Research
The Playbook for Curating Liquidity in US Equity ATSs
Accessing and aggregating liquidity is standard for algo brokers, but institutions truly need liquidity curation. In this two-volume series, we present the research framework behind how we think about dark liquidity curation, covering firm orders and conditional orders across the full ATS landscape.
A Novel Transaction Cost Model Addressing the Microstructural Complexities of Futures Trading
In this paper, we introduce Pulse, BestEx Research’s futures transaction cost model. Traditional models adapted from equities fail to account for the structural complexities of futures markets. Pulse addresses these challenges by separating costs into clear components and capturing the nuances of shadow liquidity, tick size, and intraday variation. Empirical evidence shows that Pulse delivers robust and accurate benchmarks across products, expiries, and order sizes.
The Time of Day Effect: A Breakthrough in Trading Cost Optimization
In this paper, we examine how trading costs vary throughout the day, independent of volume, revealing a persistent Time of Day Effect. We identify consistent cost advantages in certain intraday periods and present practical strategies to exploit this effect, including the development of a new variant of the IS Zero algorithm.
Robust Intraday Volume Estimation for Schedule-Based Algorithms Using Machine Learning and Market Microstructure Insights
In this paper, we present a new methodology for forecasting intraday volume using machine learning and market microstructure insights. We classify U.S. equities into liquidity-based groups and generate tailored volume profiles to support schedule-based execution strategies. We also evaluate prediction accuracy using historical data, comparing the new approach to prior method and highlighting its impact on reduction in error.
An Empirical Analysis of Conditional Orders: Which ATSs Prevail in Race Conditions and Offer Unique Liquidity?
In this paper, we examine the race conditions that arise when using conditional orders and identify the Alternative Trading Systems (ATSs) that execution algorithms prioritize when multiple invites are received. We also analyze a large private dataset of conditional orders to identify which ATS provides the most unique block liquidity. Additionally, the study explores the market share of ATSs across different transaction size buckets, offering insights into determining the optimal minimum execution quantity.
ESCAPING THE TOXICITY TRAP: How Strategic Venue Analysis Optimizes Algorithm Performance in Fragmented Markets
Venue analysis is an essential tool for fine-tuning liquidity access to maximize the performance of execution algorithms, and yet it remains one of the most challenging aspects of Transaction Cost Analysis (TCA). The US equities market is fragmented across sixteen exchanges and over thirty off-exchange venues. Comparing the performance of execution venues is often oversimplified to measuring markouts across fills executed in each location. This broad view can be misleading because it ignores the nuances and complexity of the intent of the algorithm and the order type. In this paper, we introduce the fallacies that typical markout analysis can create, pushing beyond traditional views for a deeper understanding of how child orders contribute to parent order performance.
Is the Order Competition Rule a Windfall for Investors?
BestEx Research has published a detailed analysis of the SEC's proposed Order Competition Rule, examining how segmenting marketable retail orders on exchanges and routing them to competitive auctions would reshape the relationship between retail brokers, wholesalers, and institutional investors. The paper covers the mechanics of the proposed rule, its impact on price improvement and adverse selection costs, and our interpretive commentary on what it means for the future of retail execution.
The Dangers of Cut & Paste: The 5 Biggest Challenges of Optimizing Futures Execution Algorithms
Some execution algorithm providers approach the design of futures algorithms by starting with an equity algo base and editing the details to tailor to the specificities of trading futures. While this approach may create a fair foundation, on average, it leads to unnecessarily high trading costs—sometimes in extreme fashion—because futures come with their own unique market structure and market microstructure challenges. In this paper, we introduce five of the biggest challenges associated with futures execution and how they can impact execution costs, including volume prediction, navigating long queues for passive execution, handling widely varied volatility profiles, exchange matching rules, and 24-hour trading.
Accessing Single Dealer Platforms (SDPs) in Execution Algorithms: Penny-Wise and Pound-Foolish?
Single dealer platforms (SDPs) are alternative sources of liquidity for US equity execution algorithms, and the negative consequences of using them are not well understood. In this paper, we review the implications of taking liquidity from an SDP as opposed to an exchange or ATS and explain what traders can do about them. We also dispel some of the common marketing misconceptions we most often hear about using SDPs, including that reacting to IOIs eliminates information leakage, that markout analysis shows there's no impact of trading with SDPs on performance, and that brokers' segmentation of order flow has no additional impact on performance.
The Good, The Bad & The Ugly of Payment for Order Flow
This is only paper in the PFOF discussion to quantify its impact on transaction costs for institutional investors. In it, we address how wholesaling is different from market making on exchanges, how much price improvement retail investors really get by routing orders to wholesalers and how it compares to price improvement on exchanges, how much the bid-offer spread would narrow if retail order flow moved to exchanges, and how information asymmetry between wholesalers and exchange market makers affects bid-offer spreads.
Queue-Jumping & Strategic Limit Order Routing
For optimal routing of passive limit orders, one must determine which of the sixteen exchanges is most likely to yield a speedy fill. In this paper, we introduce two methods for dynamically identifying the most likely destination for marketable orders for strategic limit order routing. The results are intuitive in some respects, but surprising in others; exchange rankings are not in order of market share or access fees. Our findings show that those who understand brokers’ routing preferences can jump the queue ahead of your passive limit orders. Smart investors can reverse this disadvantage and improve their passive fill rates.
