Predict stock price trends in Stock Exchange of Thailand using Ensemble Model
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Abstract
- The investment in the stock market has been interesting for domestic and foreign investors. There are many analysts attempting to improve forecasting models to increase prediction accuracy and stock return. Mostly focus on the single classifier using stock prices data as the main factor. However, the accuracy and stock return of the prediction were low. Thus, the purpose of this study attempt to increase prediction accuracy and stock return. Four factors were considered: stock prices data, indicator data, holding days and indicator days (the days that used to calculate indicators). In addition, 4 techniques of machine learning and ensemble model were used to forecast the trends of stock price by using SET’s information from January 2011 to December 2016. The results show that ensemble model in term of weight form can increase the prediction accuracy by 5% - 14% and increase the stock return by 1% - 3%. Moreover, the factors of indicators data and holding days are important factors to improve efficiency of prediction and stock return. KEY WORDS
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