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https://github.com/dongheechoi/ai-portfolio-selection

Artificial Intelligence (AI) based Portfolio Selection Papers
https://github.com/dongheechoi/ai-portfolio-selection

List: ai-portfolio-selection

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Artificial Intelligence (AI) based Portfolio Selection Papers

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# AI-based Portfolio Selection Papers

* [Archived Repo](https://github.com/sangyx/deep-finance?tab=readme-ov-file#portfolio-selection)



Artificial Intelligence (AI) based Portfolio Selection Papers



* [DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection](https://arxiv.org/abs/2407.13427) (CIKM, 2024)

* [Cross-Insight Trader: A Trading Approach Integrating Policies with Diverse Investment Horizons for Portfolio Management](https://ieeexplore.ieee.org/abstract/document/10597980) (ICDE, 2024)

* [Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysis](https://www.sciencedirect.com/science/article/pii/S0893608024004611) (Neural Networks, 2024)

* [Trend-Heuristic Reinforcement Learning Framework for News-Oriented Stock Portfolio Management](https://ieeexplore.ieee.org/abstract/document/10447993) (ICASSP, 2024)

* [Multiagent-based deep reinforcement learning framework for multi-asset adaptive trading and portfolio management](https://www.sciencedirect.com/science/article/pii/S092523122400571X) (Neurocomputing, 2024)

* [HADAPS : Hierarchical Adaptive Multi-Asset Portfolio Selection](https://ieeexplore.ieee.org/document/10149353) (IEEE Access, 2023)

* [FinBPM: A Framework for Portfolio Management-based Financial Investor Behavior Perception Model](https://aclanthology.org/2024.eacl-long.15/) (EACL, 2023)

* [A Deep Temporal Factor Analysis Method for Large Scale Financial Portfolio Selection](https://ieeexplore.ieee.org/abstract/document/10095847) (ICASSP, 2023)

* [Online portfolio management via deep reinforcement learning with high-frequency data](https://www.sciencedirect.com/science/article/pii/S030645732200348X) (Information Processing & Management, 2023)

* [Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints](https://dl.acm.org/doi/abs/10.1145/3604237.3626906) (ICAIF, 2023)

* [MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization](https://dl.acm.org/doi/abs/10.1145/3511808.3557363) (CIKM, 2022)

* [DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding.](https://ojs.aaai.org/index.php/AAAI/article/view/16144) (AAAI, 2021)

* [An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets](https://www.ijcai.org/proceedings/2021/0373) (IJCAI, 2021)

* [MAPS: Multi-Agent reinforcement learning-based Portfolio management System](https://www.ijcai.org/proceedings/2020/623) (IJCAI, 2020)

* [Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization](https://aclanthology.org/2020.acl-main.307/) (ACL, 2020)

* [Portfolio formation with preselection using deep learning from long-term financial data](https://www.sciencedirect.com/science/article/pii/S0957417419307596) (Expert Systems with Applications, 2020)

* [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://arxiv.org/abs/1706.10059) (Arxiv, 2017)



## AI-based Trading

* [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://arxiv.org/abs/2402.18485) (Arxiv, 2024)

* [MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading](https://arxiv.org/abs/2406.14537) (KDD, 2024)

* [EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading](https://ojs.aaai.org/index.php/AAAI/article/view/29384) (AAAI, 2024)

* [IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making](https://www.ijcai.org/proceedings/2024/663) (IJCAI, 2024)

* [Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization](https://arxiv.org/abs/2403.07916) (Arxiv, 2024)

* [Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts](https://dl.acm.org/doi/abs/10.1145/3580305.3599424) (KDD, 2023)

* [Efficient Continuous Space Policy Optimization for High-frequency Trading](https://dl.acm.org/doi/abs/10.1145/3580305.3599813) (KDD, 2023)

* [StockFormer: Learning Hybrid Trading Machines with Predictive Coding](https://www.ijcai.org/proceedings/2023/0530) (IJCAI, 2023)

* [DeepScalper: A risk-aware reinforcement learning framework to capture fleeting intraday trading opportunities.](https://dl.acm.org/doi/10.1145/3511808.3557283) (CIKM, 2022)

* [Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach](https://ojs.aaai.org/index.php/AAAI/article/view/16127) (AAAI, 2021)

* [Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading](https://dl.acm.org/doi/abs/10.1145/3442381.3450095) (WebConf, 2021)

* [AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks](https://dl.acm.org/doi/abs/10.1145/3292500.3330647) (AAAI, 2019)



## AI-based Stock Prediction

* [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://dl.acm.org/doi/abs/10.1145/3589334.3645611) (WebConf, 2024)

* [Enhancing Few-Shot Stock Trend Prediction with Large Language Models](https://arxiv.org/abs/2407.09003) (Arxiv, 2024)

* [Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction](https://ieeexplore.ieee.org/abstract/document/10386751) (BigData, 2024)

* [Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://dl.acm.org/doi/abs/10.1145/3539597.3570427) (WSDM, 2023)

* [Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction](https://aclanthology.org/2023.acl-long.679/) (ACL, 2023)

