Developing an approach to evaluate stocks by forecasting effective features with data mining methods
By Sasan Barak, Mohammad Modarres. 2015
96 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Yusuf Yaslan
- Zehra Cataltepe
- Hakan Gunduz
- S. Sadegh
- Sasan Barak
- Weiwei Jiang
- Omer Berat Sezer
- A. Ozbayoglu
In Management Science and Operations Research
- Yusuf Yaslan
- Zehra Cataltepe
- Hakan Gunduz
- S. Sadegh
- Sasan Barak
- Weiwei Jiang
- M. Ugur Gudelek
- Omer Berat Sezer
Citations in the corpus
- Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm (2016, background): “According to the [60], the dense structure of models bring about a low level of over fitting and cause a robust prediction.”
- Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations (2017, methodology): “The main advantages of the CNN-Corr classifier introduced in this work with respect to the feature based models in the previous studies [58, 59, 60] can be summarized as follows:”
- Restricted Boltzmann machine based stock market trend prediction (2017, background): “But there are also some articles further dividing trend prediction to three or five types [8], [28].”