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This research presents the data-driven modeling method to derive a combined trading model from the analysis of negative correlations among the top-five active stocks from each sector of the Thailand stock market. The negative movements are computed from the closing price direction of major stocks in the eight biggest sectors. The highly negative correlated stocks among market groups are then used to build predictive trading models with three algorithms: regression analysis, generalized linear modeling, and chi-square automatic interaction detection. An ensemble from the combination of the best two models is then created. The experimental results reveal that the proposed method of trading based on negative movement analysis can accurately predict closing price of the active stock with low error rate around 1.86%.

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