Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network

By Yudong Zhang, Lenan Wu. 2009

441 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems 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 Artificial Intelligence

1. Ionel Nistor
2. Eva Dezsi
3. Michal Tkác
4. Sahil R. Shah
5. Priyank Thakkar
6. Robert Verner
7. Arash Ghanbari
8. Jigar Patel

In Control and Systems Engineering

1. Ionel Nistor
2. Eva Dezsi
3. Sahil R. Shah
4. Priyank Thakkar
5. Arash Ghanbari
6. Jigar Patel
7. Sara Ahmed
8. Alaa F. Sheta

In Management Science and Operations Research

1. Michal Tkác
2. Robert Verner
3. Saman Haratizadeh
4. Ehsan Hoseinzade
5. Sara Ahmed
6. Alaa F. Sheta
7. Hossam Faris
8. Ionel Nistor

Citations in the corpus

• A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, methodology): “It is important to develop such a relationships between variables in many cases such as predicting stock market [13], [14], [30], [31].”
• Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, unknown): “…to predict stock market returns (Baba & Kozaki, 1992; Chu, Chen, Cheng, & Huang, 2009; Hiemstra, 1995; Kim & Chun, 1998; Leigh, Purvis, & Ragusa, 2002; Oh & Kim, 2002; Pai & Lin, 2005; Saad, Prokhorov, & Wunsch, 1998; Takahashi, Tamada, & Nagasaka, 1998; Tan et al., 2007; Yudong & Lenan, 2009).”
• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[239] Stock market prediction Improved bacterial chemo taxis optimization and BPNN BPNN Hybrid models performs better”
• Predicting stock market index using fusion of machine learning techniques (2015, methodology): “Zhang and Wu (2009) incorporated the backpropagation neural network with an Improved Bacterial Chemotaxis Optimization (IBCO).”
• An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange (2010, background): “…, & Ragusa, 2002; Manish & Thenmozhi, 2006; Oh & Kim, 2002; Olson & Mossman, 2003; Pai & Lin, 2005; Saad, Prokhorov, & Wunsch, 1998; Takahashi, Tamada, & Nagasaka, 1998; Tan, Quek, & See Ng, 2007; Tay & Cao, 2001; Tay & Cao, 2002; Yao, Chew, & Poh, 1999; Yoon & Swales, 1991; Yudong & Lenan, 2009).”
• Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, methodology): “Yudong and Lenan [42] proposed an improved bacterial chemotaxis optimization (IBCO) which was integrated into BPN to develop an efficient forecasting model for prediction of various stock indices.”
• Artificial neural networks in business: Two decades of research (2016, background): “Several approaches have also been suggested to improve the performance of neural networks in stock markets prediction, including normalization of data, use of hyperbolic tangent instead of logistic activation function, addition of hidden neurons and hidden layers, or integration with metaheuristic m”