A Morphological Based Prediction of News Stock Market and Money Using Genetic Algorithm
Keywords:
Genetic Algorithm, Multilayer Perceptron, Back Propagation, Stock market expectation & Expectation accuracyAbstract
Globalization has made the Stock Market Expectation (SME) precision more testing also, compensating for the scientists also, other participants in the stock market. Nearby also, global monetary situations along with the company’s monetary quality also, prospects have to be taken into account to progress the expectation accuracy. Genetic Algorithm (GA) has been identified to be one of the overwhelming data mining methods in stock market expectation area. In this paper, we survey distinctive GA models that have been tested in SME with the unique improvement methods utilized with them to progress the accuracy. Also, we explore the conceivable research procedures in this precision driven GA models.
References
Ortiz-Rodríguez José Manuel; Martínez-Blanco Ma. del Rosario; Gallego Eduardo; Vega-Carrillo Héctor Rene, “Artificial Neural Networks Modeling Evolved Genetically, a New Approach Applied in Neutron Spectrometry and Dosimetry Research Areas”, Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08, Year: 2008,Pages: 387 - 392
Xue-Bin Li; Xiao-Ling Yu, “Influence of Sample Size on Prediction of Animal Phenotype Value Using Back-Propagation Artificial Neural Network with Variable Hidden Neurons”, Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on,Year: 2009,Pages: 1 - 4,
Stephan H. Chagas; João B. Martins; Leonardo L. de Oliveira, “An approach to localization scheme of wireless sensor networks based onartificial neural networks and Genetic Algorithms”, New Circuits and Systems Conference (NEWCAS), 2012 IEEE 10th International, Year: 2012,Pages: 137 - 140
Yuhong Li; Weihua Ma, “Applications of Artificial Neural Networks in Financial Economics: A Survey”, Computational Intelligence and Design (ISCID), 2010 International Symposium on, Year: 2010, Volume: 1,Pages: 211 - 214
Veselin L. Shahpazov; Vladimir B. Velev; Lyubka A. Doukovska, “Design and application of Artificial Neural Networks for predicting the values of indexes on the Bulgarian Stock market”, Signal Processing Symposium (SPS), 2013,Year: 2013, Pages: 1 - 6,
hanghao Piao; Xiaoyong Yang; Cong Teng; HuiQian Yang, “An improved model based on artificial neural networks and Thevenin model for nickel metal hydride power battery”, 2010 International Conference on Optics, Photonics and Energy Engineering (OPEE),Year: 2010, Volume: 1, Pages: 115 – 118
Christine F. Boos; Fernando M. Azevedo, “Resizing artificial neural networks for automatic detection of epileptiform discharges: A comparison between Principal Component and Linear Discriminant Analysis”, 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO),Year: 2015,Pages: 1 – 5.
S. N. Danilin; V. V. Makarov; S. A. Shchanikov, “Design of Artificial Neural Networks with a Specified Quality of Functioning”, Engineering and Telecommunication (EnT), 2014 International Conference on, Year: 2014,Pages: 67 - 71
Khaled Shaban; Ayman El-Hag; Andrei Matveev, “A cascade of artificial neural networks to predict transformers oil parameters”, IEEE Transactions on Dielectrics and Electrical Insulation, Year: 2009, Volume: 16, Issue: 2,Pages: 516 – 523.
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