Prediction-Based Limit Order Trading

Managing the trade-off between volume and margin is among the most fundamental challenges for dealers in a securities market. We attempt to overcome this trade-off by incorporating predictions for buyer- and seller-initiated trades when submitting limit orders. Using the Avellaneda-Stoikov model as an example, we show how dealers can adjust quotes to predictions and thereby capture larger spreads at constant volume. Simulations on historical limit order book data illustrate that our model allows dealers to both increase market making revenues through trade flow-optimized positioning in the order book and reduce adverse selection cost through preempted adverse price movements