Topic
Trading, Microstructure & Execution
Order books, market making, execution, high-frequency data and trading signals.
- Papers featured
- 538
- Last 12 months
- 25
- Cited 100+
- 1
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- SSRN
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Most cited
Featured papers in this topic with the most citations today.
- 23 Jan 2025673cites
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 202351cites
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 202330cites
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
- 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
- 10 Apr 202426cites
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
- 24 May 202323cites
E-backtesting
A new backtesting procedure for Expected Shortfall forecasts is proposed using e-values and e-processes.
arXiv
- 5 Jun 202421cites
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
- 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
- 22 May 202420cites
Major Issues in High-frequency Financial Data Analysis: A Survey of Solutions
Recent studies on issues in high-frequency financial data analysis, such as nonstationarity and low signal-to-noise ratios, are categorized into data preprocessing and quantitative methods.
SSRN
Latest
- 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
- 25 Sep 20262fanfare
Taming Volatility, Feeding Crashes: Evidence from Algorithmic Trading in China's Agricultural Futures Markets
The study finds that algorithmic trading lowers realized volatility but increases tail co-movement and asymmetry in China's corn and soybean futures markets.
RePEc
- 4 Mar 20264cites
TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure
A 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.
arXiv
- 28 Dec 2025630shares
Optimal Trading with Costs and Predictability
It establishes optimal trading rules for multiple assets with predictable returns, showing performance benefits through simulations.
SSRNFeatured 2×
- 19 Dec 20253cites
Romania's Roadmap to a Greener Financial System: An analysis of Environmental, Social and Governance Reporting on the Bucharest Exchange Trading Index
Romania struggles to attract sustainable investments because its major companies have low transparency and high greenhouse gas emissions.
SSRNFeatured 2×
- 19 Dec 20250cites
Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals
The 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.
arXiv
- 19 Dec 20250cites
Fixed-Income Pricing and the Replication of Liabilities
This 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.
arXiv
- 19 Dec 20250cites
Pattern Recognition of Aluminium Arbitrage in Global Trade Data
A 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.
arXiv
- 1 Dec 20250cites
Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mamba
The report enhances a deep reinforcement learning model for liquidity provision in Uniswap V3, showing better performance and theoretical backing compared to the original.
arXiv
- 12 Nov 20250cites
A Step Towards a Solution to the Confidence-Driven Liquidity Trap Morass
Cutting 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).
arXiv
- 4 Nov 20250cites
Differential Beliefs in Financial Markets Under Information Constraints: A Modeling Perspective
Shows 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.
arXiv
- 4 Nov 20250cites
The Omniscient, yet Lazy, Investor
Shows 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.
arXiv
- 4 Nov 20250cites
PEARL: Private Equity Accessibility Reimagined with Liquidity
Liquid 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.
arXiv
- 27 Oct 20252cites
Robust insurance pricing and liquidity management
Accounting 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.
arXivIn Journal of Risk and Insurance
- 24 Oct 202590shares
HighFrequency Trading Impact
The paper discusses the effects of high-frequency trading on market factors like volatility, transaction costs, and liquidity, indicating varied opinions in the financial sector.
RePEc
- 9 Oct 20251cites
A Microstructure Analysis of Coupling in CFMMs
The 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.
arXiv
- 3 Oct 20250cites
Mean-field theory of the Santa Fe model revisited: a systematic derivation from an exact BBGKY hierarchy for the zero-intelligence limit-order book model
The 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.
arXivFeatured 2×
- 13 Sep 20253cites
Optimal Exit Time for Liquidity Providers in Automated Market Makers
The 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.
arXiv
- 13 Sep 20252cites
Painting the market: generative diffusion models for financial limit order book simulation and forecasting
A 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.
arXivFeatured 2×
- 29 Aug 20251cites
Bimodal dynamics of the artificial limit order book stock exchange with autonomous traders
The paper uncovers the inherent bistability and complex dynamics of an artificial stock market exchange, which emerge from micro-level trading rules.
arXivIn Communications in Nonlinear Science and Numerical Simulation
- 29 Aug 20251cites
Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning
The study suggests a learning framework to enhance the detection of trade-based manipulation in financial markets, with Transformer-based architectures proving most successful.
arXiv
- 12 Aug 20258cites
Optimal Fees for Liquidity Provision in Automated Market Makers
The 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.
arXiv