{"total":60,"rows":[{"id":"2107.09051","title":"AI in Finance: Challenges, Techniques and Opportunities","created":"2021-07-20","cited_by_count":81,"area":null},{"id":"2201.13094","title":"Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer","created":"2022-01-31","cited_by_count":22,"area":null},{"id":"2403.18910","title":"A Geometric Explanation of the Likelihood OOD Detection Paradox","created":"2024-03-27","cited_by_count":22,"area":null},{"id":"2101.07107","title":"Deep Reinforcement Learning for Active High Frequency Trading","created":"2021-01-18","cited_by_count":21,"area":null},{"id":"2112.08534","title":"Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture","created":"2021-12-16","cited_by_count":16,"area":null},{"id":"2105.13727","title":"Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection","created":"2021-05-28","cited_by_count":13,"area":null},{"id":"2105.10430","title":"Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units","created":"2021-05-21","cited_by_count":10,"area":null},{"id":"2209.10166","title":"Chaotic Hedging with Iterated Integrals and Neural Networks","created":"2022-09-21","cited_by_count":10,"area":null},{"id":"2102.08811","title":"Deep Learning for Market by Order Data","created":"2021-02-17","cited_by_count":8,"area":null},{"id":"2107.10941","title":"Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data","created":"2021-07-22","cited_by_count":8,"area":null},{"id":"2405.18938","title":"HLOB -- Information Persistence and Structure in Limit Order Books","created":"2024-05-29","cited_by_count":8,"area":null},{"id":"2108.00480","title":"Realised Volatility Forecasting: Machine Learning via Financial Word Embedding","created":"2021-08-01","cited_by_count":7,"area":null},{"id":"2403.09267","title":"Deep Limit Order Book Forecasting","created":"2024-03-14","cited_by_count":7,"area":null},{"id":"2110.04752","title":"How Robust are Limit Order Book Representations under Data Perturbation?","created":"2021-10-10","cited_by_count":6,"area":null},{"id":"2209.01018","title":"Normalization effects on deep neural networks","created":"2022-09-02","cited_by_count":6,"area":null},{"id":"2312.00626","title":"Forecasting trends in food security with real time data","created":"2023-12-01","cited_by_count":6,"area":null},{"id":"2602.23784","title":"TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure","created":"2026-02-27","cited_by_count":6,"area":null},{"id":"2311.03283","title":"Risk of Transfer Learning and its Applications in Finance","created":"2023-11-06","cited_by_count":5,"area":null},{"id":"2112.10139","title":"Denoised Labels for Financial Time-Series Data via Self-Supervised Learning","created":"2021-12-19","cited_by_count":4,"area":null},{"id":"2309.11400","title":"Transformers versus LSTMs for electronic trading","created":"2023-09-20","cited_by_count":4,"area":null},{"id":"2310.10500","title":"Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2502.15757","title":"TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2104.12895","title":"Computational Performance of Deep Reinforcement Learning to find Nash Equilibria","created":"2021-04-26","cited_by_count":3,"area":null},{"id":"2301.08688","title":"Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets","created":"2023-01-20","cited_by_count":3,"area":null},{"id":"2304.09840","title":"Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting","created":"2023-04-17","cited_by_count":2,"area":null},{"id":"2410.16858","title":"Dynamic graph neural networks for enhanced volatility prediction in financial markets","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2508.02247","title":"ByteGen: A Tokenizer-Free Generative Model for Orderbook Events in Byte Space","created":"2025-08-04","cited_by_count":2,"area":null},{"id":"2511.12563","title":"LOBERT: Generative AI Foundation Model for Limit Order Book Messages","created":"2025-11-16","cited_by_count":2,"area":null},{"id":"2107.00534","title":"The Limit Order Book Recreation Model (LOBRM): An Extended Analysis","created":"2021-07-01","cited_by_count":1,"area":null},{"id":"2109.13905","title":"Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow","created":"2021-09-28","cited_by_count":1,"area":null},{"id":"2206.14114","title":"On the universality of the volatility formation process: when machine learning and rough volatility agree","created":"2022-06-28","cited_by_count":1,"area":null},{"id":"2207.10539","title":"Estimating value at risk: LSTM vs. GARCH","created":"2022-07-21","cited_by_count":1,"area":null},{"id":"2210.14605","title":"Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots","created":"2022-10-26","cited_by_count":1,"area":null},{"id":"2305.15821","title":"Market Making with Deep Reinforcement Learning from Limit Order Books","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2310.12144","title":"Dynamic financial processes identification using sparse regressive reservoir computers","created":"2023-10-18","cited_by_count":1,"area":null},{"id":"2409.06514","title":"Limit Order Book Simulation and Trade Evaluation with $K$-Nearest-Neighbor Resampling","created":"2024-09-10","cited_by_count":1,"area":null},{"id":"2511.00190","title":"Deep reinforcement learning for optimal trading with partial information","created":"2025-10-31","cited_by_count":1,"area":"deeprl"},{"id":"2601.03464","title":"Prompting Underestimates LLM Capability for Time Series Classification","created":"2026-01-06","cited_by_count":1,"area":"mechinterp"},{"id":"2110.11156","title":"DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation","created":"2021-10-21","cited_by_count":0,"area":null},{"id":"2112.09015","title":"Multivariate Realized Volatility Forecasting with Graph Neural Network","created":"2021-12-16","cited_by_count":0,"area":null},{"id":"2205.08382","title":"Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy","created":"2022-05-11","cited_by_count":0,"area":null},{"id":"2306.05987","title":"Liquidity takers behavior representation through a contrastive learning approach","created":"2023-06-09","cited_by_count":0,"area":null},{"id":"2402.10803","title":"Modelling crypto markets by multi-agent reinforcement learning","created":"2024-02-16","cited_by_count":0,"area":null},{"id":"2403.18831","title":"DeepTraderX: Challenging Conventional Trading Strategies with Deep Learning in Multi-Threaded Market Simulations","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2409.05192","title":"Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets","created":"2024-09-08","cited_by_count":0,"area":null},{"id":"2505.22678","title":"An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2506.05764","title":"Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2506.08026","title":"TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2506.17989","title":"Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings","created":"2025-06-22","cited_by_count":0,"area":"synth"},{"id":"2510.24074","title":"Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2603.04328","title":"Algorithmic Compliance and Regulatory Loss in Digital Assets","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2603.19288","title":"Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction","created":"2026-03-09","cited_by_count":0,"area":null},{"id":"2604.09650","title":"Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks","created":"2026-03-29","cited_by_count":0,"area":null},{"id":"2606.08379","title":"TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution","created":"2026-06-07","cited_by_count":0,"area":"deeprl"},{"id":"2606.10412","title":"A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis","created":"2026-06-09","cited_by_count":0,"area":"deeprl"},{"id":"2607.09230","title":"When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures","created":"2026-07-10","cited_by_count":0,"area":null},{"id":"2607.23682","title":"Extreme Volatility Warning under Label Scarcity via Multi-Source Anomaly Fusion","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2609.16930","title":"Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.31491","title":"UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2610.20104","title":"What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}