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 …
Bitcoin price forecasting: A perspective of underlying blockchain transactions
Cryptocurrency price forecasting plays an important role in financial markets. Traditional
approaches face two challenges:(1) it is difficult to ascertain the influential factors related to …
approaches face two challenges:(1) it is difficult to ascertain the influential factors related to …
[PDF][PDF] A comparison between regression, artificial neural networks and support vector machines for predicting stock market index
Obtaining accurate prediction of stock index sig-nificantly helps decision maker to take
correct actions to develop a better economy. The inability to predict fluctuation of the stock …
correct actions to develop a better economy. The inability to predict fluctuation of the stock …
A new hybrid financial time series prediction model
B Alhnaity, M Abbod - Engineering Applications of Artificial Intelligence, 2020 - Elsevier
Due to the characteristics of financial time series, such as being non-linear, non-stationary
and noisy, with uncertain and hidden relationships, it is difficult to capture its non-stationary …
and noisy, with uncertain and hidden relationships, it is difficult to capture its non-stationary …
Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks
LA Laboissiere, RAS Fernandes, GG Lage - Applied Soft Computing, 2015 - Elsevier
Time series forecasting has been widely used to determine future prices of stocks, and the
analysis and modeling of finance time series is an important task for guiding investors' …
analysis and modeling of finance time series is an important task for guiding investors' …
Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles
Q Wang, W Xu, H Zheng - Neurocomputing, 2018 - Elsevier
Many researchers and practitioners have attempted to predict financial market trends for
excess returns using multiple information sources including social media. Recent studies …
excess returns using multiple information sources including social media. Recent studies …
A causal feature selection algorithm for stock prediction modeling
A key issue of quantitative investment (QI) product design is how to select representative
features for stock prediction. However, existing stock prediction models adopt feature …
features for stock prediction. However, existing stock prediction models adopt feature …
Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series
BQ Sun, H Guo, HR Karimi, Y Ge, S Xiong - Neurocomputing, 2015 - Elsevier
This paper makes a prediction of Chinese stock index (CSI) future prices using fuzzy sets
and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the …
and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the …
Machine learning for financial prediction under regime change using technical analysis: A systematic review
AL Suárez-Cetrulo, D Quintana, A Cervantes - 2023 - reunir.unir.net
Recent crises, recessions and bubbles have stressed the non-stationary nature and the
presence of drastic structural changes in the financial domain. The most recent literature …
presence of drastic structural changes in the financial domain. The most recent literature …
Stock2Vec: a hybrid deep learning framework for stock market prediction with representation learning and temporal convolutional network
We have proposed to develop a global hybrid deep learning framework to predict the daily
prices in the stock market. With representation learning, we derived an embedding called …
prices in the stock market. With representation learning, we derived an embedding called …