasing from more than 66% to less than 60%. For the most-actively traded stocks, the decrease is marginal and insignificant. In addition, the average level of the proportion of information asymmetry costs is lower for the moreactively traded stocks, confirming that less-actively traded stocks are more affected by informed trading. INSERT TABLE 3 HERE As a secondary analysis, firms in the sample are sorted based on the prechange proportion of information asymmetry costs to determine if the effect of the new legislation is largest for stocks with the highest prechange asymmetry. Since the information asymmetry component measures the impact of informed trading on providers of liquidity, a high prechange proportion of information asymmetry may indicate more insider trading activity. If this is the case, then the impact of stronger regulations should be more pronounced among firms with high prechange information asymmetry because there is greater scope for improvement. Results for the top and bottom 30 stocks, sorted according to the prechange proportion of information asymmetry costs, are reported in Table 3, Panel B. Virtually all of the reduction in the proportion of information asymmetry costs comes from stocks with a high prechange proportion of information asymmetry costs. This group experiences a highly significant reduction (i.e., from more than 77% to almost 69%). This indicates that the legislation has been most effective for companies with the greatest problems prechange. The results presented so far indicate that the proportion of information asymmetry costs decreased after the enactment of the new legislation. To investigate whether this decrease actually occurred immediately following the enactment, the decomposition model is estimated using a 12-month window, which rolled forward by one month. Based on these rolling windows, the total proportion of information asymmetry costs for each period is estimated. In addition, the proportion of information asymmetry costs for the top 30 and bottom 30 stocks in terms of trading activity is also estimated. The results are illustrated in Figure 1. INSERT FIGURE 1 HERE 15

16 Figure 1 shows a steep, lasting decrease in the average proportion of information asymmetry costs for the whole sample and firms with low trading activity starting immediately after the introduction of the new law. Firms with high trading activity, on the other hand, show a very small decrease in the information asymmetry component around the time of the introduction of the new law. By the end of the sample period, these companies have even higher proportions than in the prechange period. The timing of the decreases for the low-trade firms and the size of the decrease suggest that the new regulations have been effective in reducing the information asymmetry component of the spread. Robustness Analysis: The Glosten and Harris (1988) Decomposition Model De Jong et al. (1996) argue that the parameter estimates of a decomposition model depend heavily on the model specification. Therefore, a robustness check using an alternative model specification is warranted. We opt for the Glosten and Harris (1988) decomposition model, which has been used in markets with electronic limit order books (see Ahn et al. 2002). This model is also a trade indicator model, which, in addition, considers information asymmetry, and order- processing and inventory costs to be a function of traded volume. The model is pt = α + θ0 xt + θ1xtvt + φ0 xt + φ1 xtv t + ηt, (6) where α captures the drift in returns, V t is the traded volume at time t, and η t captures the arrival of new public announcements and the effects of price discreteness. The parameters φ 0 and φ 1 relate to the order processing and inventory costs, while θ 0 and θ 1 relate to the information asymmetry costs. We normalize all traded volumes by dividing by 1,000 and, in line with De Jong et al., truncate the sample by removing all transactions that have more than twice the median traded volume. This truncation removes the effect of extremely large trades, which have a large impact on the parameter estimates (see Hausman et al. 1992). We estimate this model using ordinary least squares and compute standard errors using a Newey-West correction. Table 4, Panel A presents the summary statistics for the parameter estimates using the Glosten and Harris (1988) model. The results are in line with the earlier 16

