Author(s)

Jingxuan Jiang

Corresponding Author

Jingxuan Jiang

ABSTRACT

Prediction of a stock price is an extremely challenging and complex task because of the dynamic economic environment. However, in this era of data, the availability of data and techniques makes it easier to do the prediction. As data mining techniques have been acquainted with and applied for financial prediction, in this paper, outlier detection and clustering which are two primary steps of data mining will be introduced to show how to process the data. Moreover, machine learning is ideal for stock prediction by analyzing historical data and giving the predicted result. This paper also introduces the detailed principles and technologies of the Support Vector Machine (SVM), Long Short-term Memory (LSTM), and Recurrent Neural Networks (RNN).

KEYWORDS

Stock price prediction, big data, machine learning, Support Vector Machine (SVM), Long Short-term Memory (LSTM), Recurrent Neural Networks (RNN)