Stock Prediction
26 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Stock Prediction
StockNet
Most implemented papers
Price graphs: Utilizing the structural information of financial time series for stock prediction
Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the chaotic property.
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
microsoft/qlib
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In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices.
Long Term Stock Prediction based on Financial Statements
This paper proposes a model with LSTM and fully connected layers to predict long term stock trendings based on financial statements.
Multi-modal Attention Network for Stock Movements Prediction
Traditionally, the prediction of future stock movements is based on the historical trading record.
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
microsoft/qlib
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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.
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Stock Movement Prediction (SMP) aims at predicting listed companies' 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 Stock Prediction
yonxie/advfintweet
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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.
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
xiaoting135/TRAN
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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.
Differential equation and probability inspired graph neural networks for latent variable learning
zshicode/latent-variable-gnn
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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.
