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 …
Predicting stock market using machine learning: best and accurate way to know future stock prices
D Sheth, M Shah - International Journal of System Assurance Engineering …, 2023 - Springer
Dissatisfaction is the first step of progress, this statement serves to be the base of using
Artifcial Intelligence in predicting stock prices. A great deal of people dreamed of predicting …
Artifcial Intelligence in predicting stock prices. A great deal of people dreamed of predicting …
Deep reinforcement learning for automated stock trading: An ensemble strategy
Stock trading strategies play a critical role in investment. However, it is challenging to design
a profitable strategy in a complex and dynamic stock market. In this paper, we propose an …
a profitable strategy in a complex and dynamic stock market. In this paper, we propose an …
A novel ensemble deep learning model for stock prediction based on stock prices and news
Y Li, Y Pan - International Journal of Data Science and Analytics, 2022 - Springer
In recent years, machine learning and deep learning have become popular methods for
financial data analysis, including financial textual data, numerical data, and graphical data …
financial data analysis, including financial textual data, numerical data, and graphical data …
FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning
Finance is a particularly challenging playground for deep reinforcement learning. However,
establishing high-quality market environments and benchmarks for financial reinforcement …
establishing high-quality market environments and benchmarks for financial reinforcement …
Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models
This research paper investigates the efficacy of various machine learning models, including
deep learning and hybrid models, for text classification in the English and Bangla …
deep learning and hybrid models, for text classification in the English and Bangla …
Privacy-preserving healthcare monitoring for IoT devices under edge computing
W Cao, W Shen, Z Zhang, J Qin - Computers & Security, 2023 - Elsevier
With the rapid development of the Internet of Things (IoT) technology, e-healthcare has
received extensive attention because it is able to provide real-time health status feedback for …
received extensive attention because it is able to provide real-time health status feedback for …
Hybrid information mixing module for stock movement prediction
J Choi, S Yoo, X Zhou, Y Kim - IEEE Access, 2023 - ieeexplore.ieee.org
With the continuing active research on deep learning, research on stock price prediction
using deep learning has been actively conducted in the financial industry. This paper …
using deep learning has been actively conducted in the financial industry. This paper …
Dynamic datasets and market environments for financial reinforcement learning
The financial market is a particularly challenging playground for deep reinforcement
learning due to its unique feature of dynamic datasets. Building high-quality market …
learning due to its unique feature of dynamic datasets. Building high-quality market …
Generation of realistic synthetic financial time-series
Financial markets have always been a point of interest for automated systems. Due to their
complex nature, financial algorithms and fintech frameworks require vast amounts of data to …
complex nature, financial algorithms and fintech frameworks require vast amounts of data to …