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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

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

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

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

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

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