Adjusted Z-Score Benchmark for Order Execution Quality
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Solution Overview
Problem
Conventional benchmarks for evaluating order execution quality in financial markets are limited by contextualization issues and potential manipulation, failing to provide a comprehensive picture across markets, instruments, and order duration, and are often based on limited access to price quotes.
Innovation Solution
The evaluation system calculates an adjusted Z score using time-weighted standard deviation of quote data during the order's lifetime, incorporating bid, ask, and midpoint prices to assess order execution quality, providing a normalized benchmark that accounts for market dynamics and reduces manipulation risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional benchmarks based on market trade information are used to evaluate order execution quality, then the evaluation can be performed with available data, but the evaluation fails to provide a complete picture and is susceptible to manipulation
Solution Approach 1:
The patent changes the data parameter from trade information to quote information (bid, ask, midpoint prices). Quote data provides a more complete picture of price movements and is less susceptible to manipulation since quotes are published by market makers rather than derived from executed trades. This parameter change directly addresses both the reliability and information completeness issues.
2Adaptability or versatility
If conventional benchmarks are used, then the evaluation system can operate with limited data access, but the evaluation cannot capture total price movement across markets and instruments
Solution Approach 1:
The patent creates a universal evaluation methodology that works across different markets, instruments, and order durations by using standardized quote data processing. The adjusted Z-score benchmark can be applied uniformly to stocks, bonds, futures, and other financial instruments, providing consistent order execution quality measurement across diverse trading environments.
Solution Approach 2:
By switching from trade-based to quote-based parameters, the system captures total price movement more completely. Quote data includes bid, ask, and midpoint prices that reflect the full range of price activity, enabling comprehensive evaluation across all markets and instruments regardless of trading volume or liquidity conditions.
3Ease of manufacture
If conventional benchmarks are used, then the evaluation can be implemented with current market data services, but the evaluation is limited by user access and data extraction capabilities
Solution Approach 1:
The patent uses adjusted Z-score benchmarks calculated from quote data, which maintains implementation feasibility since quote data is widely available through standard market data services. The methodology remains computationally straightforward while significantly improving measurement precision through the use of midpoint prices and standardized statistical calculations that reduce sensitivity to data access limitations.
Data Source
AI summary
The present disclosure generally provides techniques for analyzing and displaying the order execution quality of market instruments traded during a relevant period, or lifetime, of the order. The utilization of quote information in calculating a normalization factor allows for comparisons across instruments, orders, and days for instruments with a publicly available price irrespective of executed volume. A calculated Z score illustrates the quality of an order execution for a specific traded time as compared to all possible random executions. Moreover, the techniques disclosed herein allow for the comparison of execution quality across market instruments, orders sizes, and other variables via a graphical user interface and other data visualization tools, and can encapsulate evaluation methods using other adjusted Z score thresholds and/or alternatively take into account desirable volume weighting when calculating the standard deviation.