  This essay investigates the question of how the naive Bayes classifier and
the support vector machine compare in their ability to forecast the Stock
Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier
is explored. The algorithms are trained using data from the month of January
2010, extracted from the MarketWatch.com website. Input features are selected
based on previous studies of the SET100 Index. The Weka 3 software is used to
create models from the labeled training data. Mean squared error and proportion
of correctly classified instances, and a number of other error measurements are
the used to compare the two algorithms. This essay shows that these two
algorithms are currently not advanced enough to accurately model the stock
exchange. Nevertheless, the naive Bayes is better than the support vector
machine at predicting the Stock Exchange of Thailand.
