Comprehensive review and comparative analysis of transformer models in sentiment analysis
Sentiment analysis has become an important task in natural language processing because it
is used in many different areas. This paper gives a detailed review of sentiment analysis …
is used in many different areas. This paper gives a detailed review of sentiment analysis …
Stock price prediction using a frequency decomposition based GRU transformer neural network
C Li, G Qian - Applied Sciences, 2022 - mdpi.com
Stock price prediction is crucial but also challenging in any trading system in stock markets.
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …
Deep LSTM and LSTM-Attention Q-learning based reinforcement learning in oil and gas sector prediction
DO Oyewola, SA Akinwunmi… - Knowledge-Based Systems, 2024 - Elsevier
Accurate prediction of stock market trends and movements holds great significance in the
financial industry as it enables investors, traders, and decision-makers to make informed …
financial industry as it enables investors, traders, and decision-makers to make informed …
Stock market prediction with time series data and news headlines: a stacking ensemble approach
R Corizzo, J Rosen - Journal of Intelligent Information Systems, 2024 - Springer
Time series forecasting models are gaining traction in many real-world domains as valuable
decision support tools. Stock market analysis is a challenging domain, characterized by a …
decision support tools. Stock market analysis is a challenging domain, characterized by a …
Forecasting the S&P 500 index using mathematical-based sentiment analysis and deep learning models: a FinBERT transformer model and LSTM
J Kim, HS Kim, SY Choi - Axioms, 2023 - mdpi.com
Stock price prediction has been a subject of significant interest in the financial mathematics
field. Recently, interest in natural language processing models has increased, and among …
field. Recently, interest in natural language processing models has increased, and among …
A deep fusion model for stock market prediction with news headlines and time series data
P Chen, Z Boukouvalas, R Corizzo - Neural Computing and Applications, 2024 - Springer
Time series forecasting models are essential decision support tools in real-world domains.
Stock market is a remarkably complex domain, due to its quickly evolving temporal nature …
Stock market is a remarkably complex domain, due to its quickly evolving temporal nature …
Integrating EEMD and ensemble CNN with X (Twitter) sentiment for enhanced stock price predictions
This research proposes a novel method for enhancing the accuracy of stock price prediction
by combining ensemble empirical mode decomposition (EEMD), ensemble convolutional …
by combining ensemble empirical mode decomposition (EEMD), ensemble convolutional …
Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis
Social media platforms have allowed many people to publicly express and disseminate their
opinions. A topic of considerable interest among researchers is the impact of social media …
opinions. A topic of considerable interest among researchers is the impact of social media …
Principal component-based hybrid model for time series forecasting
Parallel hybridization is one of the most well-established hybrid structures proposed in the
literature. Since an unavoidable high degree of multi-collinearity (MC) exists among …
literature. Since an unavoidable high degree of multi-collinearity (MC) exists among …
Impacts of Investor Attention and Accounting Information Comparability on Stock Returns: Empirical Evidence from Chinese Listed Companies
The efficient capital markets hypothesis (EMH) posits that security prices incorporate all
available information in capital markets. Nevertheless, real stock markets often exhibit …
available information in capital markets. Nevertheless, real stock markets often exhibit …