Applications of deep learning in stock market prediction: recent progress
W Jiang - Expert Systems with Applications, 2021 - Elsevier
Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …
both economists and computer scientists. With the purpose of building an effective prediction …
[HTML][HTML] A comprehensive review on multiple hybrid deep learning approaches for stock prediction
J Shah, D Vaidya, M Shah - Intelligent Systems with Applications, 2022 - Elsevier
Numerous recent studies have attempted to create efficient mechanical trading systems
through the use of machine learning approaches for stock price estimation and portfolio …
through the use of machine learning approaches for stock price estimation and portfolio …
Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM
Y Liang, Y Lin, Q Lu - Expert Systems with Applications, 2022 - Elsevier
Gold price has always played an important role in the world economy and finance. In order
to predict the gold price more accurately, this paper proposes a novel decomposition …
to predict the gold price more accurately, this paper proposes a novel decomposition …
[HTML][HTML] A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …
establishing new infrastructure in the power generation network. To deliver a high-quality …
Deep attentive learning for stock movement prediction from social media text and company correlations
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated
and intricate stock movement prediction task. Stock forecasting is complex, given the …
and intricate stock movement prediction task. Stock forecasting is complex, given the …
Scientometric review and analysis of recent approaches to stock market forecasting: Two decades survey
TO Kehinde, FTS Chan, SH Chung - Expert Systems with Applications, 2023 - Elsevier
Abstract Stock Market Forecasting (SMF) has become a spotlighted area and is receiving
increasing attention due to the potential that investment returns can generate profound …
increasing attention due to the potential that investment returns can generate profound …
Spatiotemporal hypergraph convolution network for stock movement forecasting
Stock movement prediction, a widely addressed research avenue in the world of computer
science and finance, it finds fundamental applications in quantitative trading and investment …
science and finance, it finds fundamental applications in quantitative trading and investment …
Stock market prediction via deep learning techniques: A survey
Existing surveys on stock market prediction often focus on traditional machine learning
methods instead of deep learning methods. This motivates us to provide a structured and …
methods instead of deep learning methods. This motivates us to provide a structured and …
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 …
Exploring the scale-free nature of stock markets: Hyperbolic graph learning for algorithmic trading
Quantitative trading and investment decision making are intricate financial tasks in the ever-
increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a …
increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a …