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Found 2 papers, 2 papers with code
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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