Stock Prediction
26 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Stock Prediction
StockNet
Latest papers
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.
Multi-modal Attention Network for Stock Movements Prediction
Traditionally, the prediction of future stock movements is based on the historical trading record.
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.
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.
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.
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.
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
Zhihan1996/TradeTheEvent
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Findings (ACL) 2021
In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles.
Stock price prediction using Generative Adversarial Networks
ChickenBenny/Stock-prediction-with-GAN-and-WGAN
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Journal of Computer Science 2021
In this paper, it proposes a stock prediction model using Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU) used as a generator that inputs historical stock price and generates future stock price and Convolutional Neural Network (CNN) as a discriminator to discriminate between the real stock price and generated stock price.
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways.
Improving S&P stock prediction with time series stock similarity
Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock.
