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Application of fuzzy support vector machine in stock price forecast. Supportvector machine is a new type of machine learning method proposed in 1990s. Itcan deal with classification and regression problems very successfully. Due tothe excellent learning performance of support vector machine, the technologyhas become a hot research topic in the field of machine learning, and it hasbeen successfully applied in many fields. However, as a new technology, thereare many limitations to support vector machines. There is a large amount offuzzy information in the objective world. If the training of support vectormachine contains noise and fuzzy information, the performance of the supportvector machine will become very weak and powerless. As the complexity of manyfactors influence the stock price prediction, the prediction results oftraditional support vector machine cannot meet people with precision, thisstudy improved the traditional support vector machine fuzzy predictionalgorithm is proposed to improve the new model precision. NASDAQ Stock Market,Standard & Poor's (S&P) Stock market are considered. Novel advanced- fuzzysupport vector machine (NA-FSVM) is the proposed methodology.

A novel improved fuzzy support vector machine based stock price trend forecast model