<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=15609&amp;fmt=gif">

react to sudden market shifts and predict remaining volume distribution

part 3_ipadExecution management systems that feature algorithmic trading strategies can save time by automating trades that would require days or weeks to complete. To ensure large, institutional trades are executed properly, accurate market volume forecasts can help protect an order from adverse selection.

Volume forecasting models typically focus on predicting expected volume. Predicting the available volume in the rest of the trading day is important to set order sizes for large institutional orders that will likely require multiple days to complete. It is also important to understand the uncertainty of remaining volume to avoid over and under reacting to sudden market shifts.

FactSet’s Trading Solutions team has developed a set of event-aware forecasting tools to handle the more challenging execution optimization problems. In this white paper, Event-Aware Forecasting, Part 3: Fat Tails in Trading Volume, we show that the remaining volume distribution can be modeled as a four-parameter fault-line distribution and provide estimates of the distribution parameters as a function of the time of day, the Obizhaeva-Kyle measure of a stock’s activity, and the observed volume surprise versus the expectation.

Download the third white paper in our series to discover how the event-aware model accounts for:

  • Greater front-loading of market volume following overnight stress
  • Calendar events and news, including earnings announcements
  • Federal Open Market Committee (FOMC) announcement days
  • Short market days on holidays

Complete the form to download your free copy of our white paper, Event-Aware Forecasting, Part 3: Fat Tails in Trading Volume.

Patrick Biddiscombe, CEO
At New Breed we believe Inbound is no longer just about filling your funnel. It is about customer acquisition and actually growing your business.

- Patrick Biddiscombe, CEO

Get the Event-Aware Model Forecasting White Paper