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Found 3 papers, 2 papers with code
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes
Stock portfolios are often exposed to rare consequential events (e. g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from.
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.
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