Multi-source aggregated classification for stock price movement prediction
Predicting stock price movements is a challenging task. Previous studies mostly used
numerical features and news sentiments of target stocks to predict stock price movements …
numerical features and news sentiments of target stocks to predict stock price movements …
Robust recurrent neural networks for time series forecasting
X Zhang, C Zhong, J Zhang, T Wang, WWY Ng - Neurocomputing, 2023 - Elsevier
Recurrent neural networks (RNNs) are widely utilized in time series forecasting tasks. In
practical applications, there are noises in real-life time series data. A model's generalization …
practical applications, there are noises in real-life time series data. A model's generalization …
Quantitative stock portfolio optimization by multi-task learning risk and return
Selecting profitable stocks for investments is a challenging task. Recent research has made
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
Temporal and heterogeneous graph neural network for financial time series prediction
The price movement prediction of stock market has been a classical yet challenging
problem, with the attention of both economists and computer scientists. In recent years …
problem, with the attention of both economists and computer scientists. In recent years …
A review of sentiment, semantic and event-extraction-based approaches in stock forecasting
Stock forecasting is a significant and challenging task. The recent development of web
technologies has transformed the communication channel to allow the public to share …
technologies has transformed the communication channel to allow the public to share …
Knowledge graph-based event embedding framework for financial quantitative investments
Event representative learning aims to embed news events into continuous space vectors for
capturing syntactic and semantic information from text corpus, which is benefit to event …
capturing syntactic and semantic information from text corpus, which is benefit to event …
Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane
PY Hao, CF Kung, CY Chang, JB Ou - Applied Soft Computing, 2021 - Elsevier
This study extracts the hidden topic model and emotional information from news articles. A
novel fuzzy twin support vector machine is also developed to merge the large volume of …
novel fuzzy twin support vector machine is also developed to merge the large volume of …
Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks
R Cheng, Q Li - Proceedings of the AAAI Conference on artificial …, 2021 - ojs.aaai.org
In finance, the momentum spillovers of listed firms is well acknowledged. Only few studies
predicted the trend of one firm in terms of its relevant firms. A common strategy of the pilot …
predicted the trend of one firm in terms of its relevant firms. A common strategy of the pilot …
HGNN: Hierarchical graph neural network for predicting the classification of price-limit-hitting stocks
In some stock markets, stock prices are not allowed to rise above a daily limit to restrain the
surge of price (called price limit). When the price limit occurs, investors tend to chase the …
surge of price (called price limit). When the price limit occurs, investors tend to chase the …
Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms
The recent advances usually mine market information from the chaotic data to conduct a
stock movement prediction task. However, the current stock price movement prediction …
stock movement prediction task. However, the current stock price movement prediction …