https://www.ml-quant.com/topics/trading-microstructure-execution/Order books, market making, execution, high-frequency data and trading signals. en https://www.ml-quant.com/papers/arxiv/2609.22785/https://www.ml-quant.com/papers/arxiv/2609.22785/Fri, 25 Sep 2026 07:00:00 +0000The research extends adversarial reinforcement learning for market making to handle self-exciting order arrivals and price impact, using an LSTM module to improve robustness in complex microstructure environments. https://www.ml-quant.com/papers/arxiv/2609.26606/https://www.ml-quant.com/papers/arxiv/2609.26606/Fri, 25 Sep 2026 07:00:00 +0000Develops a nested optimization model for market making and rebate design in option markets, showing how exchanges can set fees to incentivize liquidity provision and improve market depth. https://www.ml-quant.com/papers/arxiv/2609.27786/https://www.ml-quant.com/papers/arxiv/2609.27786/Fri, 25 Sep 2026 07:00:00 +0000Extends the Almgren-Chriss optimal execution framework to enforce intertemporal cash constraints, reducing peak cash drawdown while maintaining implementation shortfall in multi-asset rebalancing. https://www.ml-quant.com/papers/arxiv/2609.26861/https://www.ml-quant.com/papers/arxiv/2609.26861/Fri, 25 Sep 2026 07:00:00 +0000Experiments show that algorithm design features like warnings, pre-configured strategies, and LLM advice raise market prices by increasing starting prices and fostering cooperative algorithm designs. https://www.ml-quant.com/papers/ssrn/7500483/https://www.ml-quant.com/papers/ssrn/7500483/Fri, 25 Sep 2026 07:00:00 +0000Q-learning pricing agents in simulated duopolies reach supracompetitive outcomes with no communication, achieving collusion indices of 0.778 and 40% profit gains over competitive benchmarks. https://www.ml-quant.com/papers/ssrn/7512338/https://www.ml-quant.com/papers/ssrn/7512338/Fri, 25 Sep 2026 07:00:00 +0000Analysis of Kalshi's regulated Bitcoin and Ethereum futures reveals that 39-48% of notional trades are mechanical fixed-size orders that vanish when fees are charged, indicating costless artificial volume rather than legitimate trading. https://www.ml-quant.com/papers/ssrn/7201787/https://www.ml-quant.com/papers/ssrn/7201787/Fri, 25 Sep 2026 07:00:00 +0000Hungary's 2015 adoption of payment-versus-payment settlement reduced currency excess returns by ten basis points, demonstrating settlement risk is a priced friction limiting arbitrage. https://www.ml-quant.com/papers/ssrn/7479619/https://www.ml-quant.com/papers/ssrn/7479619/Fri, 25 Sep 2026 07:00:00 +0000Semantic and tonal shifts across sequential Federal Reserve communications generate significant intraday price movements and abnormal volume, revealing incomplete information absorption at initial announcement. https://www.ml-quant.com/papers/repec/zbw-safewp-343098/https://www.ml-quant.com/papers/repec/zbw-safewp-343098/Fri, 25 Sep 2026 07:00:00 +0000Using German sovereign bond repo data, the research shows hedge funds are price-elastic in cash markets but highly inelastic in repo, inheriting elasticity from their cash-market counterparties. https://www.ml-quant.com/papers/repec/ags-aaea26-404354/https://www.ml-quant.com/papers/repec/ags-aaea26-404354/Fri, 25 Sep 2026 07:00:00 +0000The study finds that algorithmic trading lowers realized volatility but increases tail co-movement and asymmetry in China's corn and soybean futures markets. https://www.ml-quant.com/papers/arxiv/2602.23784/https://www.ml-quant.com/papers/arxiv/2602.23784/Wed, 04 Mar 2026 07:00:00 +0000A Model for Trade-Flow in Market Microstructure: TradeFM is a new AI model that improves the analysis of market structures by studying billions of trade events in stocks, leading to better simulations of financial returns. https://www.ml-quant.com/papers/ssrn/4466658/https://www.ml-quant.com/papers/ssrn/4466658/Sun, 28 Dec 2025 07:00:00 +0000It establishes optimal trading rules for multiple assets with predictable returns, showing performance benefits through simulations. https://www.ml-quant.com/papers/ssrn/4440516/https://www.ml-quant.com/papers/ssrn/4440516/Fri, 19 Dec 2025 07:00:00 +0000Romania struggles to attract sustainable investments because its major companies have low transparency and high greenhouse gas emissions. https://www.ml-quant.com/papers/arxiv/2512.12924/https://www.ml-quant.com/papers/arxiv/2512.12924/Fri, 19 Dec 2025 07:00:00 +0000The authors introduce a walk-forward validation technique for algorithmic trading that focuses on interpretability and robust testing, offering modest gains and strong protection against losses. https://www.ml-quant.com/papers/arxiv/2512.14662/https://www.ml-quant.com/papers/arxiv/2512.14662/Fri, 19 Dec 2025 07:00:00 +0000This paper presents a model-free method for pricing fixed-income assets and replicating liabilities, linking no arbitrage with a positive discount curve to guide investment and regulatory