ai-portfolio-selection
Artificial Intelligence (AI) based Portfolio Selection Papers
https://github.com/dongheechoi/ai-portfolio-selection
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Other Approaches
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost
- Optimization of conditional value-at-risk
- VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls
- Modeling financial uncertainty with multivariate temporal entropy-based curriculums
- Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market
- Portfolio Selection: Efficient Diversification of Investments
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost
- Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation
- An asset subset-constrained minimax optimization framework for online portfolio selection
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost
- Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation
- Quantitative Day Trading from Natural Language using Reinforcement Learning
- Modeling financial uncertainty with multivariate temporal entropy-based curriculums
- Optimization of conditional value-at-risk
- VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls
- Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market
- Portfolio Selection: Efficient Diversification of Investments
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
- PST: Improving Quantitative Trading via Program Sketch-based Tuning
- Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
- ChatGPT Informed Graph Neural Network for Stock Movement Prediction
- The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Leveraging Vision-Language Models for Granular Market Change Prediction
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
- FinSense: An Assistant System for Financial Journalists and Investors
- Hybrid Learning to Rank for Financial Event Ranking
- MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction
- Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation
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AI-based Stock Prediction
- DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting
- Transformer-based attention network for stock movement prediction
- Stock market index prediction using deep Transformer model.
- Forecasting daily stock trend using multi-filter feature selection and deep learning
- A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM
- Enhancing Few-Shot Stock Trend Prediction with Large Language Models
- Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction
- PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability
- NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting
- Transformer-based attention network for stock movement prediction
- Stock market index prediction using deep Transformer model.
- Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction
- FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns
- Stock market index prediction using deep Transformer model.
- Forecasting daily stock trend using multi-filter feature selection and deep learning
- Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework
- Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction
- Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction
- Enhancing Few-Shot Stock Trend Prediction with Large Language Models
- NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting
- Transformer-based attention network for stock movement prediction
- PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability
- FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns
- Forecasting daily stock trend using multi-filter feature selection and deep learning
- Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction
- Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
- Enhancing Stock Movement Prediction with Adversarial Training
- Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
- Stock Movement Prediction from Tweets and Historical Prices
- Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction
- Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
- Enhancing Stock Movement Prediction with Adversarial Training
- Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
- Stock Movement Prediction from Tweets and Historical Prices
- Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
- Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
- Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
- Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
- REST: Relational Event-driven Stock Trend Forecasting
- Multimodal Multi-Task Financial Risk Forecasting
- Temporal Relational Ranking for Stock Prediction
- Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News
- Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
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Survey
- Applications of deep learning in stock market prediction: Recent progress
- Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
- Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
- Foundation Models for Time Series Analysis: A Tutorial and Survey
- Reinforcement Learning for Quantitative Trading
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AI-based Trading
- A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
- MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading
- EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
- IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
- Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
- StockFormer: Learning Hybrid Trading Machines with Predictive Coding
- Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach
- Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts
- STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading
- Efficient Continuous Space Policy Optimization for High-frequency Trading
- DeepScalper: A risk-aware reinforcement learning framework to capture fleeting intraday trading opportunities.
- Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading
- AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks
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