Intakosum, Sarun

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Intakosum, Sarun
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sarun.in@kmitl.ac.th

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    Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend
    (2016-01-01)
    Inthachot, Montri
    ;
    Boonjing, Veera
    ;
    Intakosum, Sarun
    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.
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    Predicting SET50 index trend using artificial neural network and support vector machine
    (2015-01-01)
    Inthachot, Montri
    ;
    Boonjing, Veera
    ;
    Intakosum, Sarun
    Finding ways to making better prediction of stock market trend has attracted a lot of attention from researchers because an accurate prediction can substantially reduce investment risk and increase profit gain for investors. This study investigated the use of two machine learning methods, Artificial Neural Network (ANN) and Support Vector Machine (SVM), for predicting the trend of Thailand’s emerging stock market, SET50 index. Raw SET50 index records from 2009 to 2013 were converted into 10 widely-accepted technical indicators that were then used as input for model construction and testing. Our test results showed that the accuracy of the ANN model outperforms that of the SVM model.