Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications
G Sonkavde, DS Dharrao, AM Bongale… - International Journal of …, 2023 - mdpi.com
The financial sector has greatly impacted the monetary well-being of consumers, traders,
and financial institutions. In the current era, artificial intelligence is redefining the limits of the …
and financial institutions. In the current era, artificial intelligence is redefining the limits of the …
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 …
A novel graph convolutional feature based convolutional neural network for stock trend prediction
W Chen, M Jiang, WG Zhang, Z Chen - Information Sciences, 2021 - Elsevier
Stock trend prediction is one of the most widely investigated and challenging problems for
investors and researchers. Since the convolutional neural network (CNN) was introduced to …
investors and researchers. Since the convolutional neural network (CNN) was introduced to …
Machine learning approaches in stock price prediction: a systematic review
P Soni, Y Tewari, D Krishnan - Journal of Physics: Conference …, 2022 - iopscience.iop.org
Prediction of stock prices is one of the most researched topics and gathers interest from
academia and the industry alike. With the emergence of Artificial Intelligence, various …
academia and the industry alike. With the emergence of Artificial Intelligence, various …
A multi parameter forecasting for stock time series data using LSTM and deep learning model
Financial data are a type of historical time series data that provide a large amount of
information that is frequently employed in data analysis tasks. The question of how to …
information that is frequently employed in data analysis tasks. The question of how to …
Constructing a stock-price forecast CNN model with gold and crude oil indicators
In this study, we propose algorithms to predict future stock market trends based on 8 different
input features, including financial technology indicators, gold prices, a gold price volatility …
input features, including financial technology indicators, gold prices, a gold price volatility …
Learning-based stock trending prediction by incorporating technical indicators and social media sentiment
Stock trending prediction is a challenging task due to its dynamic and nonlinear
characteristics. With the development of social platform and artificial intelligence (AI) …
characteristics. With the development of social platform and artificial intelligence (AI) …
Stock Price Forecast Based on CNN‐BiLSTM‐ECA Model
Financial data as a kind of multimedia data contains rich information, which has been widely
used for data analysis task. However, how to predict the stock price is still a hot research …
used for data analysis task. However, how to predict the stock price is still a hot research …
Short-term stock trends prediction based on sentiment analysis and machine learning
Y Qiu, Z Song, Z Chen - Soft Computing, 2022 - Springer
Investor-generated textual contents have been proved to be the crucial factor that can cause
fluctuations in stock price. However, the existing researches only used the equal-weighted …
fluctuations in stock price. However, the existing researches only used the equal-weighted …
VGC-GAN: A multi-graph convolution adversarial network for stock price prediction
D Ma, D Yuan, M Huang, L Dong - Expert Systems with Applications, 2024 - Elsevier
Not only market signals but also disturbances of related companies influence the stock
volatility of a company. Currently, most approaches that utilize inter-stock correlations rely on …
volatility of a company. Currently, most approaches that utilize inter-stock correlations rely on …