[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …
stock market prediction. A focus area in this literature review is the stock markets …
A systematic review of fundamental and technical analysis of stock market predictions
The stock market is a key pivot in every growing and thriving economy, and every investment
in the market is aimed at maximising profit and minimising associated risk. As a result …
in the market is aimed at maximising profit and minimising associated risk. As a result …
Global stock market investment strategies based on financial network indicators using machine learning techniques
This study presents financial network indicators that can be applied to global stock market
investment strategies. We propose to design both undirected and directed volatility networks …
investment strategies. We propose to design both undirected and directed volatility networks …
Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review
Despite the wide application of evolutionary computation (EC) techniques to rule discovery
in stock algorithmic trading (AT), a comprehensive literature review on this topic is …
in stock algorithmic trading (AT), a comprehensive literature review on this topic is …
Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market
X Yuan, J Yuan, T Jiang, QU Ain - IEEE Access, 2020 - ieeexplore.ieee.org
The classical linear multi-factor stock selection model is widely used for long-term stock
price trend prediction. However, the stock market is chaotic, complex, and dynamic, for …
price trend prediction. However, the stock market is chaotic, complex, and dynamic, for …
Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory
With artificial intelligence and data quality development, portfolio optimization has improved
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …
rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio …
Multi-objective evolutionary feature selection for online sales forecasting
Sales forecasting uses historical sales figures, in association with products characteristics
and peculiarities, to predict short-term or long-term future performance in a business, and it …
and peculiarities, to predict short-term or long-term future performance in a business, and it …
Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural …
Q Chen, W Zhang, Y Lou - IEEE Access, 2020 - ieeexplore.ieee.org
Stock prices forecasting is a topic research in the fields of investment and national policy,
which has been a challenging problem owing to the multi-noise, nonlinearity, high …
which has been a challenging problem owing to the multi-noise, nonlinearity, high …
Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction
The issue of harnessing machine learning (ML) algorithms for the prediction of adverse
medical events is important considering the prevalence of vast repositories of patient-level …
medical events is important considering the prevalence of vast repositories of patient-level …
Evaluating the performance of ensemble classifiers in stock returns prediction using effective features
MR Toochaei, F Moeini - Expert Systems with Applications, 2023 - Elsevier
Stock market prediction is considered as an important yet challenging aspect of financial
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …