Optimizing Order Execution with AI: A New Approach to Market Impact

Summary : Quantum Signals is enhancing order execution for financial institutions by leveraging AI to predict intraday mid-price movements. By analyzing Level 2 limit order book data and applying advanced generative AI techniques, we aim to optimize trade execution strategies, improving efficiency and minimizing market impact for large orders.

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One of the significant challenges faced by traders and portfolio managers at large financial institutions—including investment banks, hedge funds, and retail banks—is the efficient execution of large orders. These professionals must carefully strategize how to place their orders to minimize market impact and avoid adverse price movements. For instance, placing a substantial buy order can drive up the asset’s price, while a large sell order can have the opposite effect, leading to ‘price slippage’. This phenomenon occurs when the execution price deviates from the expected price due to the visibility of open orders in the market.

To mitigate this issue, traders typically break down large orders into smaller chunks and execute them using strategies like Time-Weighted Average Price (TWAP) and Volume-Weighted Average Price (VWAP). TWAP distributes trades evenly over a specified period, while VWAP balances execution with market volume.

The Challenge of Optimal Execution

Optimal execution is crucial for financial institutions, including asset managers and hedge funds, aiming to minimize trading costs. These costs can significantly erode the expected profits from a trading strategy, which might explain why many active managers struggle to outperform passive index investing over the long term.

Trading costs can be categorized into two types:

• Direct Trading Costs: These are usually well-defined and include brokerage fees, which are straightforward to measure and typically range from 0.1 to 1 basis points.
• Indirect Trading Costs: These arise from market microstructure effects and fluctuations in supply and demand throughout the trading day. Indirect costs can be subtle and vary based on market liquidity and stock volatility, often ranging from a few basis points in liquid markets to higher amounts in less liquid markets.

Our Approach: Leveraging AI for Improved Execution

At Quantum Signals, we aim to revolutionize order execution by training AI models to predict intraday mid-price movements across various securities and markets. We utilize Level 2 limit order book (LOB) data, which provides insights into the volume traded at different price levels over time.

Inspired by recent advancements in Generative AI, which have shown remarkable success in natural language processing, we apply similar techniques to time series forecasting of market prices. Just as language models predict and generate sequences of words, our models will forecast price movements based on historical data, tracking order books in real time to provide predictions on mid-price trends.

Our focus is on short-term predictions, spanning from one to several minutes into the future, rather than high-frequency strategies or long-term market forecasts.

Optimizing Order Execution

Our AI-driven predictive signals aim to enhance traditional TWAP and VWAP strategies, making order execution more efficient. By offering robust and accurate mid-price change predictions, we help portfolio managers and traders optimize their execution strategies, ensuring better outcomes for their trading decisions.

Our Goals:

• Develop a B2B enterprise software service that provides financial institutions with reliable and precise signals on mid-price changes.
• Offer a recommendation service to assist traders in refining the execution of large orders.

Through this innovative approach, Quantum Signals is dedicated to improving market execution and supporting financial institutions in achieving their trading objectives.

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