17 findings for the parameter estimates using the Madhavan et al. (1997) model. The parameter related to information asymmetry costs θ 0 is significantly lower in the postchange period. In addition, a reduction in the order processing and inventory costs (φ 0 ) is observed. This reduction, however, is insignificant. The inclusion of traded volume in the Glosten and Harris (1988) model makes it possible to test whether informed traders prefer to trade large or small volumes to best utilize their information. 12 A positive (negative) value for θ 1 indicates that insiders prefer to trade at large (small) volumes, whereas an insignificant value for θ 1 indicates no preference for a particular volume. Panel A in Table 4 shows that the value for θ 1 is positive in the prechange period (the t-statistic for θ 1 in the prechange period is 3.27), which provides some evidence for the hypothesis that large trades are more informative. In the postchange period, θ 1 decreases significantly (the mean value of θ 1 is still positive but the t-statistic of 1.23 shows that it is no longer statistically significant). Arguably, the insiders who traded before the new legislation may have traded large volumes to maximize profits. After the new legislation, the impact of large trades on the value of the asset decreases, which suggests that there has been a reduction in insider trading or insiders hide trades by splitting orders. INSERT TABLE 4 HERE The estimates for φ 0 are generally much larger than estimates for θ 0 (about three times as large), which indicates that order processing and inventory costs are much larger than information asymmetry costs. The negative value for φ 1 indicates that large trades lead to lower order processing and inventory costs, which is in line with Ahn et al. (2002). The negative φ 1 suggests that order processing costs, which decrease as traded volume increases, are the most important costs in the New Zealand market. If market participants needed to be covered for inventory costs, we would 12 There has been a long debate about how insiders exploit their private information. One hypothesis states that insiders trade large volumes in order to maximize the profits of their private information. This hypothesis is supported empirically by Glosten and Harris (1988), and Chung and Charoenwong (1998), amongst others. A second hypothesis, known as the stealth trading hypothesis, postulates that insiders trade medium sized volumes in order to conceal their private signals from other market participants. Empirical evidence for this hypothesis is provided by Barclay and Warner (1993) and Chakravarty (2001). 17

18 expect φ 1 to be positive because large trades lead to larger unwanted inventory imbalances. Table 4, Panel B presents the information asymmetry (i.e., θ V ~ 0 + θ 1 ), and order- processing and inventory costs (i.e., φ V ~ 0 + φ 1 ) at median trade volumes (V ~ ). The proportion of the spread attributable to information asymmetry costs, defined as ~ ( θ0 + θ1v ) ~ ~ ( θ + θ V ) + ( φ + φ V ) is also reported. The results support the findings of the Madhavan et al. (1997) model and show a significant decrease in the information asymmetry component (from to ). The same conclusion can be drawn from the proportion of the spread attributable to information asymmetry costs, which shows a significant decrease in the postchange period. Interestingly, the proportion of the spread is roughly half of the proportion calculated using the Madhavan et al. model. However, similar results are found by Ahn et al. (2002) and confirm De Jong et al. s (1996) conclusion that estimates depend on model specification. The Contribution of Information Asymmetries to Price Volatility The Madhavan et al. (1997) decomposition model makes it possible to not only determine the cost of trading attributable to informed trading, but also to infer the impact of information asymmetries on price volatility. In this model, volatility is caused by two different sources: (1) the arrival of new public information and (2) market frictions. The existence of asymmetrically informed traders is one source of frictions, and because their presence introduces uncertainty about the location of the fundamental value, stock prices become volatile. It is postulated that if the new legislation has been effective in reducing the amount of insider trading the proportional contribution of information asymmetry to price volatility should decrease. This issue is investigated in this section. Using the model for the price process outlined in section 2, Madhavan et al. (1997) suggest that price volatility 18

19 var[ t p ] = σ + 2σ + (1 λ)[( θ + φ) + ( ρθ + φ) 2( θ + φ)( ρθ φ) ρ], (7) ε ξ + can be expressed as a function of several components. These components are the contribution of public news shocks to total volatility ( σ ), the contribution of price 2 discreteness (2 σ ), the contribution of information asymmetry ( ( 1 λ)(1 ρ 2 ) θ ), 2 ξ 2 and the contribution of inventory and order processing costs ( 2(1 λ)(1 ρ) φ ). In addition, there remains an interaction term between information asymmetry and 2 ε 2 inventory and order processing cost ( 2φθ (1 λ)(1 ρ ) ). Apart from the first component ( σ ), all components relate to market frictions. To identify the additional 2 ε 2 2 parameters in equation (7) (i.e., σ ε and σ ξ ), two extra orthogonality conditions are added to the conditions given in equation (5): ( u ) ( 2 ) t α σ ε + σ ξ E = 0. (8) 2 ( )( 1 ) ut α ut α + σ ξ Table 5 presents the total price volatility (on a transaction basis) and the relative contributions of each of the components to either price volatility or to the total amount of market frictions. We observe a large but insignificant decrease in total price volatility (from to ) after the introduction of the new laws. To determine which factors contribute to the decrease in volatility, the separate components are studied. First, more than 50% of the total price volatility is caused by public news announcements. However, a decrease in this proportion is noticed in the postchange period. This could explain the decrease in total volatility. However, it also indicates that proportionately more volatility is generated by market frictions in the postchange period. INSERT TABLE 5 HERE The components of the market frictions are expressed as fractions of total market frictions. Of these components, price discreteness contributes almost 50% to 19

