Execution Analytics

Led 3-person research team to optimize trading costs and implementation strategies to improve net client alpha at $1.1 tn global asset manager.● Spearheaded engagement with investment teams across equities and fixed income to communicate cost outcomes and educate investors about the drivers of trading costs; provided historical and cross-sectional context and custom analysis; applied proprietary attribution model to disentangle contributions to total implementation shortfall and identify levers for stakeholders to manage costs.● Drove the creation of a methodology and process for estimating investment strategy capacity in a systematic, unified, and data-driven way; unlocked ~$10 B in new capacity; custom analysis of liquidity constraints helped the firm win a $5 B mandate for a new liquidity-enhanced version of an existing fund.● Designed, implemented, and supported methodologies for liquidity stress testing and swing pricing for European funds and anti-dilution levies for US CITs.● Helped launch internal mentorship program for ~65 members of the department.

Directed 4-person team of researchers with strategic mandate to analyze and optimize the firm’s interaction with all types of liquidity.● Optimized use of traditional liquidity sources for electronic workflows, e.g., leveraged closing auctions to reduce execution costs for equities by 10%.● Devised and oversaw the analysis of continuous “A vs B” experiments to systematically explore and benchmark new and existing execution strategies in an objective, controlled, and statistically robust manner.● Built partnerships with brokers to drive the creation of innovative execution solutions customized to AQR order flow.● Drove the research and design of a quantitative intraday scheduling framework to dynamically optimize allocation of child slices across a set of execution tactics, accounting for stochastic fill rates, market impact, and intraday alpha.● Constructed transaction cost models for futures; intraday volume, volatility, and dynamic impact models for equities.● Supported and continuously improved in-house algo execution platform, e.g., developed Target Close strategy.

Senior member of algorithmic execution team at Systematic Global Macro firm; AUM $5 B. Group was responsible for design, implementation, and support of a comprehensive algorithmic execution platform for all systematic order flow (both short-term and daily systems) within the firm, across ~70 global Futures markets and ~20 liquid FX pairs.● Oversaw all aspects of algorithmic execution: prod support; onboarding of new geographies and markets; execution support and setup for new investment strategies; setting and executing the research agenda.● Architect and chief author of ground-up rewrite of futures execution algorithms targeting arrival price using new custom stochastic dynamic optimizer for intraday scheduling and child limit order placement, allowing for participation and intermediate fill constraints and accommodating joint optimization of multiple orders with different end times.● Responsible for tick data collection and mgmt; maintenance of market reference databases; maintenance of volatility, volume, market impact, and limit order models; 24x6 production support; key role in launch and support of spot FX execution solution.● At initial hire, conducted alpha research for systematic trading systems – development and optimization of technical indicators; dynamic volatility targeting, constrained portfolio optimization. One system deployed for live trading.

Member of Systematic Global Macro quant group (futures); daily VAR ~ $10 MM.● Managed operation and continuous improvements to G-10 + EM FX carry model; rebuilt backtesting engine and rebalance processes; applied shrinkage techniques in portfolio construction to significantly reduce portfolio turnover.

Member of four-person quant team at a multi-asset EM hedge fund; AUM $1.5 B.● Ran $100 MM long-short country-level quantitative equity trading strategy, based on technical and fundamental indicators in 22 emerging equity markets. Rewrote backtest, execution, and performance attribution infrastructure; researched extensions to the original model. Oversaw the full investment process from data validation to portfolio construction to trade generation, execution, and review; daily contact with trading, risk, and operations teams.● Presented monthly reviews to senior members of the investment team, discussing strategy performance and new idea research.

Extended thesis analysis to incorporate significantly more data with new background suppression techniques; prepared results for journal publication.