Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

By Esmaeil Hadavandi, Hassan Shavandi, Arash Ghanbari. Knowledge-Based Systems, 2010

376 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/aed760ddd238956c69901d53a79ece79eee7bde8

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. Michal Tkác
2. Zhongyi Hu
3. Tao Xiong
4. Pooyan Nayyeri
5. Hamed Jabani
6. Robert Verner
7. Yukun Bao
8. A. Ozbayoglu

In Management Science and Operations Research

1. Mustafa Ucar
2. Ilknur Ucar
3. Tzu-Yu Lin
4. Michal Tkác
5. Zhongyi Hu
6. Tao Xiong
7. Pooyan Nayyeri
8. Hamed Jabani

Citations in the corpus

• Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, methodology): “According to an extensive literature investigation, it is not difficult to find a wide variety of methodologies and techniques that have been used for stock price forecasting with various degrees of success, such as the Box–Jenkins method [1], general autoregressive conditional heteroskedasticity [2”
• Restricted Boltzmann machine based stock market trend prediction (2017, background): “[20], decision trees [21], fuzzy set [22], [23], [24], hybrid models [25] and some deep learning models.”
• Artificial neural networks in business: Two decades of research (2016, methodology): “The integration of neural networks with secondary artificial intelligence and metaheuristic methods such as fuzzy logic [102, 139, 273, 368], genetic algorithms [119, 132, 234, 311], bee colony algorithm [153], or artificial immune systems [191] have therefore been proposed in order to eliminate thi”
• A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example (2012, background): “In the meantime, the correlation between the population size and ll rights reserved. shan District, Taipei City 104, Furthermore, this article refer to Ravisankar [17], Li [15], Cho [5] and Hadavandi [10] adopts the financial distress data of enterprises with stocks listed in the regular stock marke”
• Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms (2015, background): “Most of these studies were concentrated on stock market forecasting [1], [2], trend detection [3], and strategy development based on technical indicators.”
• Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting (2012, background): “Financial time series forecasting has come to play an important role in the world economy as a result of its ability to forecast economic benefits and influence countries' economic development; it has attracted increasing attention from academic researchers and business practitioners for its theoret”
• Forecasting Share Prices of Small Size Companies in Bursa Malaysia Using Geometric Brownian Motion (2014, background): “Keywords: Forecasting, geometric Brownian motion, investment, stock market”
• Deep Learning for Stock Market Prediction (2020, background): “on linear models for predicting future prices [17, 18]. Several research activities have done for extensive predictions with Artificial Neural Networks (ANN), Genetic Algorithms (GA), fuzzy logic etc [19, 20, 21]. Zhang et al. [22] combined Improved Bacterial Chemotaxis Optimization (IBCO) with arti”