20 total market frictions (or approximately 22% of total volatility). Interestingly, this component is one of the smallest contributors observed by Madhavan et al. (1997). One possible explanation for this may be the fact that the percentage quoted spread observed in our sample is almost twice the percentage quoted spread observed by Madhavan et al. In addition, most trades in our sample occur at the quoted spread (λ is almost zero), whereas Madhavan et al. observe a probability of trades occurring within the quoted spread of approximately 0.3. When considering the contribution of information asymmetry to total market frictions we observe a significant decrease in the postchange period. The mean (median) fractions decrease by more than 16% (22%). The importance of this decrease is further highlighted by the fact that 48 companies experienced a decrease in this fraction. These findings provide some support for the hypothesis that the enactment of the new legislation reduced the contribution of asymmetric information to price volatility. A significant increase of approximately 4% is found when considering the fraction of market frictions attributable to inventory and order processing costs. Again, the increase was experienced by a substantial number of stocks (40 stocks). In addition, the interaction term showed a negligible change between the pre- and postchange periods. The reduction in the information asymmetry cost of trading after the enactment of the new legislation was largest for small and less-actively traded firms, and it is expected that the largest reduction in the contribution of asymmetric information to price volatility will occur for these inactive stocks. Therefore, the sample is split based on trading activity (proxied by average daily number of trades), and two portfolios containing the top 30 stocks and the bottom 30 stocks in terms of trading activity are constructed. Table 6, Panel A presents the results for the fraction of market frictions attributable to information asymmetry and shows a reduction of similar magnitude for actively- and inactively-traded stocks. However, only the decrease for inactively-traded stocks is significant. Differences become more substantial when turning to median values: That is, the decrease is large and significant for the least-actively traded stocks and only marginal and insignificant for the most-actively traded stocks. INSERT TABLE 6 HERE 20

21 When the firms are sorted by prechange information asymmetry (see Table 6, Panel B), there is a considerable difference between the low- and high-asymmetry groups. For low-asymmetry stocks, the fraction of market frictions attributable to information asymmetry is only 8%, and there is almost no change from the pre- to postchange period. For high-asymmetry firms, the proportion is more than 30% in the prechange period. This, however, decreases significantly to less than 25% in the postchange period. V. Conclusion This paper examined the impact of a significant tightening in regulations targeted directly at all aspects of insider trading in New Zealand. The New Zealand Stock market is a small, relatively illiquid market and was previously governed by poor insider trading regulations. Recent changes in insider trading regulations provided an ideal setting for examining the impact of government intervention on the cost of trading. This issue is explored by decomposing the spread into separate components. The present study finds strong evidence of a relationship between the efficacy of insider trading rules and the proportion of the spread attributable to information asymmetry costs. The results therefore imply that corporate insider represented a significant proportion of informed traders. These results are robust when using a different model specification. Splitting the sample according to liquidity and prechange information asymmetry shows that the reduction in information asymmetry costs was mainly driven by illiquid firms that suffer from high prechange information asymmetry. Finally, it is observed that the overall stock price volatility decreased after the introduction of the new legislation and that the contribution of information asymmetry to the total frictions that cause volatility decreased significantly. The findings of this study suggest that the introduction of effective measures to limit insider trading may be beneficial for other markets. Many countries, especially smaller and emerging markets, may suffer from a similar lack of appropriate regulation to address insider trading. While the liquidity of the New Zealand market is not representative of other developed markets, a majority of smaller stocks listed in larger markets may suffer from a similar lack of market attention. For these stocks and countries, effective government intervention may prove beneficial. 21

