Predicting SET50 stock prices using CARIMA (Cross Correlation ARIMA)
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Abstract
Investing in stocks is one of the most popular approaches for money investment. This paper aims to predict short-term stock prices of SET50 of Stock Exchange of Thailand (SET). The proposed method is called CARIMA (Cross Correlation Autoregressive Integrated Moving Average. The basic idea of CARIMA is to find the most highly correlated s tock t o predict the target one in addition to ARIMA predicted price. The results of CARIMA model yield better price trends (measured by 10-day correlation coefficient) while % MAEs (Mean Absolute Errors) are quite similar with those of ARIMA.
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ARIMA, Correlation, Prediction, Stock, Time Series
Citation
Icsec 2015 19th International Computer Science and Engineering Conference Hybrid Cloud Computing A New Approach for Big Data Era, 2016