Topic
Trading, Microstructure & Execution
Order books, market making, execution, high-frequency data and trading signals.
- Papers featured
- 559
- Last 12 months
- 45
- Cited 100+
- 1
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- SSRN
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Featured papers in this topic with the most citations today.
- 23 Jan 2025741cites
FAST: Efficient Action Tokenization for Vision-Language-Action Models
A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
Machine learningIn Robotics
- 24 Aug 202352cites
Deep Reinforcement Learning for Active High Frequency Trading
A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
arXiv
- 21 Sep 202333cites
Transformers versus LSTMs for electronic trading
A comparison study of LSTM-based and Transformer-based models for financial time series prediction found that LSTM-based models perform better in difference sequence prediction, despite Transformer-based models having limited advantages in absolute price sequence prediction.
arXiv
- 30 Aug 202332cites
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading
JAX-LOB: The paper introduces JAX-LOB, the first GPU-powered limit order book simulator capable of processing multiple books simultaneously, designed for efficient large-scale simulations of LOB dynamics for research, calibration, and reinforcement learning training.
arXivIn Proceedings of the Fourth ACM International Conference on AI in Finance
- 14 Jun 202328cites
Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers
A deep learning method outperforms other approaches in estimating filltimes of limit orders.
arXivIn Quantitative Finance
- 10 Apr 202427cites
StockGPT: A GenAI Model for Stock Prediction and Trading
The study introduces StockGPT, a model that predicts stock return dynamics using AI, showcasing the potential of AI in complex financial investment decisions.
arXiv
- 3 Jan 202427cites
Deep Reinforcement Learning for Quantitative Trading
AI and machine learning are revolutionizing quantitative trading with advanced algorithms, including a new model, QTNet, that uses deep reinforcement learning to manage volatile financial data.
arXivIn 2024 4th International Conference on Electronics, Circuits and Information Engineering (ECIE)
- 28 Jun 202327cites
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Conditional generative models used for order book simulation, enhanced with adversarial attacks.
arXivIn Proceedings of the Fourth ACM International Conference on AI in Finance
- 24 May 202324cites
E-backtesting
A new backtesting procedure for Expected Shortfall forecasts is proposed using e-values and e-processes.
arXiv
- 5 Jun 202422cites
HLOB - Information Persistence and Structure in Limit Order Books
A new deep learning model, HLOB, has been developed for predicting Limit Order Book mid-price changes, outperforming nine other models and offering new insights into information distribution in Limit Order Books.
arXivIn Expert systems with applications
- 28 Sep 202321cites
Don't Let MEV Slip: The Costs of Swapping on the Uniswap Protocol
Efficiency and Slippage Analysis: The research analyzes the costs of trading on a decentralized exchange, showing that costs vary based on trade characteristics, and proposes that DEXs could be a trustworthy alternative to centralized exchanges for trading digital assets.
arXivIn Financial Cryptography
- 27 Nov 202420cites
Strict Universality of the Square-Root Law in Price Impact across Stocks: A Complete Survey of the Tokyo Stock Exchange.
Research using Tokyo Stock Exchange data supports the econophysics theory that average price impact follows a power law in relation to transaction volume.
arXivIn Physical review letters
Latest
- 9 Oct 20263fanfare
Event History Over Scale: Compact Transformers for Low-Latency Limit Order Book Forecasting
MBOFormer and MBOFusion, two causal transformers with under 70 kilobyte serialized state, predict limit order book price trends with sub-millisecond latency across three markets and four prediction horizons.
arXiv
- 9 Oct 20263fanfare
Latent Continuum of Regimes in Limit Order Book Dynamics
Analysis of high-frequency limit-order-book geometry finds market states form a continuous structure with a dominant latent coordinate capturing between 83.66% and 85.27% of covariance variation.
arXiv
- 9 Oct 20263fanfare
How Execution Assumptions Change Short-Horizon Sharpe Rankings: Evidence from a Synthetic Trading Benchmark
Varying execution realism from ideal fills to latency and impact reshuffles rankings of LLM and classical trading policies, showing how backtest conventions affect headline results.
arXiv
- 9 Oct 20262fanfare
Modelling Regime Shifts in Continuous Intraday Electricity Markets with State-dependent Hawkes Processes
A regime-switching Hawkes process models intraday electricity order flow across liquidity conditions, showing that stress mainly strengthens self-excitation rather than reshaping cross-side dynamics.
arXiv
- 9 Oct 20263fanfare
What Survives Honest Tes1ng? A Pre-registered Test of Eleven Trading Signals
Applies a strict pre-registered protocol to eleven trading signals across equity and futures markets, finding only trend survives development and hold-out periods with borderline significance after multiple-testing adjustment.
SSRN
- 9 Oct 20263fanfare
The Toll-Fenced Market: Documented Anomalies in Indian Equities Replicate but do Not Survive Retail Implementation Costs
Well-documented Indian equity anomalies replicate strongly in-sample but all fail to survive 12 to 32 basis point retail transaction costs; required directional accuracy reaches 72% at hourly horizons.