22 References: Affleck-Graves, A., S. Hedge and R. Miller, 1994, Trading mechanisms and the components of the bid-ask spread, Journal of Finance 49, Ahn, H., J. Cai, Y. Hamao and R. Ho, 2002, The components of the bid-ask spread in a limit-order market: evidence from the Tokyo Stock Exchange, Journal of Empirical Finance 9, Barclay, M. and J. Warner, 1993, Stealth trading and volatility: which trades move prices?, Journal of Financial Economics 34, Beny, L., 2005, Do insider trading laws matter? Some preliminary comparative evidence, American Law and Economics Review 7, Bhattacharya, U. and H. Daouk, 2002, The world price of insider trading, Journal of Finance 57, Brockman, P. and D. Chung, 1999, Bid-ask spread components in an order-driven environment, Journal of Financial Research 22, Bushman, R., J. Piotroski and A. Smith, 2005, Insider trading restrictions and analyst s incentives to follow firms, Journal of Finance 60, Chakravarty, S., 2001, Stealth-trading: Which traders trades move stock prices?, Journal of Financial Economics 61, Chung, K. and C. Charoenwong, 1998, Insider trading and bid-ask spread, The Financial Review 33, Chung, K., B. Van Ness and R. Van Ness, 2004, Specialists, limit-order traders, and the components of the bid-ask spread, The Financial Review 39, De Jong, F., T. Nijman and A. Roell, 1996, Price effects of trading and components of the bid-ask spread on the Paris Bourse, Journal of Empirical Finance 3, Easley, D., N. Kiefer, M. O Hara and J. Paperman, 1996, Liquidity, information, and infrequently traded stocks, Journal of Finance 51, Eleswarapu, V., R. Thompson and K. Venkataraman, 2004, The impact of Regulation Fair Disclosure: trading costs and asymmetric information, Journal of Financial and Quantitative Analysis 39, Friederich, S., A. Gregory, J. Matako and I. Tonks, 2002, Detecting returns around the trades of corporate insiders in the London Stock Exchange, European Financial Management 8,

23 George, T., G. Kaul and M. Nimalendran, 1991, Estimation of the bid-ask spread and its components: a new approach, Review of Financial Studies 4, Gilbert, A., A. Tourani-Rad and T. Wisniweski, 2005, The impact of regulatory change on insider trading profitability: some early evidence from New Zealand, Advances in Financial Economics 11, Glosten, H. and L. Harris, 1988, Estimating the components of the bid-ask spread, Journal of Financial Economics 21, Gregory, A., J. Matatko and I. Tonks, 1997, Detecting information from directors trades: Signal definition and variable size effects, Journal of Business Finance and Accounting 24, Hausman, J., A. Lo and A. MacKinlay, 1992, An ordered probit analysis of transaction stock prices, Journal of Financial Economics 31, Huang, R. and H. Stoll, 1997, The components of the bid-ask spread: a general approach, Review of Financial Studies 10, Jiang, C. and J.-C. Kim, 2005, Trading costs of non-u.s. stocks on the New York Stock Exchange: The effect of institutional ownership, analyst following, and market regulation, Journal of Financial Research 28, Kim, S. and J. Ogden, 1996, Determinants of the components of the bid-ask spreads on stocks, European Financial Management 1, Kyle, A., 1985, Continuous auctions and insider trading, Econometrica 53, La Porta, R., F. Lopez-de-Silanes, A. Shleifer and R. Vishny, 2000, Investor protection and corporate governance, Journal of Financial Economics 58, Lee, C. and M. Ready, 1991, Inferring trade direction from intraday data, Journal of Finance 46, Lin, J., G. Sanger and G. Booth, 1995, Trade size and the components of the bid-ask spread, Review of Financial Studies 8, Madhavan, A., M. Richardson and M. Roomans, 1997, Why do security prices change? A transaction-level analysis of NYSE stocks, Review of Financial Studies 10, Menyah, K. and K. Paudyal, 2000, The components of bid-ask spreads on the London Stock Exchange, Journal of Banking and Finance 24, Newey, W. and K. West, 1987, A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica 55,

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