News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review
MN Ashtiani, B Raahemi - Expert Systems with Applications, 2023 - Elsevier
Researchers and practitioners have attempted to predict the financial market by analyzing
textual (eg, news articles and social media) and numeric data (eg, hourly stock prices, and …
textual (eg, news articles and social media) and numeric data (eg, hourly stock prices, and …
Artificial intelligence applied to stock market trading: a review
FGDC Ferreira, AH Gandomi, RTN Cardoso - IEEE Access, 2021 - ieeexplore.ieee.org
The application of Artificial Intelligence (AI) to financial investment is a research area that
has attracted extensive research attention since the 1990s, when there was an accelerated …
has attracted extensive research attention since the 1990s, when there was an accelerated …
Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process
The stock market has performed one of the most important functions in a laissez-faire
economic system by gathering people, companies, and flows of money for several centuries …
economic system by gathering people, companies, and flows of money for several centuries …
Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis
M Nabipour, P Nayyeri, H Jabani, S Shahab… - Ieee …, 2020 - ieeexplore.ieee.org
The nature of stock market movement has always been ambiguous for investors because of
various influential factors. This study aims to significantly reduce the risk of trend prediction …
various influential factors. This study aims to significantly reduce the risk of trend prediction …
Stock price prediction using deep learning and frequency decomposition
H Rezaei, H Faaljou, G Mansourfar - Expert Systems with Applications, 2021 - Elsevier
Nonlinearity and high volatility of financial time series have made it difficult to predict stock
price. However, thanks to recent developments in deep learning and methods such as long …
price. However, thanks to recent developments in deep learning and methods such as long …
An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges
Forecasting the behavior of the stock market is a classic but difficult topic, one that has
attracted the interest of both economists and computer scientists. Over the course of the last …
attracted the interest of both economists and computer scientists. Over the course of the last …
Prediction of stock market index based on ISSA-BP neural network
X Liu, J Guo, H Wang, F Zhang - Expert Systems with Applications, 2022 - Elsevier
Stock market index forecasting is a very tempting topic. Appropriate analysis of such a topic
will provide valuable insights for investors, traders and policymakers in the appealing stock …
will provide valuable insights for investors, traders and policymakers in the appealing stock …
Application of deep learning models for automated identification of Parkinson's disease: A review (2011–2021)
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting
over 6 million people globally. Although there are symptomatic treatments that can increase …
over 6 million people globally. Although there are symptomatic treatments that can increase …
Long-term wind power forecasting using tree-based learning algorithms
The intermittent and uncertain nature of wind places a premium on accurate wind power
forecasting for the reliable and efficient operation of power grids with large-scale wind power …
forecasting for the reliable and efficient operation of power grids with large-scale wind power …
Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms
Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for
implementing most mathematical models of petroleum engineering geomechanics …
implementing most mathematical models of petroleum engineering geomechanics …