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Trading, Microstructure & Execution
Order books, market making, execution, high-frequency data and trading signals.
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Featured papers in this topic with the most citations today.
- 23 Jan 2025815cites
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
- 30 Aug 202338cites
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
- 21 Sep 202335cites
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
- 14 Jun 202329cites
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
- 28 Jun 202328cites
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 202328cites
E-backtesting
A new backtesting procedure for Expected Shortfall forecasts is proposed using e-values and e-processes.
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)
- 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
- 27 Nov 202422cites
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
- 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
- 9 Jan 202421cites
Backtest overfitting in the machine learning era: A comparison of out-of-sample testing methods in a synthetic controlled environment
The Combinatorial Purged Cross-Validation (CPCV) method is superior in financial analytics for reducing overfitting risks, outperforming traditional methods like K-Fold and Walk-Forward.
SSRNIn Knowl. Based Syst.
Latest
- 6 Nov 20263fanfare
Hard to Fool: A Bounded LLM Research Loop for Quantitative Finance, and What Two Dozen Campaigns Found
An open-source research loop constrains language-model proposals to strict JSON and enforces immutable data splits, discovery gates, and hash-chained logs to prevent false discoveries in backtest-driven strategy development.
arXiv
- 6 Nov 20263fanfare
Can We Stop the Backtest Yet? Anytime-Valid Promotion Tests for Adaptive Financial Research Agents
A protocol using e-processes and Ville arguments lets researchers monitor candidates born at data-dependent times and adaptively chosen, bounding false promotion rates without a fixed sample size.
arXiv
- 6 Nov 20263fanfare
Learning AI Agents, Market Makers, and Stress in Automated Markets
The paper models learning traders on automated market makers, showing herds and arbitrage-dominated markets have identical statistics yet stress episodes differing unboundedly in length, and that maker-taker pricing restores stability.
arXiv
- 6 Nov 20263fanfare
Herding or Racing? Decomposing Cross-Account Order-Flow Persistence in On-Chain Markets
Decomposing cross-account order-flow persistence on decentralized exchanges, the study isolates two components and finds that on-chain herding mostly reflects arbitrageurs racing to centralized prices, not independent traders.
arXiv
- 6 Nov 20263fanfare
Beyond Anonymous Order Flow: Wallet Identity as a Feature for Short-Horizon Return Prediction
Using pseudonymous wallet addresses from on-chain order books as features, the study finds persistent top-trader markouts reaching 3.11 basis points at ten seconds and improving one-second prediction.
arXiv
- 6 Nov 20263fanfare
TensorLOB: Structure-Preserving Tensor Learning for Limit Order Book Forecasting
A tensor-native model for limit-order-book forecasting preserves the multi-way structure of market side, price level, and order attributes, outperforming flattened matrix approaches on benchmarks.
arXiv
- 6 Nov 20262fanfare
Regret Bounds for Market Making with Persistent Orders
A deterministic market-making policy achieves logarithmic regret against fixed quotes and square-root regret against dynamic quotes when orders persist until execution.
arXiv
- 6 Nov 20263fanfare
Buy the Rumor, Sell the News: When is News Priced in?
Analyzing 1.68 million stock-day events from 4.57 million articles, the study finds that directional price moves concentrate before and at publication day, with little incremental return later.
SSRN
- 6 Nov 20263fanfare
The Illusion of Informed Congressional Trading: Evidence from Committee Networks and Hearings
Using machine learning to map firm disclosures to congressional committee jurisdictions, finds no evidence that members earn abnormal returns from legislative access or committee hearings.
SSRN
- 6 Nov 20262fanfare
Scoring Informed Trading in Prediction Markets: A Likelihood Ratio Framework
A likelihood-ratio framework ranks wallets by their information content in binary prediction markets, separating ranking from multiple testing and validating the method through simulation.
SSRN
- 6 Nov 20262fanfare
Contract-level Informational Efficiency in Prediction Markets: Measurable Ex Post, Not Predictable Ex Ante
Studying 244 resolved Kalshi markets, the research finds that efficiency is measurable ex post but not predictable ex ante, and naive trading strategies fail against market benchmarks.
SSRN
- 6 Nov 20262fanfare
How Costly Are Cross-Border Payments for Firms?
Analysis of quoted prices reveals that banks charge firms 2-3.5% for cross-border payments, with foreign exchange margins dominating costs and substantial price dispersion across banks for identical corridors.