approaches. https://www.ml-quant.com/papers/arxiv/2512.14410/https://www.ml-quant.com/papers/arxiv/2512.14410/Fri, 19 Dec 2025 07:00:00 +0000A study found that efforts to reduce carbon emissions in the aluminum industry have led to illegal trade practices, highlighting the need for new customs enforcement strategies. https://www.ml-quant.com/papers/arxiv/2511.22101/https://www.ml-quant.com/papers/arxiv/2511.22101/Mon, 01 Dec 2025 07:00:00 +0000The report enhances a deep reinforcement learning model for liquidity provision in Uniswap V3, showing better performance and theoretical backing compared to the original. https://www.ml-quant.com/papers/arxiv/2511.04782/https://www.ml-quant.com/papers/arxiv/2511.04782/Wed, 12 Nov 2025 07:00:00 +0000Cutting the model’s future‑state complexity removes an expectations “trap,” gives a single equilibrium, and implies government spending always raises consumption (potentially by a large amount). https://www.ml-quant.com/papers/arxiv/2511.01486/https://www.ml-quant.com/papers/arxiv/2511.01486/Tue, 04 Nov 2025 07:00:00 +0000Shows how a mathematical model explains why prices and trader biases converge as traders get more information, and gives the best way to combine expert opinions to hunt for arbitrage. https://www.ml-quant.com/papers/arxiv/2510.24467/https://www.ml-quant.com/papers/arxiv/2510.24467/Tue, 04 Nov 2025 07:00:00 +0000Shows a perfectly informed but slow trader has a single optimal waiting time between trades set by execution costs and the price path's roughness (Hurst/fractal), supported by theory and data. https://www.ml-quant.com/papers/arxiv/2510.23183/https://www.ml-quant.com/papers/arxiv/2510.23183/Tue, 04 Nov 2025 07:00:00 +0000Liquid Private‑Equity Replication: Introduces PEARL, an AI method that reconstructs private‑equity returns from liquid assets using timing and leverage adjustments to better match quarterly PE benchmarks. https://www.ml-quant.com/papers/arxiv/2510.15709/https://www.ml-quant.com/papers/arxiv/2510.15709/Mon, 27 Oct 2025 07:00:00 +0000Accounting for model uncertainty makes insurers set higher, more conservative prices and liquidity buffers, widens capacity ranges, and produces much longer underwriting cycles with more time in low‑capacity states. https://www.ml-quant.com/papers/repec/kap-fmktpm-v-33-y-2019-i-2-d-10-1007-s11408-019-00331-6/https://www.ml-quant.com/papers/repec/kap-fmktpm-v-33-y-2019-i-2-d-10-1007-s11408-019-00331-6/Fri, 24 Oct 2025 07:00:00 +0000The paper discusses the effects of high-frequency trading on market factors like volatility, transaction costs, and liquidity, indicating varied opinions in the financial sector. https://www.ml-quant.com/papers/arxiv/2510.06095/https://www.ml-quant.com/papers/arxiv/2510.06095/Thu, 09 Oct 2025 07:00:00 +0000The article investigates the impact of smart contract protocols on market dynamics, focusing on their influence on price drift, trade size, and market depth in coupled markets. https://www.ml-quant.com/papers/arxiv/2510.01814/https://www.ml-quant.com/papers/arxiv/2510.01814/Fri, 03 Oct 2025 07:00:00 +0000The Santa Fe model, used for analyzing the dynamics of the limit order book, is reevaluated using kinetic theory, leading to a new equation for the order-book density profile and identifying a previous error by E. Smith and colleagues. https://www.ml-quant.com/papers/arxiv/2509.06510/https://www.ml-quant.com/papers/arxiv/2509.06510/Sat, 13 Sep 2025 07:00:00 +0000The study investigates the best way for a liquidity provider to withdraw liquidity in an automated market, balancing fees and potential losses, and offers insights into dynamic liquidity provision. https://www.ml-quant.com/papers/arxiv/2509.05107/https://www.ml-quant.com/papers/arxiv/2509.05107/Sat, 13 Sep 2025 07:00:00 +0000A new method for simulating financial market data transforms limit order book data into an image format and uses diffusion models to predict future states, showing top performance on LOB-Bench. https://www.ml-quant.com/papers/arxiv/2508.17837/https://www.ml-quant.com/papers/arxiv/2508.17837/Fri, 29 Aug 2025 07:00:00 +0000The paper uncovers the inherent bistability and complex dynamics of an artificial stock market exchange, which emerge from micro-level trading rules. https://www.ml-quant.com/papers/arxiv/2508.17086/https://www.ml-quant.com/papers/arxiv/2508.17086/Fri, 29 Aug 2025 07:00:00 +0000The study suggests a learning framework to enhance the detection of trade-based manipulation in financial markets, with Transformer-based architectures proving most successful. https://www.ml-quant.com/papers/arxiv/2508.08152/https://www.ml-quant.com/papers/arxiv/2508.08152/Tue, 12 Aug 2025 07:00:00 +0000The research investigates the earnings of passive liquidity providers in automated markets, suggesting that optimal fees should balance volume attraction and revenue generation, and dynamic fees can enhance results.