Stock Market Prediction
41 papers with code • 3 benchmarks • 4 datasets
Libraries
Use these libraries to find Stock Market Prediction models and implementations
StockNet
Latest papers
FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model
However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news.
Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
milenavuletic/Fin-GAN
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Quantitative Finance 2024
We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series.
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
Hidden Markov Models for Stock Market Prediction
In this article, we trained and tested a Hidden Markov Model for the purpose of predicting a stock closing price based on its opening price and the preceding day's prices.
A Multifactor Analysis Model for Stock Market Prediction
Stock Market predictions have historically been a problem tackled by different singular approaches even though markets are influenced by many different factors.
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network
Predicting the Stock movement attracts much attention from both industry and academia.
LERT: A Linguistically-motivated Pre-trained Language Model
ymcui/lert
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We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.
A Modular Framework for Reinforcement Learning Optimal Execution
In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution.
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
jinanzou/astock
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In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
PERT: Pre-training BERT with Permuted Language Model
ymcui/pert
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We permute a proportion of the input text, and the training objective is to predict the position of the original token.