RePEc
- 30 Oct 20262fanfare
Optimal Size in Market Making under State-Dependent Hawkes Process
A parametric volume model with state-dependent Hawkes order flow and quote-depth multipliers informs optimal placement policies that outperform baseline quoting in realized terms.
arXiv
- 30 Oct 20262fanfare
Optimal Order Placement under a State-Dependent Hawkes Process Limit Order Book model
A dynamic programming framework characterizes inventory-dependent optimal quotes for market makers facing Hawkes-driven order flow, tested on real limit-order-book data.
arXiv
- 30 Oct 20262fanfare
Calibration of Passive Market Impact with a Queue-Sensitive Propagator Model
A framework decomposing price into universal propagator and queue-sensitive corrections improves model fit and predicts passive limit-order impact better than classical propagator models.
arXiv
- 30 Oct 20263fanfare
When Collateral Prices Count: Recognition Clocks and Liquidation Batches
Research on decentralized lending shows that collateral revaluation rules predictably determine when and how many positions are liquidated together, with large price moves triggering batch liquidations far exceeding single-position boundaries.
SSRN
- 30 Oct 20263fanfare
The Value of Private Meetings with Central Bankers
Private bilateral meetings between ECB officials and financial institutions correlate with higher trading profits, suggesting selective communication conveys economically valuable central-bank information.
SSRN
- 30 Oct 20263fanfare
Who is Calling? Information Gathering versus Lobbying in Analyst Forecasts
Mobile-call data between brokerages and firms reveals that brokerage-initiated calls improve forecast accuracy, while firm-initiated calls reduce it, suggesting information gathering versus lobbying.
SSRN
- 30 Oct 20262fanfare
Daily mean Reversion in Global Equity Indices: Serial Dependence, Tail Risk, and Exploitation
Systematic trading strategies exploiting significant negative autocorrelation across six major equity indices achieve a pooled Sharpe ratio of 1.640 with regime filtering and downside-risk controls over 2002-2026.
SSRN
- 30 Oct 20262fanfare
Shallow versus Deep: The Anomaly in the Limit Order Book
Shallow-book imbalance shocks predict higher future volatility and positive returns, while deep-book shocks predict lower future volatility and negative returns, a pattern robust across Chinese stock constituents since 2014.
SSRN
- 30 Oct 20262fanfare
Picked Off in One Second: Adverse Selection and Out-of-Sample Defences for an NSE Index Options Market Maker
Analysis of live market-making on Indian options shows losses to adverse selection within one second, but regime-detection signals that pull quotes in stress cut losses by forty-six percent for NIFTY in early tests.
SSRN
- 30 Oct 20263fanfare
Offshore forwards and exchange rate transmission in emerging markets
Transaction-level data from centrally cleared NDFs shows offshore order flow moves both forward and spot rates, with a $1 billion long-USD order depreciating the spot by roughly 10 basis points.
RePEc
- 30 Oct 20262fanfare
Competing rails for crossborder payments: banks, fintechs, and stablecoins
Fintech money-transfer operators have compressed cross-border payment costs relative to banks; stablecoins do not eliminate frictions across corridors and payment values.
RePEc
- 23 Oct 20260cites
Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books
A Mamba sequence model trained on limit-order-book messages learns order-flow imbalance as an internal representation with R² ≈ 0.59 and generalizes across stocks and microstructure metrics.
arXiv
- 23 Oct 20260cites
A game-theoretic analysis of the market microstructure in decentralized exchanges with concentrated liquidity
The research models liquidity provision on decentralized exchanges as a mean-field game and derives closed-form solutions linking critical volatility to market microstructure parameters, validated on Ethereum data.
arXiv
- 23 Oct 20260cites
LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models
Injecting metaorders into neural limit-order-book generators reveals that market impact emerges naturally from next-token prediction, enabling benchmarking of realistic simulator behavior.
arXiv
- 23 Oct 20260cites
Intraday Forecasting Ability and Liquidity Provision of Institutional Traders
High-frequency data shows skillful traders have persistent predictive edge; one-standard-deviation improvement in forecasting ability raises position P&L by 1.02–1.19 units.
arXiv
- 23 Oct 20260cites
From Alpha to Execution: Trading Horizons, Regimes, and Schedules under Concave Impact and Hurst Scaling
Optimal execution windows maximize realized P&L by balancing alpha decay against concave impact costs; schedule shapes follow from convex optimization without depending on order size or impact coefficients.
arXiv
- 23 Oct 20260cites
Adversarial Post-Training of Foundation Models for High-Frequency Trading
Wasserstein GAN post-training on limit order book foundation models cuts error accumulation and improves realism by 17%, outperforming continued pre-training with far lower compute cost.
arXiv
- 23 Oct 20260cites
Equilibria and Learning in Competitive Market Making
The research characterizes pure-strategy Nash equilibria for multi-market-maker games, proves equilibria exist under minimal assumptions, and validates theoretical predictions via simulation.
arXiv