The ability to make predictions of stock market trends is highly desirable for traders as well as those who study the market as a career. This research has devised a method using a data mining based stock market trend predictions system. It uses a genetic algorithm optimized decision tree-support vector machine (SVM) hybrid designed to predict one-day-ahead trends. Rather than approaching trend prediction in the traditional sense as a regression problem, a common approach in previous studies, the research uses a hybrid system able to adapt to the changing market conditions looking at it as a classification problem.
This study uses the historical time series data from the Bombay stock exchange sensitive index (BSE-Sensex) from January 2 2007 to October 30, 2010. A comparison is done to an artificial neutral network (ANN) based system and a naïve Bayes based system. The results show that the trend prediction accuracy is highest for the hybrid system and the genetic algorithm optimized decision tree SVM hybrid system outperforms the artificial neutral network and the naïve Bayes based trend prediction system.
The intention of the system, based on four steps, is to allow an individual an accurate insight into whether they should buy, sell, or hold their stocks. The first step is the computation of technical indices from the historical stock market data. Second, the technical indices are selected using a decision tree, these are then used by a support vector classifier to predict the next day’s trends. The final steps include the GA based optimization of the decision tree and support vector classifier parameters to ensure the most accurate prediction. The decision tree is deemed one of the most important aspects of the hybrid as it plays a significant role in the prediction, the overall intent of the study.
Large unforeseen events have the potential to significantly influence the market one way or another that cannot be predicted or accounted for. Therefore, this method is useful during times of relative world stability but when a large unforeseen crisis takes place, it is likely to exhibit inaccuracy. Furthermore, by applying the method to more strenuous tests with other data sets in different markets, the efficiency and usefulness of the system would have increased. Although it out performed the ANN and naïve Bayes in this particular set of data, further tests in other data sets would strengthen its validity and usefulness.
Additionally, the passing reference of stock market speculation as a “regression problem” and the chosen stance as a “classification problem” should be elaborated upon for comparison. The expansion of the issues associated with the “regression problem” approach using previous studies or basic examples would increase the validity of the “classification problem” approach. A brief comparison would have been especially useful for individuals new to the field, further clarifying the benefits of the new approach.
Furthermore, due to the hybrid nature of the technique, an increased differentiation between each method is necessary before combining the methods to achieve the hybrid. A more significant breakdown of each technique would have allowed for a more precise understanding of the overall hybrid. Due to the decision tree's significance in the study, elaboration would have proved especially useful in this area of the methodology.
Nair, B. B., Mohandas, V. P., & Sakthivel, N. R. (2010). A Genetic Algorithm Optimized Decision Tree-SVM basaed Stock Market Trend Prediction System. International Journal On Computer Science & Engineering, 2981-2988