AI-based Portfolio Selection Papers

• Archived Repo

Artificial Intelligence (AI) based Portfolio Selection Papers

• DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection (CIKM, 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)

AI-based Trading

• 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)

AI-based Stock Prediction

• 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)

Survey

• 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)

Other Approaches

• 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)
• 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).