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

DOI

10.2991/iemss-17.2017.144How to use a DOI?

Keywords

NASDAQ Stock Market, Standard & Poor's (S&P) Stock market, support vector machine, Novel advanced- fuzzy support vector machine (NA- FSVM).

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. Introduction

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuheng Wang
AU  - Guohao Li
AU  - Yifan Bao
PY  - 2017/04
DA  - 2017/04
TI  - A novel improved fuzzy support vector machine based stock price trend forecast model
BT  - Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)
PB  - Atlantis Press
SP  - 730
EP  - 740
SN  - 2352-5428
UR  - https://doi.org/10.2991/iemss-17.2017.144
DO  - 10.2991/iemss-17.2017.144
ID  - Wang2017/04
ER  -