Stock Prediction
26 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Stock Prediction
Latest papers
Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
The training samples for the PPO trainer are also the responses generated during the reflective process, which eliminates the need for human annotators.
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data.
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network
Predicting the Stock movement attracts much attention from both industry and academia.
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors.
Differential equation and probability inspired graph neural networks for latent variable learning
Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of learning latent variable from observation.
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather information and predict movements stock prices.
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.