Stock Prediction

26 papers with code • 0 benchmarks • 4 datasets

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Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models

koa-fin/sep 6 Feb 2024

The training samples for the PPO trainer are also the responses generated during the reflective process, which eliminates the need for human annotators.

34
06 Feb 2024

Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction

koa-fin/dva 18 Aug 2023

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.

42
18 Aug 2023

Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network

JingyiGu/IndexGAN 27 Feb 2023

Predicting the Stock movement attracts much attention from both industry and academia.

11
27 Feb 2023

Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model

jinanzou/astock 14 Jun 2022

In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.

184
14 Jun 2022

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions

yonxie/advfintweet NAACL 2022

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.

19
01 May 2022

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

zshicode/attention-clx-stock-prediction 6 Apr 2022

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.

222
06 Apr 2022

Differential equation and probability inspired graph neural networks for latent variable learning

zshicode/latent-variable-gnn 28 Feb 2022

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.

4
28 Feb 2022

Graph-Based Stock Recommendation by Time-Aware Relational Attention Network

xiaoting135/TRAN ACM Transactions on Knowledge Discovery from Data 2022

For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios.

16
01 Feb 2022

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction

yonxie/advfintweet ACL ARR January 2022

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.

19
16 Jan 2022

DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

microsoft/qlib 11 Jan 2022

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.

14,021
11 Jan 2022