Transaction Cost Analysis (TCA) Reports
Last updated
Last updated
Our TCA reports focus on the execution quality of the Smart Order Router (SOR) itself. We hand-pick metrics that give the most insightful data, and use the data to draw conclusions about the SOR's performance in several different categories. You can see an example of the TCA report below.
Below is a description of the major parts of the report and the value they provide.
The executive summary will provide the most relevant data from the analysis, such as slippage. More importantly, it will provide the revealed characteristics/insight of the SOR performance that we find during our analysis. Table 1 illustrates the most crucial data for understanding SOR performance.
The most important metric for judging an SOR's performance is it's slippage. Comparing SOR slippage values will directly answer the question 'Which SOR can execute my schedule for the lowest cost?' The TCA report measures a normalized value of slippage (per $1M notional), and compares to standard benchmarks as a reference. Lower slippage values represent more cost savings. Figure 1 shows how we directly compare these strategies.
Building from the last section, here the report provides more details on the performance of the SOR and benchmark strategies. Statistical significance p-values (pair t-test) are shown as a rejection of the null hypothesis, giving strong evidence for SOR performance.
The hit rate, also known as routing accuracy, measures the percentage of orders routed to venues providing the best cost. The metric helps us understand slippage values, where high slippage values could be understood through low hit rates.
Markouts measure the mid-price movement a short time after execution, normally driven through adverse selection. Large markouts could hint at large price impact, which the SOR optimizes for, but could lead to higher slippage. Markouts can be caused from a large amount of factors, including toxic flow, highly volatile market conditions, or time of day execution / liquidity shortage. Slippage can be affected from markouts, especially in subsequent orders of a large parent order.
The sharpe ratio is calculated for different market conditions, giving a better look at the risk/variance. This builds intuition on the risky-ness of trading in different scenarios, and informs the trader of market states where the highest returns and lowest risk are to be expected.
The report groups slippage values stratified by market conditions. This gives particularly insightful guidance on when to optimally use the SOR. For example, table 5 shows a preference for SOR usage during closing time and high market volatility.
The conclusion offers actionable recommendations to the trader based on the above analysis. Advice to improve execution performance is given, with suggestions to gather more data in missing areas if necessary.
