Key papers

• Transformer for Times Series: an Application to the S&P500 (2024)
  Pierre Brugiere and Gabriel Turinici
  4.21
• PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management (2026)
  Yuxuan Zhao et al.
  4.08
• Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction (2025)
  So-Yoon Cho et al.
  3.38
• A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction (2025)
  Zhipeng Liu et al.
  2.88
• Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks (2026)
  Rylan Wade
  1.83
• Deep Learning Forecasting of the U.S. Aggregate Bond Index (2026)
  Ajay Kumar Verma et al.
  1.83
• Financially Guided Deep Portfolio Optimization (2026)
  Rahul Fernandes et al.
  1.83
• Forecasting Tangency Portfolios and Investing in the Minimum Euclidean Distance Portfolio to Maximize Out-of-Sample Sharpe Ratios (2026)
  Nolan Alexander et al.
  1.78
• Generative AI for Stock Selection (2026)
  Keywan Christian Rasekhschaffe
  1.67
• Fast Times, Slow Times: Timescale Separation in Financial Timeseries Data (2026)
  Jan Rosenzweig
  1.61
• Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting (2025)
  Anna Perekhodko et al.
  1.56
• The Aligned Economic Index & The State Switching Model (2025)
  Ilias Aarab
  1.56
• Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization (2025)
  Jun Kevin et al.
  1.50
• Re(Visiting) Time Series Foundation Models in Finance (2025)
  Eghbal Rahimikia et al.
  1.50
• Signature-Informed Transformer for Asset Allocation (2025)
  Yoontae Hwang et al.
  1.44
• Market-Based Variance of Market Portfolio and of Entire Market (2025)
  Victor Olkhov
  1.44
• On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model (2025)
  Jan Kwiatkowski and Jaros{\l}aw A. Chudziak
  1.44
• Increase Alpha: Performance and Risk of an AI-Driven Trading Framework (2025)
  Sid Ghatak et al.
  1.39
• From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions (2025)
  Yun Lin et al.
  1.39
• Solving dynamic portfolio selection problems via score-based diffusion models (2025)
  Ahmad Aghapour et al.
  1.28
• Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints (2025)
  Zihan Lin et al.
  1.28
• Markowitz Variance May Vastly Undervalue or Overestimate Portfolio Variance and Risks (2025)
  Victor Olkhov
  1.28
• From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting (2025)
  Giorgos Demosthenous et al.
  1.22
• Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting (2025)
  Fiona Xiao Jingyi et al.
  1.22
• NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks (2025)
  Shunyao Wang and Ming Cheng and Christina Dan Wang
  1.17
• LLM-Enhanced Black-Litterman Portfolio Optimization (2025)
  Youngbin Lee et al.
  1.11
• MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents (2025)
  George Fatouros et al.
  1.00
• Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance (2025)
  Adil Rengim Cetingoz and Charles-Albert Lehalle
  0.94
• Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2016)
  Leopoldo Catania and Anna Gloria Bill\'e
  —
• Time-varying neural network for stock return prediction (2020)
  Steven Y. K. Wong (1) et al.
  —
• Stock Movement Prediction with Financial News using Contextualized Embedding from BERT (2021)
  Qinkai Chen
  —
• Forecasting Financial Market Structure from Network Features using Machine Learning (2021)
  Douglas Castilho et al.
  —
• Autoencoding Conditional GAN for Portfolio Allocation Diversification (2022)
  Jun Lu et al.
  —
• A Hybrid Approach on Conditional GAN for Portfolio Analysis (2022)
  Jun Lu et al.
  —
• A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks (2022)
  Jaydip Sen and Abhishek Dutta
  —
• Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models (2022)
  Dorien Herremans et al.
  —
• Bi-LSTM Price Prediction based on Attention Mechanism (2022)
  Jiashu Lou et al.
  —
• Robustifying Markowitz (2022)
  Wolfgang Karl H\"ardle and Yegor Klochkov and Alla Petukhina and Nikita Zhivotovskiy
  —
• A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management (2022)
  MohammadAmin Fazli et al.
  —
• Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies (2023)
  Wee Ling Tan et al.
  —
• An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition (2023)
  Joseph de Vilmarest et al.
  —
• A Natural Copula (2023)
  Peter B. Lerner
  —
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models (2023)
  Yichi Zhang et al.
  —
• Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction (2023)
  Sheng Xiang et al.
  —
• E2EAI: End-to-End Deep Learning Framework for Active Investing (2023)
  Zikai Wei et al.
  —
• Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning (2023)
  Joel Ong et al.
  —
• Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns (2023)
  Eugene W. Park
  —
• A causal interactions indicator between two time series using extreme variations in the first eigenvalue of lagged correlation matrices (2023)
  Alejandro Rodriguez Dominguez et al.
  —
• Portfolio Optimization: A Comparative Study (2023)
  Jaydip Sen et al.
  —
• Company2Vec -- German Company Embeddings based on Corporate Websites (2023)
  Christopher Gerling
  —
• Portfolio Selection via Topological Data Analysis (2023)
  Petr Sokerin et al.
  —
• Learning to Learn Financial Networks for Optimising Momentum Strategies (2023)
  Xingyue Pu et al.
  —
• Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies (2023)
  Jakub Micha\'nk\'ow et al.
  —
• Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems (2023)
  Lele Cao et al.
  —
• NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading (2023)
  Hsiang-Hui Liu et al.
  —
• Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies (2023)
  Kieran Wood et al.
  —
• Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading (2023)
  Koti S. Jaddu and Paul A. Bilokon
  —
• Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series (2023)
  Woosung Koh et al.
  —
• RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction (2024)
  Yilun Wang et al.
  —
• DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models (2024)
  Divyanshu Daiya et al.
  —