Artificial Intelligence (AI) based Portfolio Selection Papers
- DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection (CIKM, 2024)
- FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition (KDD, 2024)
- Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools (Webconf, 2024)
- Cross-Insight Trader: A Trading Approach Integrating Policies with Diverse Investment Horizons for Portfolio Management (ICDE, 2024)
- Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysis (Neural Networks, 2024)
- Trend-Heuristic Reinforcement Learning Framework for News-Oriented Stock Portfolio Management (ICASSP, 2024)
- Multiagent-based deep reinforcement learning framework for multi-asset adaptive trading and portfolio management (Neurocomputing, 2024)
- HADAPS : Hierarchical Adaptive Multi-Asset Portfolio Selection (IEEE Access, 2023)
- FinBPM: A Framework for Portfolio Management-based Financial Investor Behavior Perception Model (EACL, 2023)
- A Deep Temporal Factor Analysis Method for Large Scale Financial Portfolio Selection (ICASSP, 2023)
- Online portfolio management via deep reinforcement learning with high-frequency data (Information Processing & Management, 2023)
- Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints (ICAIF, 2023)
- MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization (CIKM, 2022)
- DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. (AAAI, 2021)
- An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets (IJCAI, 2021)
- MAPS: Multi-Agent reinforcement learning-based Portfolio management System (IJCAI, 2020)
- Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization (ACL, 2020)
- Portfolio formation with preselection using deep learning from long-term financial data (Expert Systems with Applications, 2020)
- A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (Arxiv, 2017)
- STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading (Arxiv, 2024)
- A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist (Arxiv, 2024)
- MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading (KDD, 2024)
- EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading (AAAI, 2024)
- IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making (IJCAI, 2024)
- Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization (Arxiv, 2024)
- Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts (KDD, 2023)
- Efficient Continuous Space Policy Optimization for High-frequency Trading (KDD, 2023)
- StockFormer: Learning Hybrid Trading Machines with Predictive Coding (IJCAI, 2023)
- DeepScalper: A risk-aware reinforcement learning framework to capture fleeting intraday trading opportunities. (CIKM, 2022)
- Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach (AAAI, 2021)
- Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading (WebConf, 2021)
- AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks (AAAI, 2019)
- Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models (WebConf, 2024)
- Enhancing Few-Shot Stock Trend Prediction with Large Language Models (Arxiv, 2024)
- Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction (BigData, 2024)
- Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction (WSDM, 2023)
- Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (ACL, 2023)
- DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting (KDD, 2023)
- PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability (AAAI, 2023)
- FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns (AAAI, 2022)
- NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting (AAAI, 2022)
- Transformer-based attention network for stock movement prediction (Expert Systems with Applications, 2022)
- Stock market index prediction using deep Transformer model. (Expert Systems with Applications,2022)
- Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts (KDD, 2021)
- Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport (KDD, 2021)
- REST: Relational Event-driven Stock Trend Forecasting (WebConf, 2021)
- Forecasting daily stock trend using multi-filter feature selection and deep learning (Expert Systems with Applications, 2021)
- Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework (AAAI, 2020)
- Multimodal Multi-Task Financial Risk Forecasting (MM, 2020)
- A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM (Expert Systems with Applications, 2020)
- Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction (IJCAI, 2020)
- Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction (ICPR, 2020)
- Temporal Relational Ranking for Stock Prediction (TOIS, 2019)
- Enhancing Stock Movement Prediction with Adversarial Training (IJCAI, 2019)
- Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network (IEEE Access,2019)
- Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News (CIKM, 2018)
- Stock Movement Prediction from Tweets and Historical Prices (ACL, 2018)
- Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (WSDM, 2018)
- Foundation Models for Time Series Analysis: A Tutorial and Survey (KDD, 2024)
- Reinforcement Learning for Quantitative Trading (ACM Transactions on Intelligent Systems and Technology, 2023)
- Applications of deep learning in stock market prediction: Recent progress (Expert Systems with Applications, 2021)
- Financial time series forecasting with deep learning : A systematic literature review: 2005–2019 (Applied Soft Computing, 2020)
- LSTM-Copula Hybrid Approach for Forecasting Ris in Multi-Asset Portfolios (Preprint, 2025)
- Time-Series Premium Risk Prediction via Bidirectional Transformer (Transactions on Computational and Scientific Methods, 2025)
- Multimodal Data-Driven Factor Models for Stock Market Forecasting (Journal of Computer Technology and Software, 2025)
- TradExpert: Revolutionizing Trading with Mixture of Expert LLMs (Arxiv, 2024)
- PST: Improving Quantitative Trading via Program Sketch-based Tuning (Arxiv, 2023)
- Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting (Arxiv, 2023)
- ChatGPT Informed Graph Neural Network for Stock Movement Prediction (SSRN, 2023)
- The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges (Arxiv, 2023)
- An asset subset-constrained minimax optimization framework for online portfolio selection (Expert Systems with Applications, 2024)
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation (Computational Economics, 2024)
- Leveraging Vision-Language Models for Granular Market Change Prediction (Multimodal AI for Financial Forecasting Workshop (Muffin) at AAAI 2023)
- A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost (Expert Systems with Applications, 2023)
- Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation (IJCAI, 2022)
- FinSense: An Assistant System for Financial Journalists and Investors (WSDM, 2021)
- Quantitative Day Trading from Natural Language using Reinforcement Learning (NAACL, 2021)
- Hybrid Learning to Rank for Financial Event Ranking (SIGIR, 2021)
- Modeling financial uncertainty with multivariate temporal entropy-based curriculums (UAI, 2021)
- Optimization of conditional value-at-risk (Journal of risk 2, 2000)
- VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (EMNLP,2020)
- MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction (CIKM, 2020)
- Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market (Management Science, 1991)
- Portfolio Selection: Efficient Diversification of Investments (1959).