* [DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting](https://dl.acm.org/doi/abs/10.1145/3580305.3599315) (KDD, 2023)

* [PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability](https://ojs.aaai.org/index.php/AAAI/article/view/25648) (AAAI, 2023)

* [FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns](https://ojs.aaai.org/index.php/AAAI/article/view/20369) (AAAI, 2022)

* [NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/21414) (AAAI, 2022)

* [Transformer-based attention network for stock movement prediction](https://www.sciencedirect.com/science/article/pii/S0957417422006170) (Expert Systems with Applications, 2022)

* [Stock market index prediction using deep Transformer model.](https://www.sciencedirect.com/science/article/pii/S0957417422013100) (Expert Systems with Applications,2022)

* [Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts](https://dl.acm.org/doi/abs/10.1145/3447548.3467297) (KDD, 2021)

* [Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport](https://dl.acm.org/doi/abs/10.1145/3447548.3467358) (KDD, 2021)

* [REST: Relational Event-driven Stock Trend Forecasting](https://dl.acm.org/doi/abs/10.1145/3442381.3450032) (WebConf, 2021)

* [Forecasting daily stock trend using multi-filter feature selection and deep learning](https://www.sciencedirect.com/science/article/pii/S095741742031099X) (Expert Systems with Applications, 2021)

* [Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework](https://ojs.aaai.org/index.php/AAAI/article/view/5445) (AAAI, 2020)

* [Multimodal Multi-Task Financial Risk Forecasting](https://dl.acm.org/doi/abs/10.1145/3394171.3413752) (MM, 2020)

* [A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM](https://www.sciencedirect.com/science/article/pii/S0957417420304334) (Expert Systems with Applications, 2020)

* [Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction](https://www.ijcai.org/proceedings/2020/0626) (IJCAI, 2020)

* [Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction](https://ieeexplore.ieee.org/abstract/document/9412695) (ICPR, 2020)

* [Temporal Relational Ranking for Stock Prediction](https://dl.acm.org/doi/abs/10.1145/3309547) (TOIS, 2019)

* [Enhancing Stock Movement Prediction with Adversarial Training](https://www.ijcai.org/proceedings/2019/0810) (IJCAI, 2019)

* [Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network](https://ieeexplore.ieee.org/document/8901118) (IEEE Access,2019)

* [Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News](https://dl.acm.org/doi/abs/10.1145/3269206.3269286) (CIKM, 2018)

* [Stock Movement Prediction from Tweets and Historical Prices](https://aclanthology.org/P18-1183/) (ACL, 2018) 

* [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://dl.acm.org/doi/10.1145/3159652.3159690) (WSDM, 2018)



## Survey

* [Reinforcement Learning for Quantitative Trading](https://dl.acm.org/doi/full/10.1145/3582560) (ACM Transactions on Intelligent Systems and Technology, 2023)

* [Applications of deep learning in stock market prediction: Recent progress](https://www.sciencedirect.com/science/article/pii/S0957417421009441) (Expert Systems with Applications, 2021)

* [Financial time series forecasting with deep learning : A systematic literature review: 2005–2019](https://www.sciencedirect.com/science/article/pii/S1568494620301216) (Applied Soft Computing, 2020)



## Other Approaches

* [An asset subset-constrained minimax optimization framework for online portfolio selection](https://www.sciencedirect.com/science/article/pii/S0957417424011655) (Expert Systems with Applications, 2024)

* [Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation](https://link.springer.com/article/10.1007/s10614-024-10699-x) (Computational Economics, 2024)

* [A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost](https://www.sciencedirect.com/science/article/pii/S0957417422016414) (Expert Systems with Applications, 2023)

* [Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation](https://www.ijcai.org/proceedings/2022/0550) (IJCAI, 2022)

* [FinSense: An Assistant System for Financial Journalists and Investors](https://dl.acm.org/doi/abs/10.1145/3437963.3441704) (WSDM, 2021)

* [Quantitative Day Trading from Natural Language using Reinforcement Learning](https://aclanthology.org/2021.naacl-main.316/) (NAACL, 2021)

* [Hybrid Learning to Rank for Financial Event Ranking](https://dl.acm.org/doi/abs/10.1145/3404835.3462969) (SIGIR, 2021)

* [Modeling financial uncertainty with multivariate temporal entropy-based curriculums](https://proceedings.mlr.press/v161/sawhney21a.html) (UAI, 2021)

* [Optimization of conditional value-at-risk](https://www.ise.ufl.edu/uryasev/files/2011/11/CVaR1_JOR.pdf) (Journal of risk 2, 2000)

* [VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls](https://aclanthology.org/2020.emnlp-main.643/) (EMNLP,2020)

* [MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction](https://dl.acm.org/doi/abs/10.1145/3340531.3412879) (CIKM, 2020)

* [Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market](https://www.jstor.org/stable/2632458) (Management Science, 1991)

* [Portfolio Selection: Efficient Diversification of Investments](https://www.jstor.org/stable/2975974?searchText=&searchUri=&ab_segments=&searchKey=&refreqid=fastly-default%3A93c79db50c6f70ff56cc64fdedc43b1a) (1959).