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This song is not currently available in your region. Try the alternative versions below. Allenchipipaajyoh one of ABBA's best. Forgot Password? Get Code. Male Female. They do, however, not analyze parallel trading but rather construct a sample of stocks that switched their trading location e. They then estimate the probability of informed trading separately for the pre- and post-switch period and compare the resulting estimates using t-tests and non-parametric tests.

In contrast, we estimate the model jointly for both markets and draw inferences by imposing appropriate restrictions and testing whether they are binding. A frac- tion of these traders is potentially informed. Prior to the beginning of the trading day, nature determines whether an information event takes place. The asset value is revealed at the end of the trading day.

Information events on different days are assumed to be independent. There are two groups of traders. Uninformed traders do not know the asset value nor do they observe whether an information event occurred.

They trade for liquidity reasons. Informed traders know whether an information event took place and observe the true asset value.

They buy assets when the value to be publicly revealed at T is high and sell when the value is low. This implies that they do not trade when there was no information event. We rather assume that the arrival rates of uninformed and informed traders in both markets are given. We do, however, allow these arrival rates to be different in the two markets. Figure 1 shows the composition of the order flow in both trading systems on days with good news, bad news and no news. For example, after a buyer-initiated transaction they will revise the probability assigned to a positive information event upwards.

For the updating process and the resulting bid and ask prices it is of importance whether a market maker observes only the trades in her market or the trades in both markets.

This is in- consequential, however, for the estimation of the parameters of our structural model. This follows from the assumption of independent order arrival rates in both markets. Consider the market maker on the floor. The spread at time t is thus equal to the prob- ability that a buy is information based times the expected loss to an informed buyer, plus a symmetric term for sells.

The probability that any trade that occurs at time t is information- based is the average of the probability of an information-based sell and the probability of an information based buy, weighted by the probability that the next transaction is buyer- or seller- initiated, respectively.

These parameters can be used to obtain an estimate of the unconditional probability PI 0 to encounter an informed trader. Our objective is to estimate the parameters for two markets and to test the hypothesis that the probability PIS to encounter an informed trader in the anonymous screen trading system is higher than the corresponding probability PIF on the floor. We have to take into account the inherent linkage between the two markets.

Our esti- mation is, therefore, based on the joint likelihood of observing a given trade history in both markets. BF ,i! Note that 5 equals the product of the densities of the four independent Poisson processes that determine the arrival of uninformed sellers and buyers in the two trading systems. Standard errors of the transformed coefficients are cal- culated using the delta method. This is our base model, denoted model 1. We estimate three other models.

Model 2 allows all parameters to be differ- ent across markets. This provides a useful check of the va- lidity of the empirical model. Model 3 restricts all parameters to be equal across markets. We perform likelihood ratio tests of model 3 against model 1 to test for differences in the trading intensity.

Rejection of this restriction does not generally allow conclusions about whether the probability of informed trading is higher in one of the markets. The likelihood ratio test of model 4 against model 1 serves as the test of our main hypothesis. The procedures make use of the Constrained Maximum Likelihood li- brary and are available upon request. All re- sults are based on the 5-second aggregation rule for IBIS transactions defined in section 3.

Results for the no aggregation rule are similar and are shown in a table in the appendix. This is consistent with the observation that the return on the DAX was positive during the sample period. The order arrival rates are generally higher in the electronic trading system. Visual inspection of the figure yields two conclusions. First, the differences of the PI point estimates are large for the major- ity of stocks and, second, there is either no or only a small overlap of the confidence intervals.

They are very similar to both the no aggre- gation and the 5-second aggregation case. Note that the parameter estimates for the floor are not the same for the different aggregation rules because we estimated the model for the floor and IBIS jointly.

The differences in the estimated parameters for the floor are, however, negligible. A more formal test of this hypothesis will be presented below.

Insert Figure 2 here An important issue is whether the probability of informed trading is systematically related to the firm size or the liquidity of the stocks.

This is confirmed in our data set. We regressed the PI estimates on the log of the number of transactions. The results for the 5-second aggregation rule and the no aggregation case, presented in Table 3, document that the probability of informed trading is negatively related to the overall liquidity of the stock.

Insert Table 3 about here In Section 4 we have proposed a set of likelihood ratio tests that serve to test the validity of our model specification and the parameter restriction implied by our main hypothesis. Table 4 reports the results. As outlined above, our base model model 1 can be interpreted as a re- stricted version of the most flexible model 2.

Hence, a rejection of model 1 in favor of model 2 would clearly indicate misspecification. The results reported in column 2 of Table 4 are, how- ever, encouraging. Visual inspection of Table 2 suggests already that the order arrival rates tend to be higher in IBIS. It is therefore not surprising that the restrictive model 3 is re- jected in favor of model 1.

The test thus provides clear evidence that the trading intensities in the two trading system are significantly different. Insert Table 4 about here We now turn to the formal test of our main hypothesis that the probability of informed trading is larger in the anonymous screen trading system. A formal test of this hypothesis is provided by the likelihood ratio test of model 1 against model 4. The last column of Table 4 contains the p-values that correspond to this test.

The specifica- tion of model 4 imposes the nonlinear restriction 10 , i. Simi- larly, we tested the hypothesis of equal median PI using a non-parametric Wilcoxon test. The results are summarized in Table 5. Therefore, the results of the formal statistical tests confirms the conclusions drawn from in- spection of Figure 2.

We make use of the fact that the probability of informed trading, PI, is directly related to the adverse selection risk that suppliers of liquidity face. This adverse selection risk should be incorporated into the bid-ask spread. We therefore expect stocks with higher PI to have higher bid-ask spreads.

We used the quoted bid-ask spread, the effective bid-ask spread and the adverse selection component of the spread. All spread measures are expressed as a percentage of the quote mid- point. It was insignificant in each case.

We then matched each transaction with the next transaction at the opposite side of the market i. The prices of the two transactions are then used to calculate the realized spread which directly measures the profit of the suppliers of immediacy. The difference between the average effective and the average realized spread is our measure of the adverse selection component.

Results based on the no aggregation and 1-second aggregation rules are very similar. Insert Table 6 here As hypothesized, the spread is positively related to the probability of informed trading. The probability of informed trading is, as is evidenced by the R2, most closely related to the ad- verse selection component of the spread.

This is what one would expect to find given that the PI is a direct measure of the adverse selection risk. Consistent with the larger PI estimates, the adverse selection component of the spread is gen- erally higher in IBIS than on the floor. The average over all stocks is 0. We interpret this as evidence of the validity of our empirical model.

We make use of a unique institutional feature of the German stock market, namely, the co-existence of an anonymous electronic trading system and a non-anonymous floor-based exchange. Our main objective is to analyze whether informed traders have a preference for the anonymous trading system. This allows us to for- mally test the null hypothesis of equal probabilities of informed trading. This hypothesis is strongly rejected. Our results thus provide evidence that informed traders prefer the anony- mous electronic trading system.

We further confirm findings by others that the probability of informed trading is negatively related to measures of the overall liquidity of the sample stocks. Supplementary analysis supports our conclusions.

   

 

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