PFOF Analytics Tool - Case Study

Payment for Order Flow Analytics Tool

The Challenge

For firms that receive payment for order flow (“PFOF”), the economics are usually governed by a broker rate card, meaning the broker's schedule of payment terms. Those terms can vary based on factors such as order type, trading session, price range, and other execution characteristics.

The business question is simple: is the firm being paid what it expects, and is there a measurable opportunity to improve that result? In practice, answering that question is difficult because the outcome is distributed across numerous fills, various payment categories and a changing daily trading mix.

Knowtice Analytics provides that practical solution

Through this case study of a client’s actual use of Knowtice’s PFOF analytics tool, the utility of such a tool becomes apparent.

STEP 1

Applying Current Data

The client used its existing broker payment terms to build a current view within Knowtice's PFOF tool of expected payment for order flow.

  • That view was compared against actual payment results to confirm that the baseline accurately reflected the client's current payment economics. The client concluded that the modeled current payment was close to actual payment (within a margin of 1%), providing for confirmation of accuracy.

STEP 2

Applying Hypothetical Alternatives

Once the current baseline was established, the client used hypothetical rate cards within Knowtice's PFOF tool to test how different payment structures would affect the same underlying trading activity.

  • The comparison was not based on changing volume, changing customer behavior, or changing market conditions. It was based on repricing comparable flow under different broker payment terms. That made it possible to compare the current card and alternative cards on a like-for-like basis and to measure how much of the difference came from specific categories rather than from changes in trading activity.

STEP 3

Analyzing the Comparative Results

Within Knowtice's PFOF tool, the analysis is available at more than one level.

  • At the top level, the tool compares total current payments and total hypothetical payments for a given day or month.
  • At the category level, the tool shows which parts of the rate structure are driving the difference.
  • At the most detailed level, the client can review individual fills.
  • That level of detail becomes important. For an operations team, the details make the results explainable and reviewable. For an executive audience, the details prove that the conclusion is tied back to actual trading activity rather than to a broad estimate.

STEP 4

Negotiation of a More Favorable Rate Card

Using Knowtice's PFOF tool, the client tested various rate-card alternatives to quantify the likely effect of proposed changes to its current rate-card. On that basis, the client communicated to the executing broker the desired revisions with the knowledge of what revisions were most important and where non-material concessions could be offered. The executing broker acquiesced to those revisions.

The Results

After the revised rate became effective, the results became apparent within the first month. Although the client's trading volume stayed the same as compared to the previous month, the PFOF revenue increased materially.

  • The first month under the revised rate showed an increase of approximately 15%, representing increased PFOF revenues of a material high-five-figure month-over-month increase. For the 22 trading days of the month, the client earned several thousand dollars more per trading day, on average. Each trading day, the daily earnings increased, with percentage differences ranging from 8% to 23% for the month.
  • Knowtice's PFOF tool also pinpointed whether a different rate card could even pay more or which parts of the rate card mattered most. Separate scenario testing showed that not every alternative structure produced the same result.

One scenario with broader changes to non-core trading periods produced a smaller modeled increase (8% on a representative tracked sample day).

Another scenario that changed the economics of core non-marketable and sub-dollar flow produced the larger modeled increase (~15% on that same sample basis). That gave the client a more specific basis for broker discussions because it identified which categories appeared to have the greatest economic impact.

That distinction also mattered during negotiation. Not every category moved in the same direction. Some rate-card categories were reduced, while others improved.

In the end, the value of Knowtice’s PFOF tool was that it allowed the client to evaluate the full net effect rather than focusing on any one category in isolation. In other words, the client could determine whether concessions in one area were meaningfully offset by gains in other areas before deciding whether the revised rate card was economically favorable overall.

In this same manner, Knowtice Analytics is confident that it can help its broker-dealer clients to earn increased PFOF revenues without increased trading volume. Past the initial analysis, the tool tracks PFOF revenues on a daily basis, helping clients monitor the flow and the PFOF revenues it expects, keeping executing brokers honest and its revenues intact. Clients also use this tool to analyze whether a relationship with an additional executing broker has merit.

Knowtice Analytics is in the business of revenue optimization.

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