This Is AuburnElectronic Theses and Dissertations

Application of machine learning techniques for stock market prediction




Weng, Bin

Type of Degree

PhD Dissertation


Industrial and Systems Engineering


The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. Since the stock market is very sensitive to the external information, the performance of previous prediction systems is limited by merely considering the traditional stock data. New forms of collective intelligence have emerged with the rise of the Internet (e.g. Google Trends, Wikipedia, etc.). The changes on these platforms will significantly affect the stock market. In addition, both the financial news sentiment and volumes are believed to have an impact on the stock price. In this study, disparate data sources are used to generate a prediction model along with a comparison of different machine learning methods. Besides historical data directly from the stock market, numbers of external data sources are also considered as inputs to the model. The goal of this study is to develop and evaluate a decision-making system that could be used to predict stocks’ short term movement, trend, and price. We took advantage of the open source API and public economic database which allow us to explore the hidden information among these platforms. The prediction models are compared and evaluated using machine learning techniques, such as neural network, support vector regression and boosted tree. Numbers of case studies are performed to evaluate the performance of the prediction system. From the case studies, several results were obtained: (1) the use of external data sources along with traditional metrics leads to improve the prediction performance; (2) the prediction models benefit from the feature selection and dimensional reduction techniques. (3) The prediction performance dominates the related works. Finally, a decision support system is provided to assist investors in making trading decision on any stocks.