Global Trading examines six of the most influential trading and market microstructure papers published online in 2024.
Competition and Learning in Dealer Markets
A world in which AI-powered robots dominate trading may seem dystopian to some, but in their paper Hanna Assayag, Alexander Barzykin, Rama Cont, and Wei Xiong embrace this future. They analyse dealer behaviour in a hypothetical market consisting of autonomous market making agents with the ability to learn from experience. In a tour-de-force of theoretical exposition, the paper combines Nash equilibria (where players in a game can only win from others’ mistakes), mean-field theory (the physics of magnets), and reinforcement learning, the AI technique used by Google DeepMind to defeat the world’s best Go players. Their findings reveal that diversity among dealers helps prevent systematic over-bidding and under-offering (supra-competitive quoting strategies), suggesting that heterogeneity in dealer strategies contributes to more efficient market pricing for the market makers themselves.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4838181
Navigating the Murky World of Hidden Liquidity
Lit markets are often criticised for providing the illusion of liquidity, because fear of information leakage means most available liquidity remains concealed from view. Robert Bartlett and Maureen O’Hara seek to quantify this effect in U.S. equity markets, with a comprehensive multi-venue database of $467 billion of trades in which 40% of activity is hidden. Their research demonstrates how a simple machine learning model can assist broker-dealers in identifying hidden liquidity pools, and with better recall than conventional statistical models. With the help of a vendor dataset, they provide evidence of the bigger prevalence of hidden liquidity in high priced stocks (over $US100) as opposed to lower priced stocks (sub US$ 5)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4988855
Passive Market Impact Theory
There is a natural dichotomy in trading between liquidity-taking market orders and liquidity-providing limit orders. Executing entirely via limit orders is known as passive trading, and here Youssef Ouazzani Chahdi, Mathieu Rosenbaum, and Grégoire Szymanski introduce innovative models for understanding the market impact of such strategies. Previous work focused on the impact of metaorders when they are executed as market orders. Using a model known as the Hawkes propagator, here the authors provide theoretical predictions of the impact of meta orders executed passively – that is executed through providing liquidity on the bid or offer side of the market.
https://arxiv.org/abs/2412.07461
ETF Flow Dynamics and Market Bubbles: Ponzi funds
Efficient market theory states that securities prices reflect fundamental information but this view is under increasing attack from the market microstructure community. The latest assault comes from Philippe van der Beck, Jean-Philippe Bouchaud, and Dario Villamaina who argue that investors are unable to disentangle fundamentals from fund flows that inflate returns. Through anonymised thematic ETF flow data, they statistically examine the self inflated return and price impact of said ETF, as well as model price reversal events. They identify daily wealth reallocations of approximately 500 million dollars from ETF alone and propose a new regulatory metric called “fund illiquidity” to measure bubble risk.
https://arxiv.org/abs/2405.12768
Beyond the Bid–Ask: Strategic Insights into Spread Prediction and the Global Mid-Price Phenomenon
A hot topic in microstructure analysis is how the depth of limit order books (LOBs) affects quantitative finance questions such as option pricing and risk measurement. Yifan He, Abootaleb Shirvani, Barret Shao, Svetlozar Rachev, and Frank Fabozzi propose new LOB-based mid-price and spread metrics, incorporating the deeper liquidity in the total order book. Their findings, analysing these metrics for the stocks of Apple, Amazon, and Google, reveal heavy-tailed return distributions and innovative approaches to hedging liquidity risks with option pricing models. These insights refine trading strategies and risk management frameworks to take into account all available information within lit orderbooks.
https://arxiv.org/abs/2404.11722
Competitive Equilibria in Trading
Neil Chriss was a junior equity trader at Morgan Stanley in 1998 when he co-wrote the seminal paper ‘Optimal Liquidation’ which first explored the trade-off between market impact and volatility in execution strategies. After a distinguished career as an entrepreneur and hedge fund manager, Chriss has released a series of new papers. This one examines Nash equilibria (the best choice for each individual trader considering all information available) in multi-trader competition, providing closed-form solutions for equilibrium strategies and implementation costs. He highlights the benefits and drawbacks of different centralised trading strategies, optimised order flows, and the persistence of aggregate costs despite increased competition. These insights offer strategies for reducing trading costs in competitive markets. He demonstrates that naïve centralisation can result in increased cost through front running by other traders and an optimum centralised trading strategy can be found through splitting orders.
https://arxiv.org/abs/2410.13583
Which trading & markets microstructure research is important for you as a practitioner?
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