Stock Prediction
26 papers with code • 0 benchmarks • 4 datasets
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Latest papers with no code
Higher-order Graph Attention Network for Stock Selection with Joint Analysis
H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis.
HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE
Factor model is a fundamental investment tool in quantitative investment, which can be empowered by deep learning to become more flexible and efficient in practical complicated investing situations.
Support for Stock Trend Prediction Using Transformers and Sentiment Analysis
However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows.
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty
Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction.
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem.
Machine Learning for Stock Prediction Based on Fundamental Analysis
A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks historical data.
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN.
Stock Movement Prediction Based on Bi-typed and Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Stock Movement Prediction (SMP) aims at predicting listed company's stock future price trend, which is a challenging task due to the volatile nature of financial markets.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather information and predict certain stocks' prices (meme stock).
Selective transfer learning with adversarial training for stock movement prediction
To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset.