Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks

By Leonel A. Laboissiere, R. Fernandes, G. G. Lage. 2015

134 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, 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 Electrical and Electronic Engineering

1. Charu C. Aggarwal
2. Liheng Zhang
3. Xuanzhe Liu
4. Junseok Kim
5. Jian Wang
6. Guo-Jun Qi
7. Tyson R. Browning
8. Martin Palecek

In Management Science and Operations Research

1. Jiang Bian
2. Charu C. Aggarwal
3. Liheng Zhang
4. Tie-Yan Liu
5. Junseok Kim
6. Jian Wang
7. Guo-Jun Qi
8. Weiwei Jiang

Citations in the corpus

• Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “With the developement of deep learning, more research efforts have been paid on exploiting deep neurual networks for financial prediction [2, 8, 12, 14, 21, 25].”
• Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, background): “With the advance of deep learning, it has become promising to exploit and explore deep neural networks [15, 16, 20] for financial prediction.”
• Predicting Stock Price Trend Using MACD Optimized by Historical Volatility (2018, methodology): “[10] developed a model involving correlation analysis and artificial neural networks (NNs) to predict the stock prices of Brazilian electric companies.”