SSRN
- 2 Oct 20260cites
Certified Alpha Capacity: Statistical Evidence, Economic Lifetime, and Arbitrage under Decay
The research measures when a trading signal accumulates enough statistical evidence for deployment before its economic value decays, deriving exact feasibility thresholds.
arXiv
- 2 Oct 20260cites
Evolution of Market Microstructure in the Age of AI
A survey traces how market design has evolved from floor trading through electronic exchanges to AI agents, examining whether markets can allocate goods efficiently without full revelation.
arXiv
- 2 Oct 20260cites
Say, Echo, Do: Strategic Narratives and Revealed Positioning in Financial Markets
The research shows when to follow or fade financial narratives by studying the covariance between institutional statements and revealed trading positions using machine learning.
arXiv
- 2 Oct 20260cites
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
A lightweight module adds confidence estimates to limit-order-book forecasters, raising directional F1 by 0.11-0.15 when predicting the most confident 10% of trades.
arXiv
- 2 Oct 20260cites
ReLOBGen: Replayable Limit Order Book Message Generation
A method generates realistic limit order book messages guaranteed to be consistent with current market state, achieving 100% replayability and 2.7–3.6× speedup over existing approaches.
arXiv
- 2 Oct 20260cites
Packets, Transactions and Queues: Design Principles for HFT Systems from a Measurement Study of CME Market Data
Measurement of over a year of CME market data reveals transactions cluster within microseconds, yielding design principles: single-thread receivers never queue, two-thread splits reduce tail latency.
arXiv
- 2 Oct 20260cites
PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading
Reinforcement learning agent combining policy optimization with regime priors controls drawdown while blending learned and rule-based portfolio exposure on held-out equity data.
arXiv
- 2 Oct 20260cites
Agentic Limit Order Books: Phase Transitions and Market Impact
The study shows that reinforcement-learning traders in limit order books create phase transitions between orderly trading and volatile cascades, with non-linear market impact dynamics.
arXiv
- 2 Oct 20263fanfare
Order Flow Imbalance and Price Discovery on Prediction Markets: Evidence from Kalshi
Order flow imbalance predicts contemporaneous mid-price changes in Kalshi binary event contracts, with explanatory power varying from 0.29 for sports to 0.02 for macroeconomic events.
SSRN
- 2 Oct 20263fanfare
Spot Bitcoin ETF Flows and Bitcoin Returns: Timestamp Alignment and Asymmetric Feedback
Correcting for timestamp mismatch between ETF and Bitcoin markets, the study finds that same-day flows predict next-day returns at 1.67 percentage points per billion of inflow, while prior Bitcoin returns predict flows.
SSRN
- 2 Oct 20263fanfare
Does Crypto Trading Activity Follow New York Time? Cross-Venue and New York-London Evidence from Clock Changes
Bitcoin and Ether trading activity on major venues increases during the UTC window aligned with New York market open, with the effect appearing on both assets.
SSRN
- 2 Oct 20262fanfare
Evolution of Market Microstructure in the Age of AI From Double-Sided Continuous Auctions to Agentic Markets
A survey traces how market design rules evolved from floor trading to limit order books to automated market makers to agent-based trading systems.
SSRN
- 2 Oct 20262fanfare
Market Attention and Information Quality in On-Chain Prediction Markets: Evidence from Polymarket
On-chain prediction market analysis finds 68.2 percent of volume sits in well-calibrated markets with average seven-day pricing error of 0.03.
SSRN
- 2 Oct 20262fanfare
Order Flow Imbalance and Price Impact in Indian Equity Derivatives: Cross-Sectional Evidence from NSE F&O Markets
Multilevel order flow imbalance in Indian equity derivatives predicts mean-reverting price dynamics with information coefficients growing from -0.004 at ten seconds to -0.032 at sixty seconds.
SSRN
- 2 Oct 20262fanfare
One dollar, many prices: dealer-specific pricing of synthetic dollar funding
Comparing FX forwards in identical currency pairs and maturities, the study finds large pricing variation across dealers reflecting clientele and pricing power rather than funding costs.
RePEc
- 25 Sep 20260cites
Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
The 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.
arXiv
- 25 Sep 20260cites
Liquidity Provision and Rebate Design in Option Markets
Develops 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.
arXiv
- 25 Sep 20260cites
Feasible Multi-Asset Optimal Execution under Cash Constraints
Extends the Almgren-Chriss optimal execution framework to enforce intertemporal cash constraints, reducing peak cash drawdown while maintaining implementation shortfall in multi-asset rebalancing.
arXiv
- 25 Sep 20260cites
Rule-Based Pricing Algorithms and Market Outcomes: An Experimental Study
Experiments 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.
arXiv
- 25 Sep 20264fanfare
Algorithmic Collusion by Reinforcement-Learning Pricing Agents: Simulation Evidence and Implications for Financial Markets and Competition Law
Q-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.
SSRN
- 25 Sep 20263fanfare
Incentives at Play: Fee-Induced Volume on a Regulated Perpetual Futures Venue
Analysis 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.
SSRN
- 25 Sep 20263fanfare
Settlement Risk and Currency Markets
Hungary'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.
SSRN
- 25 Sep 20262fanfare
Fedspeak, LLM-Derived Signals, and High-Frequency Trading
Semantic and tonal shifts across sequential Federal Reserve communications generate significant intraday price movements and abnormal volume, revealing incomplete information absorption at initial announcement.
SSRN
- 25 Sep 20263fanfare
Elastic in cash, inelastic in repo: Hedge funds in the treasury and repo markets
Using 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.
RePEc