[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 …
Literature review: Machine learning techniques applied to financial market prediction
BM Henrique, VA Sobreiro, H Kimura - Expert Systems with Applications, 2019 - Elsevier
The search for models to predict the prices of financial markets is still a highly researched
topic, despite major related challenges. The prices of financial assets are non-linear …
topic, despite major related challenges. The prices of financial assets are non-linear …
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 …
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies
We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …
and prediction. Its ability to extract features from a large set of raw data without relying on …
ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module
Forecasting a financial asset's price is important as one can lower the risk of investment
decision-making with accurate forecasts. Recently, the deep neural network is popularly …
decision-making with accurate forecasts. Recently, the deep neural network is popularly …
Forecasting stock market crisis events using deep and statistical machine learning techniques
SP Chatzis, V Siakoulis, A Petropoulos… - Expert systems with …, 2018 - Elsevier
This work contributes to this ongoing debate on the nature and the characteristics of
propagation channels of crash events in international stock markets. Specifically, we …
propagation channels of crash events in international stock markets. Specifically, we …
Forecasting daily stock market return using dimensionality reduction
In financial markets, it is both important and challenging to forecast the daily direction of the
stock market return. Among the few studies that focus on predicting daily stock market …
stock market return. Among the few studies that focus on predicting daily stock market …
Predicting the daily return direction of the stock market using hybrid machine learning algorithms
Big data analytic techniques associated with machine learning algorithms are playing an
increasingly important role in various application fields, including stock market investment …
increasingly important role in various application fields, including stock market investment …
A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction
Y Chen, Y Hao - Expert Systems with Applications, 2017 - Elsevier
This study investigates stock market indices prediction that is an interesting and important
research in the areas of investment and applications, as it can get more profits and returns at …
research in the areas of investment and applications, as it can get more profits and returns at …
Deeplob: Deep convolutional neural networks for limit order books
We develop a large-scale deep learning model to predict price movements from limit order
book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the …
book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the …