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

TL;DR

This paper introduces an improved fuzzy support vector machine model designed to enhance stock price trend forecasting accuracy by effectively handling fuzzy and noisy data in financial markets.

Contribution

The paper proposes a novel advanced fuzzy support vector machine (NA-FSVM) that improves prediction precision over traditional models in stock market forecasting.

Findings

Enhanced prediction accuracy on NASDAQ and S&P data

Better handling of fuzzy and noisy information in stock data

Outperforms traditional support vector machine models

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.