Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

By M. Göçken, Mehmet Özçalici, A. Boru, A. T. Dosdoğru. 2016

300 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. Humaira Arshad
2. Charu C. Aggarwal
3. Aman Bin Jantan
4. Liheng Zhang
5. Xuanzhe Liu
6. Guo-Jun Qi
7. Oludare Isaac Abiodun
8. Abiodun Esther Omolara

In Management Science and Operations Research

1. Humaira Arshad
2. Jiang Bian
3. Charu C. Aggarwal
4. Aman Bin Jantan
5. Liheng Zhang
6. Tie-Yan Liu
7. Guo-Jun Qi
8. Oludare Isaac Abiodun

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, methodology): “A Harmony Search based neural network [8] is proposed to forecast the stock market; [2] compared capability of neural networks with Autoregressive Integrated Moving Average (ARIMA) to predict stock trends; a Bayesian regularized Artificial Neural Networks [27] was presented to forecast stock…”
• Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, methodology): “[154] with the application of NN to price pattern of the stock market.”