A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
The adoption of computer-aided stock trading methods is gaining popularity in recent years,
mainly because of their ability to process efficiently past information through machine …
mainly because of their ability to process efficiently past information through machine …
Stock price forecasting for jordan insurance companies amid the covid-19 pandemic utilizing off-the-shelf technical analysis methods
One of the most difficult problems analysts and decision-makers may face is how to improve
the forecasting and predicting of financial time series. However, several efforts were made to …
the forecasting and predicting of financial time series. However, several efforts were made to …
Multimodal multi-task financial risk forecasting
Stock price movement and volatility prediction aim to predict stocks' future trends to help
investors make sound investment decisions and model financial risk. Companies' earnings …
investors make sound investment decisions and model financial risk. Companies' earnings …
TM-vector: A novel forecasting approach for market stock movement with a rich representation of twitter and market data
Stock market forecasting has been a challenging part for many analysts and researchers.
Trend analysis, statistical techniques, and movement indicators have traditionally been used …
Trend analysis, statistical techniques, and movement indicators have traditionally been used …
MG-Conv: A spatiotemporal multi-graph convolutional neural network for stock market index trend prediction
Index trend prediction is a critical topic in the sphere of financial investment. An index trend
prediction model based on a multi-graph convolutional neural network termed MG-Conv is …
prediction model based on a multi-graph convolutional neural network termed MG-Conv is …
Forecasting price movements of global financial indexes using complex quantitative financial networks
As predicting trends in the financial market becomes more important, and artificial
intelligence technology advances, there is active research on predicting stock movements …
intelligence technology advances, there is active research on predicting stock movements …
A performance comparison of machine learning models for stock market prediction with novel investment strategy
Stock market forecasting is one of the most challenging problems in today's financial
markets. According to the efficient market hypothesis, it is almost impossible to predict the …
markets. According to the efficient market hypothesis, it is almost impossible to predict the …
Deep Learning techniques for stock market forecasting: Recent trends and challenges
M Patel, K Jariwala, C Chattopadhyay - Proceedings of the 2023 6th …, 2023 - dl.acm.org
Stock market forecasting has been a very intensive area of research in recent years due to
the highly uncertain and volatile nature of stock data which makes this task challenging. By …
the highly uncertain and volatile nature of stock data which makes this task challenging. By …
Forecasting financial time series using robust deep adaptive input normalization
Deep Learning provided powerful tools for forecasting financial time series data. However,
despite the success of these approaches on many challenging financial forecasting tasks, it …
despite the success of these approaches on many challenging financial forecasting tasks, it …
Research on carbon asset trading strategy based on PSO-VMD and deep reinforcement learning
J Zhang, K Chen - Journal of Cleaner Production, 2024 - Elsevier
As the financialization of carbon emission right, developing effective carbon asset trading
strategy is important for both investors and regulators. Traditional trading strategy based on …
strategy is important for both investors and regulators. Traditional trading strategy based on …