Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/4059
Title: STOCK PRICE PREDICTION USING SUPPORT VECTOR REGRESSION
Authors: Abirami, R
Vijaya, M S
Keywords: Linear regression
Machine learning
Stock price prediction
Support vector regression
Issue Date: 2012
Publisher: Springer Link
Abstract: Forecasting stock price is an important task as well as difficult problem. Stock price prediction depends on various factors and their complex relationships. Prediction of stock price is an important issue in finance. Stock price prediction is the act of trying to determine the future value of a company stock. The successful prediction of a stock future price could yield significant profit. Hence an efficient automated prediction system is highly essential for stock forecasting. This paper demonstrates the applicability of support vector regression, a machine learning technique, for predicting the stock price by learning the historic data. The stock data for the period of four years is collected and trained with various parameter settings. The performance of the trained model is evaluated by 10-fold cross validation for its predictive accuracy. It has been observed that the support vector regression model with RBF kernel shows better performance when compared with other models.
URI: https://link.springer.com/chapter/10.1007/978-3-642-29219-4_67
Appears in Collections:3.Conference Paper (06)

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