From the descriptive statistics in Table 1 we see that there are some discrepancies between the treated and control group. To answer the first question of the thesis "Does the SI tick size regime alter SIs’ market composition?" we test the statistical significance of the difference between the treated and control groups' market share in SIs. We conduct a difference-in-difference (DiD) regression of the weekly observations of market shares before and after the tick size regime.
The treated group constitutes those stocks constrained by their tick size, and the control group constitutes those stocks that are unconstrained. Similar to Foley et al. (2020), we cluster the standard errors on the stock level as we observe the stocks in our sample to correlate within each group, e.g., the treated group's mean market capitalisation is SEK 246,874 million, while the control group's mean is SEK 96,098 million (see Table C in the appendix). The p-values are higher with clustered standard errors as it allows for correlation between these stocks. Further, note that the trade volume for each stock, in their respective group, cannot be distinguished from orders less than the SMS. Hence, when conducting the regression, we may experience an unobservable spillover effect.
Table 3 presents the DiD regression where the first row shows the DiD estimator (!-values) and the differential effect between the treated and control group. We see a variation in the direction by the tick size for the venues. However, none of these variations are statistically significant. Even after controlling for the total amount traded in SEK, volatility, market capitalisation and stock-fixed effects. Hence, the desired effects by ESMA have not been achieved as we cannot reject hypothesis 1a. The results remain unaffected if either the control variables or stock fixed-effects are removed.
The control variables in Table 3 are the weekly logarithms of total amount traded ("#$%&%!), volatility ('#()*+(+*,!) and the logarithm of the market capitalisation ("#$-)./0*1)2!). The total amount traded in the stock is statistically significant at the 1% level for dark venues and 5% level for lit venues. The effect of a one hundred per cent increase in the weekly amount traded in SEK indicates that the market shares and trading activity on dark venues increase by 2.7%. In contrast, the effect on market shares for lit venues decrease by 2.2%.
'#()*+(+*,! exhibits a decreasing effect on SIs where an increase of one hundred per cent in the stocks’ volatility would reduce SI market shares by 30.1%. This effect is significant at the 5%
level. The effect is statistically significant at the 1% level on Nasdaq Stockholm and 10% for SI. One hundred per cent increase would decrease the market shares by 24.8% for Nasdaq Stockholm while increasing by 24% for SIs.
Table 3: Difference-in-Difference; The Composition of Market Shares, Main Period market share at date ( for stock 3 during 4/14 to 9/25/20 where market share is equal to the proportion of trading volume at the “venue” of interest across all venues. # measures the differences in market share between the treated and control group before and after the SI tick size regime. $%&'(&)! takes value 1 if the stock is included in the treated group and 0 otherwise. *+,(" takes value 1 if date ( is after or equal to 6/26 and 0 otherwise. .+/$0$!" is the weekly logarithm of the total trading amount at date ( for stock 3. 1+2'(323(!!" is the weekly volatility at date ( for stock 3. .+/4'%5&(6'7!" is the weekly logarithm of the market capitalisation at date ( for stock 3. 8! and 9" are individual and time-fixed effects. ***, **, * display the significance at 1%, 5% and 10% levels respectively whereas the t-values are in the brackets. The standard errors are clustered on stock level. Kinnevik is omitted due to data insufficiencies.
5.2.2. Market quality
To answer the second research question of the thesis, "What are the effects of the SI tick size regime on market quality at Nasdaq Stockholm?" we conduct a difference-in-difference regression with daily observations of the market quality variables before and after the SI tick size regime. In contrast to previous regression, we can now distinguish orders below SMS, providing a more precise estimate of the event because SIs are obliged to follow the pre-trade transparency requirements. In addition, we include clustered standard errors on the stock level with a similar purpose as discussed in section 5.2.1.
Figure 4 visualises the total daily averages of the treated and control group's market quality variables before and after the event, marked by the dashed line. Initially, we observe that both groups experience an improvement in their market quality by lower spreads and greater depth.
In contrast with the similar patterns of the realised spread and the groups' depth, we can see that the differences between the groups' quoted spreads, effective spreads and price impact become smaller. Hence, following the SI tick size regime, the quoted and effective spread could have increased for the treated group.
By observing the SI tick size regime variable (!) as the DiD-estimator in Table 4, we see that the quoted and effective spread are positive and statistically significant at the 10% level. In comparison, the price impact is positive and statistically significant at the 5% level. That is, the implicit trading costs and transactions’ impact on the price increase for the treated group after controlling for the total amount traded, volatility, market capitalisation and stock-fixed effects.
Specifically, we observe that the quoted spread increases by 0.791 bps, the effective spread by 0.538 bps and the price impact by 0.704 bps following the SI tick size regime. Therefore, the higher-level goals of increasing market quality for the more transparent venues are not achieved by ESMA, and we can reject hypothesis 2a and 2b. While both groups experience improvements in their market quality components before and after the event, the effect of the SI tick size regime is less extensive for the treated group. If the event had not occurred, the decrease in quoted spread, effective spreads and price impact for the treated group should have been greater.
The realised spread and market depth are not statistically significant at the 10% level.
Figure 4. Market quality measurements
The control variables are reasonable in terms of magnitude and size. The second row, !"#$%$! exhibits a negative and statistically significant effect at the 1% level on quoted, effective and realised spread. One hundred per cent increase in the average total amount traded reduces the implicit trading costs by 2.153, 0.976 and 0.964 bps, respectively. Additionally, depth is positive and statistically significant at a 1% level which is reasonable since the pending trading interest in the order book is greater with an increasing order volume. &"'()*'*)+! has a significant effect on the quoted spread, effective spread, realised spread and price impact where the spreads increase from higher volatility. In contrast, it results in a negative effect on the realised spread and order book depth. !"#,(-./)0(1! is not significant for any measure of market quality.
If we remove the stock fixed-effects, not controlling for heterogeneity that may exist, individually and over time, it would alter our conclusions regarding the direction of quoted and effective spread changes. Specifically, removing the individual fixed-effects that controls for differences cross-sectionally, e.g., the industry the stocks operate in, alters our conclusion.
Further, if we did not cluster the standard errors on the stock level, we would reject hypothesis 2a and b with a significance level of 1% for the quoted spread, effective spread, price impact and depth.
Table 4: Difference-in-Difference; The Market Quality, Main Period
Quoted Spread Effective Spread Realised Spread Price Impact Depth
! 0.791* market quality measure at date ( for stock 3 during 4/6 to 9/25/20. Due to data insufficiencies, the dates 6/11 to 6/12, 7/22, 7/24, 9/3 to 9/4, 9/8 and 9/17 to 9/25/20 are omitted. # measures the differences in market quality between the treated and control group before and after the SI tick size regime. $%&'(&)! takes value 1 if the stock is included in the treated group and 0 otherwise. *+,(" takes value 1 if date ( is after or equal to 6/26 and 0 otherwise. .+/$0$!" is the daily logarithm of the total trading amount at date ( for stock 3. 1+2'(323(!!" is the daily volatility at date ( for stock 3. .+/4'%5&(6'7!" is the daily logarithm of the market capitalisation at date ( for stock 3. 8! and 9" are individual and time-fixed effects. ***, **, * display the significance at 1%, 5% and 10% levels respectively whereas the t-values are in the brackets. The standard errors are clustered on stock level.