Title: A novel improved fuzzy support vector machine based stock price trend forecast model,
Abstract: Application of fuzzy support vector machine in stock price forecast. Support
vector machine is a new type of machine learning method proposed in 1990s. It
can deal with classification and regression problems very successfully. Due to
the excellent learning performance of support vector machine, the technology
has become a hot research topic in the field of machine learning, and it has
been successfully applied in many fields. However, as a new technology, there
are many limitations to support vector machines. There is a large amount of
fuzzy information in the objective world. If the training of support vector
machine contains noise and fuzzy information, the performance of the support
vector machine will become very weak and powerless. As the complexity of many
factors influence the stock price prediction, the prediction results of
traditional support vector machine cannot meet people with precision, this
study improved the traditional support vector machine fuzzy prediction
algorithm is proposed to improve the new model precision. NASDAQ Stock Market,
Standard & Poor's (S&P) Stock market are considered. Novel advanced- fuzzy
support vector machine (NA-FSVM) is the proposed methodology.