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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.

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.

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.

In this paper, we highlight the challenges of venue analysis in TCA, focusing on the misleading nature of oversimplified venue comparisons that ignore the nuances of algorithm intent and order type. 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.

In this paper, we introduce IS Zero, a new category of execution algorithms designed to improve on VWAP to minimize implementation shortfall for low-urgency trading.

In this paper, we summarize the SEC's proposed Order Competition Rule, which aims to increase competition for retail investor orders by “requiring” retail brokers/wholesalers to expose these orders to competitive auctions, allowing for potential price improvements over the NBBO. We also analyze the impact on retail and institutional investors and offer interpretative commentary and suggestions.

In this paper, we discuss the pitfalls of designing futures algorithms based on equity algo foundations, which often leads to higher trading costs. We introduce five major challenges in futures execution—volume prediction, long queues for passive execution, varied volatility profiles, exchange matching rules, and 24-hour trading—and their impact on execution costs.

In this paper, we explore the implications of accessing Single Dealer Platforms (SDPs) in US equity execution algorithms, comparing them to exchanges and ATS. We address common misconceptions about SDPs, such as the effectiveness of IOIs in preventing information leakage, the reliability of markout analysis in showing no performance impact, and the impact of brokers’ order flow segmentation. We also provide insights on mitigating these issues.

In this paper, we quantify the impact of Payment for Order Flow (PFOF) on transaction costs for institutional investors. We examine the differences between wholesaling and market making on exchanges, the actual price improvement retail investors receive from wholesalers versus exchanges, the potential narrowing of bid-offer spreads if retail order flow moved to exchanges, and the effect of information asymmetry between wholesalers and exchange market makers on bid-offer spreads.

In this paper, we introduce two methods for dynamically identifying the best exchange for routing passive limit orders to achieve speedy fills. Our findings reveal that exchange rankings are not based on market share or access fees. We show how understanding brokers’ routing preferences can help smart investors improve their passive fill rates and avoid being disadvantaged by others who jump the queue.

This research paper highlights the partial recovery of metrics contributing to execution cost during the early days of the COVID-19 pandemic. Metrics include volume, spread, depth, and market impact, both on average and intraday.

This research paper explores metrics that contribute to execution cost during the early days of the COVID-19 pandemic. Metrics include volume, spread, depth, and market impact, both on average and intraday.