Support Vector Regression for prediction of stock trend

Abstract

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. In this paper, we present a theoretical and empirical framework to apply the Support Vector Regression (SVR) strategy to predict the stock market. Our results suggest that SVR is a powerful predictive tool for stock predictions in the financial market.

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